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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yi7xzykjgGP9"
+      },
+      "source": [
+        "## Lab 1: Getting data from API's\n",
+        "\n",
+        "A great source of data and Pandas practice is getting data from the Internet. It is not going to come in a .csv file, though: It will be a stream of records, typically in XML (eXtensible Mark-up Language) or JSON (JavaScript Object Notation) format.\n",
+        "\n",
+        "We'll look at a very simple API and some useful code chunks for getting and analyzing data, and then you'll take a look at the APIs available from the Federal government as the main work for your lab.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ko09F0g3gGP-"
+      },
+      "source": [
+        "## API Queries\n",
+        "\n",
+        "The core programming skill of the activity is to learn to query an online Application Programmer Interface (API). It is a similar experience to browsing the Internet, and initial results can be displayed the web browser. When visiting a web page, the address bar typically contains something like\n",
+        "$$\n",
+        "\\texttt{https://} \\underbrace{\\texttt{www.}}_{\\text{World Wide Web subdomain}} \\texttt{domain}. \\underbrace{\\texttt{tld}}_{\\text{Top level domain}},\n",
+        "$$\n",
+        "where the https:// specifies the protocol, www. specifies the world-wide-web subdomain, the domain is the entity, and the top-level domain .tld is typically something like .com or .gov, but is increasingly varied as ICANN releases more TLD's into circulation.\n",
+        "\n",
+        "With an online API, the user instead enters a url that goes directly to an API subdomain\n",
+        "$$\n",
+        "\\texttt{https://} \\underbrace{\\texttt{api.}}_{\\text{Application programmer interface}} \\texttt{domain.tld}/ \\texttt{(the query)}\n",
+        "$$\n",
+        "or accesses REST services as\n",
+        "$$\n",
+        "\\texttt{https://www.domain.tld} \\underbrace{\\texttt{/REST}}_{\\text{Accesses REST services}}/ \\texttt{(the query)}\n",
+        "$$\n",
+        "This accesses data on the domain's servers and returns the result directly to the user.\n",
+        "\n",
+        "The query itself is typically a string beginning with a question mark ?, followed by a series of expressions joined by ampersands &. For example,\n",
+        "\n",
+        "`?ProductType=Phone\\&Manufacturer=Apple`\n",
+        "\n",
+        "passes a query requesting all records for which the product type is recorded as phone and the manufacturer is recorded as Apple. Some API's include date ranges and other, more complex requests.\n",
+        "\n",
+        "To get started, a simple warm-up is to use the API from saferproducts.gov, which has a simple and intuitive structure for queries, and the results are simple enough to look at in the browser. Typing this in the address bar in a browser should yield about thirty records:\n",
+        "\n",
+        "    https://www.saferproducts.gov/RestWebServices/Recall?format=json&ProductType=Phone  \n",
+        "\n",
+        "with the first being, on this occasion:\n",
+        "\n",
+        "    \"RecallID\": 7856,\n",
+        "    \"RecallNumber\": \"16266\",\n",
+        "    \"RecallDate\": \"2016-09-15T00:00:00\",\n",
+        "    \"Description\": \"This recall involves the Samsung Galaxy Note7 smartphone sold before\n",
+        "    September 15, 2016. The recalled devices have a 5.7 inch screen and were sold in the\n",
+        "    following colors: black onyx, blue coral, gold platinum and silver titanium with a\n",
+        "    matching stylus. Samsung is printed on the top front of the phone and Galaxy Note7\n",
+        "    is printed on the back of the phone. To determine if your phone has been recalled,\n",
+        "    locate the IMEI number on the back of the phone or the packaging, and enter the IMEI\n",
+        "    number into the online registration site www.samsung.com or call Samsung toll-free\n",
+        "    at 844-365-6197.\",\n",
+        "    \"URL\": \"https://www.cpsc.gov/Recalls/2016/Samsung-Recalls-Galaxy-Note7-Smartphones\",\n",
+        "    \"Title\": \"Samsung Recalls Galaxy Note7 Smartphones Due to Serious Fire and Burn Hazards\",\n",
+        "    \"ConsumerContact\": \"Contact your wireless carrier or place of purchase, call Samsung\n",
+        "    toll-free at 844-365-6197 anytime, or go online at www.samsung.com.\",\n",
+        "    \"LastPublishDate\": \"2016-10-27T00:00:00\"\n",
+        "\n",
+        "The query itself in this case is:\n",
+        "\n",
+        "    ?format=json&ProductType=Phone  \n",
+        "\n",
+        "The quert requests all of the recalls in JavaScript Object Notation (json) format, where the `ProductType` variable is equal to `Phone`. In addition to `ProductType`, other options include:\n",
+        "\n",
+        "    RecallID,\n",
+        "    RecallNumber,\n",
+        "    RecallDateStart,\n",
+        "    RecallDateEnd,\n",
+        "    RecallURL,\n",
+        "    LastPublishDateStart,\n",
+        "    LastPublishDateEnd,\n",
+        "    RecallTitle,\n",
+        "    ConsumerContact,\n",
+        "    RecallDescription,\n",
+        "    ProductName,\n",
+        "    ProductDescription,\n",
+        "    ProductModel,\n",
+        "    ProductType,\n",
+        "    InconjunctionURL,\n",
+        "    ImageURL,\n",
+        "    Injury,\n",
+        "    Manufacturer,\n",
+        "    Retailer,\n",
+        "    Importer,\n",
+        "    Distributor,\n",
+        "    ManufacturerCountry,\n",
+        "    UPC,\n",
+        "    Hazard,\n",
+        "    Remedy,\n",
+        "    RemedyOption\n",
+        "\n",
+        "**1. Practice writing queries using the saferproducts.gov API and your web browser.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url = 'https://www.saferproducts.gov/RestWebServices/Recall'\n",
+        "#query = '?format=json&ProductType=Phone' #29\n",
+        "#query = '?format=json&ProductType=Grill' #70\n",
+        "#query = '?format=json&ProductType=Exercise' # 91\n",
+        "query = '?format=json&RecallTitle=Gas' # 216"
+      ],
+      "metadata": {
+        "id": "sKd_caFLEx23"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8Zwfi6ndgGP-"
+      },
+      "source": [
+        "## Accessing API's with Python\n",
+        "\n",
+        "Anytime you use a computer to access resources on the Internet, you will likely run into problems. There are many options, but two with low coding overhead: The `requests` and `urrlib.requests` packages.\n",
+        "\n",
+        "The following code chunk uses the `requests` package to get the same kind of data that was being displayed in the browser, but in an interactive Python session:\n",
+        "\n",
+        "    import requests\n",
+        "    url = 'https://www.saferproducts.gov/RestWebServices/' # Location of the API\n",
+        "    query = 'Recall?format=json&ProductType=Exercise' # The query\n",
+        "    header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:124.0)\n",
+        "        Gecko/20100101 Firefox/124.0'} # The user-agent to avoid being blocked\n",
+        "    raw = requests.get(url+query,headers=header) # Query the database\n",
+        "    data = raw.json() # Convert data from json to dictionary\n",
+        "\n",
+        "To make the code easier to read, it separates the url and the query into two different strings, then concatenates them in the GET request. This makes it easier to edit the query, as well as suggests a simple way to loop over a number of queries that might be sent to the same API.\n",
+        "\n",
+        "Many resources are designed to block access from particular kinds of users. In order to circumvent these obstacles, you can specify a `header` dictionary that presents the query to the server as coming from a hypothetical and common user. In this case, the header presents the query as coming from a Firefox browser from a Windows computer, rather than something like `python-requests/3.12.1`. This problem appears generally in scraping data from the web, and can grind the process to a halt. For whatever reason, I have been blocked and gotten 403 errors with the `requests` package, which motivated me to prepare a second alternative that seems more robust:\n",
+        "\n",
+        "    import urllib.request\n",
+        "    import json\n",
+        "    url = 'https://www.saferproducts.gov/RestWebServices/' # Location of the API\n",
+        "    query = 'Recall?format=json&ProductType=Exercise' # The query\n",
+        "    response = urllib.request.urlopen(url+query)\n",
+        "    response_bytes = response.read()\n",
+        "    data = json.loads(response_bytes) # Convert response to json\n",
+        "    response.close()\n",
+        "\n",
+        "This is a bit more code and some steps are a bit less human-friendly, but seems to work a bit more reliably than `requests`.\n",
+        "\n",
+        "**2. Practice with the saferproducts.gov API and the above code in a notebook to see how API's work, in general.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import urllib.request\n",
+        "import json\n",
+        "import pickle\n",
+        "response = urllib.request.urlopen(url+query)\n",
+        "response_bytes = response.read()\n",
+        "data = json.loads(response_bytes)\n",
+        "response.close()\n",
+        "\n",
+        "import pickle\n",
+        "with open('json_data.pkl', 'wb') as file:\n",
+        "    pickle.dump(data, file)"
+      ],
+      "metadata": {
+        "id": "Ndduu9I2E_KE"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWk9ceyRgGP_"
+      },
+      "source": [
+        "## Wrangling the Data\n",
+        "\n",
+        "Piping the data to Pandas is easy, because the preceding code chunk put the JSON data into a native Python dictionary, and the following converts it to a dataframe:\n",
+        "\n",
+        "    df = pd.DataFrame.from_dict(data)\n",
+        "\n",
+        "If the data is in raw XML or JSON format --- which might especially be true with other API's --- it would instead be \\texttt{pd.read\\_xml(data)} or \\texttt{pd.read\\_json(data)}. Ironing out these details in advance for other applications is a key part of the presentation for students, but having students resolve these issues as a component of group work or an assignment is a great way to help them mature as coding problem solvers by struggling with documentation and a well-defined problem.\n",
+        "\n",
+        "Unfortunately, there aren't many non-text fields in the \\texttt{www.saferproducts.gov} data. However, there are a few fields of interest that can be tabulated and discussed, such as RemedyOptions and ManufacturerCountries:\n",
+        "\n",
+        "    df['RemedyOptions'].value_counts()\n",
+        "\n",
+        "with output\n",
+        "\n",
+        "    RemedyOptions\n",
+        "    []                                                                               139\n",
+        "    [{'Option': 'Repair'}]                                                            49\n",
+        "    [{'Option': 'Replace'}]                                                           12\n",
+        "    [{'Option': 'Refund'}]                                                             7\n",
+        "    [{'Option': 'Replace'}, {'Option': 'Repair'}]                                      4\n",
+        "    [{'Option': 'Refund'}, {'Option': 'Replace'}, {'Option': 'Repair'}]                1\n",
+        "    [{'Option': 'Replace'}, {'Option': 'Refund'}]                                      1\n",
+        "    [{'Option': 'Refund'}, {'Option': 'Repair'}]                                       1\n",
+        "    [{'Option': 'Label'}]                                                              1\n",
+        "    [{'Option': 'New Instructions'}, {'Option': 'Replace'}, {'Option': 'Refund'}]      1\n",
+        "    Name: count, dtype: int64\n",
+        "\n",
+        "It's appropriate at this point to do some data cleaning, particularly by flattening dictionary entries. With response data that get converted from json to a dictionary, there are often values in the data frame that need to be flattened or unpacked. For example, some values are recorded as \\texttt{ [$\\{$'Country':'Canada'$\\}$]}, or, worse, a dictionary with multiple entries: \\texttt{[ $\\{$ 'Option': 'Replace'$\\}$, $\\{$'Option': 'Repair'$\\}$]  ] }. This can lead to problems when another package refuses to work with a lists of lists or doesn't know how to simplify a dictionary to data, and presents some conceptual questions when cleaning.\n",
+        "\n",
+        "A simple script to recursively collapse the dictionary entries into a single string is:\n",
+        "\n",
+        "    temp = df['RemedyOptions']\n",
+        "    clean_values = []\n",
+        "    for i in range(len(temp)):\n",
+        "        if len(temp[i])>0:\n",
+        "            values = []\n",
+        "            for j in range(len(temp[i])):\n",
+        "                values.append(temp[i][j]['Option'] )\n",
+        "            clean_values.append(values)\n",
+        "        else:\n",
+        "            clean_values.append('')\n",
+        "    df['remedy'] = clean_values\n",
+        "\n",
+        "**3. Convert this code chunk into a function you can reuse to flatten dictionaries, or explain clearly the problems you run into while attempting to do so. Make some tables or plots.**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "df = pd.DataFrame.from_dict(data)\n",
+        "print(df.shape)\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 759
+        },
+        "id": "ca5gB_41FLMl",
+        "outputId": "1f31e056-e863-4aee-e451-1f2ac9105222"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(216, 22)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   RecallID RecallNumber           RecallDate  \\\n",
+              "0     10024        24329  2024-08-01T00:00:00   \n",
+              "1     10004        24303  2024-07-11T00:00:00   \n",
+              "2      9926        24201  2024-04-18T00:00:00   \n",
+              "3      9927        24202  2024-04-18T00:00:00   \n",
+              "4      9918        24192  2024-04-11T00:00:00   \n",
+              "\n",
+              "                                         Description  \\\n",
+              "0  This recall involves two ECHO 59.7cc gas-power...   \n",
+              "1  This recall involves TonGass 8-inch round pool...   \n",
+              "2  This recall involves Gasaciods children's mult...   \n",
+              "3  This recall involves 750mL and 1500mL sizes of...   \n",
+              "4  This recall involves BRS portable liquid fuel ...   \n",
+              "\n",
+              "                                                 URL  \\\n",
+              "0  https://www.cpsc.gov/Recalls/2024/ECHO-Recalls...   \n",
+              "1  https://www.cpsc.gov/Recalls/2024/TonGass-Pool...   \n",
+              "2  https://www.cpsc.gov/Recalls/2024/Gasaciods-Ch...   \n",
+              "3  https://www.cpsc.gov/Recalls/2024/Randder-Liqu...   \n",
+              "4  https://www.cpsc.gov/Recalls/2024/BRS-Liquid-F...   \n",
+              "\n",
+              "                                               Title  \\\n",
+              "0  ECHO Recalls Gas-Powered Backpack Blowers Due ...   \n",
+              "1  TonGass Pool Drain Covers Recalled Due to Viol...   \n",
+              "2  Gasaciods Children's Helmets Recalled Due to R...   \n",
+              "3  Randder Liquid Fuel Bottles Recalled Due to Ri...   \n",
+              "4  BRS Liquid Fuel Bottles Recalled Due to Risk o...   \n",
+              "\n",
+              "                                     ConsumerContact      LastPublishDate  \\\n",
+              "0  ECHO at 800-432-3246 from 8 a.m. to 4:30 p.m. ...  2024-08-01T00:00:00   \n",
+              "1  TonGass email at tongass@126.com, online at Am...  2024-07-11T00:00:00   \n",
+              "2  Fengwang Sports via email at george66305@163.c...  2024-04-18T00:00:00   \n",
+              "3  Email at catherine0216@outlook.com, or online ...  2024-04-18T00:00:00   \n",
+              "4  Email at chencaiyu1994@hotmail.com, or online ...  2024-04-11T00:00:00   \n",
+              "\n",
+              "                                            Products Inconjunctions  ...  \\\n",
+              "0  [{'Name': 'ECHO gas-powered backpack blowers',...             []  ...   \n",
+              "1  [{'Name': 'TonGass pool drain covers', 'Descri...             []  ...   \n",
+              "2  [{'Name': 'Gasaciods Children's Multi-Purpose ...             []  ...   \n",
+              "3  [{'Name': 'Randder 2-Pack Liquid Fuel Bottles'...             []  ...   \n",
+              "4  [{'Name': 'BRS Liquid Fuel Bottles', 'Descript...             []  ...   \n",
+              "\n",
+              "                                       Manufacturers  \\\n",
+              "0  [{'Name': 'ECHO Inc., of Lake Zurich, Illinois...   \n",
+              "1                                                 []   \n",
+              "2                                                 []   \n",
+              "3                                                 []   \n",
+              "4                                                 []   \n",
+              "\n",
+              "                                           Retailers  \\\n",
+              "0  [{'Name': 'Golden Eagle Distributing Company, ...   \n",
+              "1  [{'Name': 'Exclusively online at www.amazon.co...   \n",
+              "2  [{'Name': 'Online at Temu.com from July 2023 t...   \n",
+              "3  [{'Name': 'Amazon.com from September 2022 thro...   \n",
+              "4  [{'Name': 'Amazon.com from October 2018 throug...   \n",
+              "\n",
+              "                                           Importers  \\\n",
+              "0                                                 []   \n",
+              "1  [{'Name': 'Oneida International Ltd., dba TonG...   \n",
+              "2  [{'Name': 'Dongguan Feng Wang Sports Goods Co....   \n",
+              "3                                                 []   \n",
+              "4                                                 []   \n",
+              "\n",
+              "                                        Distributors SoldAtLabel  \\\n",
+              "0                                                 []        None   \n",
+              "1                                                 []        None   \n",
+              "2                                                 []        None   \n",
+              "3  [{'Name': 'Shenzhenshi Rande Keji Youxian Gong...        None   \n",
+              "4    [{'Name': 'Huenco, of China', 'CompanyID': ''}]        None   \n",
+              "\n",
+              "            ManufacturerCountries ProductUPCs  \\\n",
+              "0  [{'Country': 'United States'}]          []   \n",
+              "1          [{'Country': 'China'}]          []   \n",
+              "2          [{'Country': 'China'}]          []   \n",
+              "3          [{'Country': 'China'}]          []   \n",
+              "4          [{'Country': 'China'}]          []   \n",
+              "\n",
+              "                                             Hazards  \\\n",
+              "0  [{'Name': 'The fuel supply line can leak gas, ...   \n",
+              "1  [{'Name': 'The recalled drain covers do not co...   \n",
+              "2  [{'Name': 'The helmets do not comply with the ...   \n",
+              "3  [{'Name': 'The portable fuel containers do not...   \n",
+              "4  [{'Name': 'The portable fuel bottles do not me...   \n",
+              "\n",
+              "                                            Remedies           RemedyOptions  \n",
+              "0  [{'Name': 'Consumers should immediately stop u...  [{'Option': 'Repair'}]  \n",
+              "1  [{'Name': 'Pool owners/operators and consumers...  [{'Option': 'Refund'}]  \n",
+              "2  [{'Name': 'Consumers should immediately stop u...  [{'Option': 'Refund'}]  \n",
+              "3  [{'Name': 'Consumers should immediately stop u...  [{'Option': 'Repair'}]  \n",
+              "4  [{'Name': 'Consumers should immediately stop u...  [{'Option': 'Refund'}]  \n",
+              "\n",
+              "[5 rows x 22 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-60c84dd6-8508-424b-85f3-42df08211d1a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>RecallID</th>\n",
+              "      <th>RecallNumber</th>\n",
+              "      <th>RecallDate</th>\n",
+              "      <th>Description</th>\n",
+              "      <th>URL</th>\n",
+              "      <th>Title</th>\n",
+              "      <th>ConsumerContact</th>\n",
+              "      <th>LastPublishDate</th>\n",
+              "      <th>Products</th>\n",
+              "      <th>Inconjunctions</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Manufacturers</th>\n",
+              "      <th>Retailers</th>\n",
+              "      <th>Importers</th>\n",
+              "      <th>Distributors</th>\n",
+              "      <th>SoldAtLabel</th>\n",
+              "      <th>ManufacturerCountries</th>\n",
+              "      <th>ProductUPCs</th>\n",
+              "      <th>Hazards</th>\n",
+              "      <th>Remedies</th>\n",
+              "      <th>RemedyOptions</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10024</td>\n",
+              "      <td>24329</td>\n",
+              "      <td>2024-08-01T00:00:00</td>\n",
+              "      <td>This recall involves two ECHO 59.7cc gas-power...</td>\n",
+              "      <td>https://www.cpsc.gov/Recalls/2024/ECHO-Recalls...</td>\n",
+              "      <td>ECHO Recalls Gas-Powered Backpack Blowers Due ...</td>\n",
+              "      <td>ECHO at 800-432-3246 from 8 a.m. to 4:30 p.m. ...</td>\n",
+              "      <td>2024-08-01T00:00:00</td>\n",
+              "      <td>[{'Name': 'ECHO gas-powered backpack blowers',...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>...</td>\n",
+              "      <td>[{'Name': 'ECHO Inc., of Lake Zurich, Illinois...</td>\n",
+              "      <td>[{'Name': 'Golden Eagle Distributing Company, ...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[{'Country': 'United States'}]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'The fuel supply line can leak gas, ...</td>\n",
+              "      <td>[{'Name': 'Consumers should immediately stop u...</td>\n",
+              "      <td>[{'Option': 'Repair'}]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10004</td>\n",
+              "      <td>24303</td>\n",
+              "      <td>2024-07-11T00:00:00</td>\n",
+              "      <td>This recall involves TonGass 8-inch round pool...</td>\n",
+              "      <td>https://www.cpsc.gov/Recalls/2024/TonGass-Pool...</td>\n",
+              "      <td>TonGass Pool Drain Covers Recalled Due to Viol...</td>\n",
+              "      <td>TonGass email at tongass@126.com, online at Am...</td>\n",
+              "      <td>2024-07-11T00:00:00</td>\n",
+              "      <td>[{'Name': 'TonGass pool drain covers', 'Descri...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Exclusively online at www.amazon.co...</td>\n",
+              "      <td>[{'Name': 'Oneida International Ltd., dba TonG...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[{'Country': 'China'}]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'The recalled drain covers do not co...</td>\n",
+              "      <td>[{'Name': 'Pool owners/operators and consumers...</td>\n",
+              "      <td>[{'Option': 'Refund'}]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>9926</td>\n",
+              "      <td>24201</td>\n",
+              "      <td>2024-04-18T00:00:00</td>\n",
+              "      <td>This recall involves Gasaciods children's mult...</td>\n",
+              "      <td>https://www.cpsc.gov/Recalls/2024/Gasaciods-Ch...</td>\n",
+              "      <td>Gasaciods Children's Helmets Recalled Due to R...</td>\n",
+              "      <td>Fengwang Sports via email at george66305@163.c...</td>\n",
+              "      <td>2024-04-18T00:00:00</td>\n",
+              "      <td>[{'Name': 'Gasaciods Children's Multi-Purpose ...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Online at Temu.com from July 2023 t...</td>\n",
+              "      <td>[{'Name': 'Dongguan Feng Wang Sports Goods Co....</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[{'Country': 'China'}]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'The helmets do not comply with the ...</td>\n",
+              "      <td>[{'Name': 'Consumers should immediately stop u...</td>\n",
+              "      <td>[{'Option': 'Refund'}]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9927</td>\n",
+              "      <td>24202</td>\n",
+              "      <td>2024-04-18T00:00:00</td>\n",
+              "      <td>This recall involves 750mL and 1500mL sizes of...</td>\n",
+              "      <td>https://www.cpsc.gov/Recalls/2024/Randder-Liqu...</td>\n",
+              "      <td>Randder Liquid Fuel Bottles Recalled Due to Ri...</td>\n",
+              "      <td>Email at catherine0216@outlook.com, or online ...</td>\n",
+              "      <td>2024-04-18T00:00:00</td>\n",
+              "      <td>[{'Name': 'Randder 2-Pack Liquid Fuel Bottles'...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Amazon.com from September 2022 thro...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Shenzhenshi Rande Keji Youxian Gong...</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[{'Country': 'China'}]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'The portable fuel containers do not...</td>\n",
+              "      <td>[{'Name': 'Consumers should immediately stop u...</td>\n",
+              "      <td>[{'Option': 'Repair'}]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9918</td>\n",
+              "      <td>24192</td>\n",
+              "      <td>2024-04-11T00:00:00</td>\n",
+              "      <td>This recall involves BRS portable liquid fuel ...</td>\n",
+              "      <td>https://www.cpsc.gov/Recalls/2024/BRS-Liquid-F...</td>\n",
+              "      <td>BRS Liquid Fuel Bottles Recalled Due to Risk o...</td>\n",
+              "      <td>Email at chencaiyu1994@hotmail.com, or online ...</td>\n",
+              "      <td>2024-04-11T00:00:00</td>\n",
+              "      <td>[{'Name': 'BRS Liquid Fuel Bottles', 'Descript...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Amazon.com from October 2018 throug...</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'Huenco, of China', 'CompanyID': ''}]</td>\n",
+              "      <td>None</td>\n",
+              "      <td>[{'Country': 'China'}]</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>[{'Name': 'The portable fuel bottles do not me...</td>\n",
+              "      <td>[{'Name': 'Consumers should immediately stop u...</td>\n",
+              "      <td>[{'Option': 'Refund'}]</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 22 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60c84dd6-8508-424b-85f3-42df08211d1a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-60c84dd6-8508-424b-85f3-42df08211d1a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-60c84dd6-8508-424b-85f3-42df08211d1a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-1c8f13ed-63db-4e79-9db1-4c9038cd0701\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c8f13ed-63db-4e79-9db1-4c9038cd0701')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-1c8f13ed-63db-4e79-9db1-4c9038cd0701 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aPN3hvzIFQal",
+        "outputId": "2c1b5aa9-648e-4436-9c37-772ec787c25b"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['RecallID', 'RecallNumber', 'RecallDate', 'Description', 'URL', 'Title',\n",
+              "       'ConsumerContact', 'LastPublishDate', 'Products', 'Inconjunctions',\n",
+              "       'Images', 'Injuries', 'Manufacturers', 'Retailers', 'Importers',\n",
+              "       'Distributors', 'SoldAtLabel', 'ManufacturerCountries', 'ProductUPCs',\n",
+              "       'Hazards', 'Remedies', 'RemedyOptions'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['ManufacturerCountries'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 836
+        },
+        "id": "p4nmYGBlFRB1",
+        "outputId": "b67f3da5-7d5f-49fd-cd2f-9b7f5ada68d6"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ManufacturerCountries\n",
+              "[{'Country': 'United States'}]                                                   61\n",
+              "[{'Country': 'China'}]                                                           53\n",
+              "[]                                                                               49\n",
+              "[{'Country': 'Canada'}]                                                          12\n",
+              "[{'Country': 'Mexico'}]                                                           9\n",
+              "[{'Country': 'Taiwan'}]                                                           8\n",
+              "[{'Country': 'China'}, {'Country': 'Hong Kong'}]                                  4\n",
+              "[{'Country': 'Austria'}]                                                          2\n",
+              "[{'Country': 'Japan'}]                                                            2\n",
+              "[{'Country': 'China'}, {'Country': 'Hong Kong'}, {'Country': 'South Africa'}]     2\n",
+              "[{'Country': 'Germany'}]                                                          2\n",
+              "[{'Country': 'Canada (heater)'}, {'Country': 'Mexico (valve)'}]                   1\n",
+              "[{'Country': 'Romania'}]                                                          1\n",
+              "[{'Country': 'Belgium'}]                                                          1\n",
+              "[{'Country': 'Israel'}]                                                           1\n",
+              "[{'Country': 'Taiwan'}, {'Country': 'United States'}]                             1\n",
+              "[{'Country': 'India'}]                                                            1\n",
+              "[{'Country': 'South Korea'}]                                                      1\n",
+              "[{'Country': 'Czech Republic'}]                                                   1\n",
+              "[{'Country': 'Costa Rica'}]                                                       1\n",
+              "[{'Country': 'Italy'}]                                                            1\n",
+              "[{'Country': 'China'}, {'Country': 'Thailand'}]                                   1\n",
+              "[{'Country': 'United Kingdom'}]                                                   1\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ManufacturerCountries</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'United States'}]</th>\n",
+              "      <td>61</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'China'}]</th>\n",
+              "      <td>53</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[]</th>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Canada'}]</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Mexico'}]</th>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Taiwan'}]</th>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'China'}, {'Country': 'Hong Kong'}]</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Austria'}]</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Japan'}]</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'China'}, {'Country': 'Hong Kong'}, {'Country': 'South Africa'}]</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Germany'}]</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Canada (heater)'}, {'Country': 'Mexico (valve)'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Romania'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Belgium'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Israel'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Taiwan'}, {'Country': 'United States'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'India'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'South Korea'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Czech Republic'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Costa Rica'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'Italy'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'China'}, {'Country': 'Thailand'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>[{'Country': 'United Kingdom'}]</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "temp = df['ManufacturerCountries']\n",
+        "clean_values = []\n",
+        "for i in range(len(temp)):\n",
+        "    if len(temp[i])==1 :\n",
+        "        clean_values.append( str(temp[i][0]['Country']) )\n",
+        "    elif len(temp[i])>1:\n",
+        "        countries = []\n",
+        "        for j in range(len(temp[i])):\n",
+        "            countries.append( temp[i][j]['Country'] )\n",
+        "        clean_values.append( str(countries) )\n",
+        "    else:\n",
+        "        clean_values.append('')\n",
+        "df['mnf_country'] = clean_values"
+      ],
+      "metadata": {
+        "id": "aCMVl2fdFTY3"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['mnf_country'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 836
+        },
+        "id": "7Aqr0rC_FVrM",
+        "outputId": "c6c827eb-d958-4309-f6b2-a533e06cf8c7"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "mnf_country\n",
+              "United States                             61\n",
+              "China                                     53\n",
+              "                                          49\n",
+              "Canada                                    12\n",
+              "Mexico                                     9\n",
+              "Taiwan                                     8\n",
+              "['China', 'Hong Kong']                     4\n",
+              "Austria                                    2\n",
+              "Japan                                      2\n",
+              "['China', 'Hong Kong', 'South Africa']     2\n",
+              "Germany                                    2\n",
+              "['Canada (heater)', 'Mexico (valve)']      1\n",
+              "Romania                                    1\n",
+              "Belgium                                    1\n",
+              "Israel                                     1\n",
+              "['Taiwan', 'United States']                1\n",
+              "India                                      1\n",
+              "South Korea                                1\n",
+              "Czech Republic                             1\n",
+              "Costa Rica                                 1\n",
+              "Italy                                      1\n",
+              "['China', 'Thailand']                      1\n",
+              "United Kingdom                             1\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mnf_country</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>United States</th>\n",
+              "      <td>61</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>China</th>\n",
+              "      <td>53</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Canada</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Mexico</th>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Taiwan</th>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['China', 'Hong Kong']</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Austria</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Japan</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['China', 'Hong Kong', 'South Africa']</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Germany</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Canada (heater)', 'Mexico (valve)']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Romania</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Belgium</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Israel</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Taiwan', 'United States']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>India</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Korea</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Czech Republic</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Costa Rica</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Italy</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['China', 'Thailand']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>United Kingdom</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def flatten(var):\n",
+        "    \"\"\" Flattens a value, recursively concatenating dictionary values. \"\"\"\n",
+        "    temp = df[var] # Get column from the dataframe\n",
+        "    clean_values = [] # List for new values\n",
+        "    key = [*temp[0][0]][0] # Get dictionary key\n",
+        "    for i in range(len(temp)):\n",
+        "        if len(temp[i])==1 : # Only one entry in the value\n",
+        "            clean_values.append( str(temp[i][0][key]) ) # Convert to string and append\n",
+        "        elif len(temp[i])>1: # Multiple entries in the value to flatten\n",
+        "            new_values = [] # List for flattened entries\n",
+        "            for j in range(len(temp[i])): # Iterate over the dictionary\n",
+        "                new_values.append( temp[i][j][key] ) # For the key, append values\n",
+        "            clean_values.append(str(new_values) ) # Convert to string from list and append\n",
+        "        else:\n",
+        "            clean_values.append('') # Missing values get a blank\n",
+        "    return clean_values"
+      ],
+      "metadata": {
+        "id": "OnocEXPAFhku"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['remedy'] = flatten('RemedyOptions')\n",
+        "df['remedy'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "HbAWKXMFFiEa",
+        "outputId": "2f20fc95-974c-4cd4-e9a0-d3d0a9681ea4"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "remedy\n",
+              "                                             139\n",
+              "Repair                                        49\n",
+              "Replace                                       12\n",
+              "Refund                                         7\n",
+              "['Replace', 'Repair']                          4\n",
+              "['Refund', 'Replace', 'Repair']                1\n",
+              "['Replace', 'Refund']                          1\n",
+              "['Refund', 'Repair']                           1\n",
+              "Label                                          1\n",
+              "['New Instructions', 'Replace', 'Refund']      1\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>remedy</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <td>139</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Repair</th>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Replace</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Refund</th>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Replace', 'Repair']</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Refund', 'Replace', 'Repair']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Replace', 'Refund']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['Refund', 'Repair']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Label</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>['New Instructions', 'Replace', 'Refund']</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['remedy'].hist(grid=False,xrot=90)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 704
+        },
+        "id": "cYlNLfYpFjdt",
+        "outputId": "4f0876e8-4662-4eac-f9c0-6dc1a88c1e7b"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['mnf_country'].hist(grid=False,xrot=90,bins=30)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 684
+        },
+        "id": "LZpjZKAKFlIr",
+        "outputId": "95010452-26db-4563-9026-0544f3e9d8e0"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option('display.max_rows', None)\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "pd.crosstab( df['remedy'],df['mnf_country'] )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "id": "VzZl94CLFm4z",
+        "outputId": "d9e061e7-443e-423c-d784-fbbcc260c798"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "mnf_country                                    Austria  Belgium  Canada  \\\n",
+              "remedy                                                                    \n",
+              "                                           49        0        0       6   \n",
+              "Label                                       0        0        0       0   \n",
+              "Refund                                      0        0        0       0   \n",
+              "Repair                                      0        2        1       5   \n",
+              "Replace                                     0        0        0       0   \n",
+              "['New Instructions', 'Replace', 'Refund']   0        0        0       0   \n",
+              "['Refund', 'Repair']                        0        0        0       0   \n",
+              "['Refund', 'Replace', 'Repair']             0        0        0       0   \n",
+              "['Replace', 'Refund']                       0        0        0       0   \n",
+              "['Replace', 'Repair']                       0        0        0       1   \n",
+              "\n",
+              "mnf_country                                China  Costa Rica  Czech Republic  \\\n",
+              "remedy                                                                         \n",
+              "                                              28           1               1   \n",
+              "Label                                          1           0               0   \n",
+              "Refund                                         6           0               0   \n",
+              "Repair                                        11           0               0   \n",
+              "Replace                                        5           0               0   \n",
+              "['New Instructions', 'Replace', 'Refund']      0           0               0   \n",
+              "['Refund', 'Repair']                           1           0               0   \n",
+              "['Refund', 'Replace', 'Repair']                1           0               0   \n",
+              "['Replace', 'Refund']                          0           0               0   \n",
+              "['Replace', 'Repair']                          0           0               0   \n",
+              "\n",
+              "mnf_country                                Germany  India  Israel  Italy  \\\n",
+              "remedy                                                                     \n",
+              "                                                 1      0       0      1   \n",
+              "Label                                            0      0       0      0   \n",
+              "Refund                                           0      0       0      0   \n",
+              "Repair                                           1      1       1      0   \n",
+              "Replace                                          0      0       0      0   \n",
+              "['New Instructions', 'Replace', 'Refund']        0      0       0      0   \n",
+              "['Refund', 'Repair']                             0      0       0      0   \n",
+              "['Refund', 'Replace', 'Repair']                  0      0       0      0   \n",
+              "['Replace', 'Refund']                            0      0       0      0   \n",
+              "['Replace', 'Repair']                            0      0       0      0   \n",
+              "\n",
+              "mnf_country                                Japan  Mexico  Romania  \\\n",
+              "remedy                                                              \n",
+              "                                               2       5        0   \n",
+              "Label                                          0       0        0   \n",
+              "Refund                                         0       0        1   \n",
+              "Repair                                         0       3        0   \n",
+              "Replace                                        0       0        0   \n",
+              "['New Instructions', 'Replace', 'Refund']      0       0        0   \n",
+              "['Refund', 'Repair']                           0       0        0   \n",
+              "['Refund', 'Replace', 'Repair']                0       0        0   \n",
+              "['Replace', 'Refund']                          0       0        0   \n",
+              "['Replace', 'Repair']                          0       1        0   \n",
+              "\n",
+              "mnf_country                                South Korea  Taiwan  \\\n",
+              "remedy                                                           \n",
+              "                                                     0       6   \n",
+              "Label                                                0       0   \n",
+              "Refund                                               0       0   \n",
+              "Repair                                               1       0   \n",
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+              "mnf_country                                United Kingdom  United States  \\\n",
+              "remedy                                                                     \n",
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+              "\n",
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+              "\n",
+              "mnf_country                                ['China', 'Hong Kong', 'South Africa']  \\\n",
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+              "\n",
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+              "\n",
+              "mnf_country                                ['China', 'Thailand']  \\\n",
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+              "mnf_country                                ['Taiwan', 'United States']  \n",
+              "remedy                                                                  \n",
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+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a1b57ef6-35ab-4721-83b9-3faebe9b012d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "      <th>mnf_country</th>\n",
+              "      <th></th>\n",
+              "      <th>Austria</th>\n",
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+              "      <th>Canada</th>\n",
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+              "      <th>Israel</th>\n",
+              "      <th>Italy</th>\n",
+              "      <th>Japan</th>\n",
+              "      <th>Mexico</th>\n",
+              "      <th>Romania</th>\n",
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+              "      <th>Taiwan</th>\n",
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+              "      <th>United States</th>\n",
+              "      <th>['Canada (heater)', 'Mexico (valve)']</th>\n",
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+              "      <th>['Taiwan', 'United States']</th>\n",
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+              "      <th>remedy</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1b57ef6-35ab-4721-83b9-3faebe9b012d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a1b57ef6-35ab-4721-83b9-3faebe9b012d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a1b57ef6-35ab-4721-83b9-3faebe9b012d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0bee4cf1-82b8-480f-a1d9-232d9e33f078\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0bee4cf1-82b8-480f-a1d9-232d9e33f078')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0bee4cf1-82b8-480f-a1d9-232d9e33f078 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "glTQ8P7ggGP_"
+      },
+      "source": [
+        "## Dashboarding the Results (Optional)\n",
+        "\n",
+        "To complete the pipeline from data to product, we can use \\texttt{streamlit} to quickly convert Python code into a web page that can be accessed locally. This can be done with essentially three lines of code: An import statement, a $.title()$ method call to set the page title, and an $.write()$ call to push the results to the page. Although relatively static, completing this step serves a pedogogical and psychological purpose: It pivots the students to thinking about how to communicate results to an audience, and how the project could become an ongoing endeavor rather than a single analytical exercise.\n",
+        "\n",
+        "The entire .py file to create the dashboard is\n",
+        "\n",
+        "    import pandas as pd\n",
+        "    import requests\n",
+        "    import streamlit as st\n",
+        "    # Conduct analysis:\n",
+        "    url = 'https://www.saferproducts.gov/RestWebServices/Recall'\n",
+        "    query = '?format=json&RecallTitle=Gas'\n",
+        "    header = {'User-Agent':\n",
+        "              'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:124.0) Gecko/20100101 Firefox/124.0'}\n",
+        "    raw = requests.get(url+query,headers=header)\n",
+        "    data = raw.json()\n",
+        "    df = pd.DataFrame.from_dict(data)\n",
+        "    temp = df['RemedyOptions']\n",
+        "    clean_values = []\n",
+        "    for i in range(len(temp)):\n",
+        "        if len(temp[i])>0:\n",
+        "            values = []\n",
+        "            for j in range(len(temp[i])):\n",
+        "                values.append(temp[i][j]['Option'] )\n",
+        "            clean_values.append(values)\n",
+        "        else:\n",
+        "            clean_values.append('')\n",
+        "    df['remedy'] = clean_values\n",
+        "    remedy_counts = df['remedy'].value_counts()\n",
+        "    # Create streamlit output:\n",
+        "    st.title('Remedy Statistics')\n",
+        "    st.write(remedy_counts)\n",
+        "\n",
+        "To create the web page, run the following at the command line:\n",
+        "\n",
+        "    streamlit run remedy.py\n",
+        "\n",
+        "This should convert the above analysis into a web page available from localhost.\n",
+        "\n",
+        "**4. Produce your own table or plot, and output it to streamlit.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import streamlit as st\n",
+        "import urllib.request\n",
+        "import json\n",
+        "\n",
+        "\"\"\"\n",
+        "## Dashboard Template\n",
+        "\n",
+        "This simple dashboard provides tables from the saferproducts.gov API.\n",
+        "\n",
+        "- `remedy` is how consumers were compensated for the recall\n",
+        "- `mnf_country` is the place where the product originated\n",
+        "\n",
+        "We focus on products in which the word \"gas\" appeared in the Recall Title.\n",
+        "\"\"\"\n",
+        "\n",
+        "url = 'https://www.saferproducts.gov/RestWebServices/Recall'\n",
+        "#query = '?format=json&ProductType=Phone' #29\n",
+        "#query = '?format=json&ProductType=Grill' #70\n",
+        "#query = '?format=json&ProductType=Exercise' # 91\n",
+        "query = '?format=json&RecallTitle=Gas' # 216\n",
+        "\n",
+        "response = urllib.request.urlopen(url+query)\n",
+        "response_bytes = response.read()\n",
+        "data = json.loads(response_bytes)\n",
+        "response.close()\n",
+        "\n",
+        "df = pd.DataFrame.from_dict(data)\n",
+        "print(df.shape)\n",
+        "\n",
+        "df.head()\n",
+        "\n",
+        "temp = df['ManufacturerCountries']\n",
+        "clean_values = []\n",
+        "for i in range(len(temp)):\n",
+        "    if len(temp[i])==1 :\n",
+        "        clean_values.append( str(temp[i][0]['Country']) )\n",
+        "    elif len(temp[i])>1:\n",
+        "        countries = []\n",
+        "        for j in range(len(temp[i])):\n",
+        "            countries.append( temp[i][j]['Country'] )\n",
+        "        clean_values.append( str(countries) )\n",
+        "    else:\n",
+        "        clean_values.append('')\n",
+        "df['mnf_country'] = clean_values\n",
+        "st.write(df['mnf_country'].value_counts())\n",
+        "\n",
+        "temp = df['RemedyOptions']\n",
+        "clean_values = []\n",
+        "for i in range(len(temp)):\n",
+        "    if len(temp[i])>0:\n",
+        "        values = []\n",
+        "        for j in range(len(temp[i])):\n",
+        "            values.append(temp[i][j]['Option'] )\n",
+        "        clean_values.append(values[0])\n",
+        "    else:\n",
+        "        clean_values.append('')\n",
+        "df['remedy'] = clean_values\n",
+        "st.write(df['remedy'].value_counts())\n",
+        "\n",
+        "pd.set_option('display.max_rows', None)\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "xtab = pd.crosstab( df['remedy'],df['mnf_country'] )\n",
+        "\n",
+        "# Create streamlit output:\n",
+        "st.write(xtab)"
+      ],
+      "metadata": {
+        "id": "iyZ8UtMPH0Qn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ogbrOZYgGP_"
+      },
+      "source": [
+        "## Other API Sources\n",
+        "\n",
+        "Valuable and interesting Federal API resources are listed at:\n",
+        "\n",
+        "    https://catalog.data.gov/dataset/?_res_format_limit=0&res_format=API\n",
+        "\n",
+        "and in the future will likely easily be found at \\texttt{data.gov}. Some highlights include:\n",
+        "\n",
+        "- CDC WONDER API for Data Query Web Service: Includes death certificates with causes since approximately the 1990's.\n",
+        "- Comprehensive Housing Affordability Strategy (CHAS): Housing and Urban Development (HUD) maintains an API that provides Census data on housing problems and needs unavailable through other sources, including IPUMS.\n",
+        "- Federal Election Commission API: Provides historical and up to the minute campaign finance data.\n",
+        "- Toxic Release Inventory: Provided by the Environmental Protection Agency, this API documents the release and management of over 800 toxic substances, reported annually by privately owned facilities and the government.\n",
+        "- Petroleum Data, Prices: Provides prices of petroleum products and crude oil at weekly, monthly, and yearly time scales.\n",
+        "- Fair Market Rents Lookup tool: Fair Market Rents (FMRs) determine the value of housing vouchers for Section 8 renters. This API provides the FMR values and other measures of housing affordability.\n",
+        "- Annual Economic Surveys, Business Patterns: Surveys of businesses at the zip code level, tracking economic sentiment and activity.\n",
+        "- Food Access Research Atlas: Provides spatial data on food access and the availability of supermarkets within census tracts. Can be merged with census data to look at under-served populations and food deserts.\n",
+        "- National Oceanographic and Atmospheric Administration: Provides API access to data on real time weather and climate change projections.\n",
+        "\n",
+        "Each of these API resources could either be the cornerstone of a project or a source of additional data. These data sources have a number of advantages: They're free, most of them can be accessed using the same API key, and most have similar documentation for how to write a query. This is ideal for students to iterate, experiment, and take risks, with little cost to failure.\n",
+        "\n",
+        "In addition to government data, many commerical apps provide API access to developers and researchers. AirBnB, Amazon, Reddit, eBay, X, and many others maintain API access to develop third-party apps. These opportunities present many advantages: The data are larger, have more variety, and there are vastly many more cases. Building a third-party app that includes analytics could easily consume an entire semester and open a variety of applications in predictive analytics, natural language processing, and generative AI (e.g. predict which reviews are fake or real for Amazon for a product group like ``women's watches', and then make recommendations for different price points). While an exciting possibility, this can also raise a lot of problems: Some API's cost money or are rate-limited depending on a subscription, and others impose significant constraints on how the data can be used. In some cases, a more useful approach might be explicit web scraping using a package like BeautifulSoup or Selenium. For example, Craigslist has no API, but can easily and productively be scraped using BeautifulSoup.\n",
+        "\n",
+        "**5. Pick an API, download some data, wrangle them, and produce some EDA results, as we did in the previous steps with the saferproducts.gov API; or, if you can't get it to work, document why. If you have the time and it's low cost, push the results to a streamlit page. If you have had enough, I recommend https://www.eia.gov/opendata/browser/electricity, since there is a friendly query builder that you can use to learn.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import urllib.request\n",
+        "import json\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "## Build query:\n",
+        "url = 'https://api.eia.gov/v2/electricity/retail-sales/data/'\n",
+        "api_key = '?api_key=YYz5xOSLjJP5ZbZ2sPbsNAuvBdEdEnSGhsEjkSFr'\n",
+        "query = '&frequency=monthly&data[0]=customers&data[1]=price&data[2]=revenue&data[3]=sales&facets[stateid][]=VA&start=2013-01&end=2023-01&sort[0][column]=period&sort[0][direction]=desc&offset=0&length=5000'\n",
+        "\n",
+        "## Query the API:\n",
+        "response = urllib.request.urlopen(url+api_key+query)\n",
+        "response_bytes = response.read()\n",
+        "data = json.loads(response_bytes)\n",
+        "response.close()\n",
+        "\n",
+        "## Convert results to dataframe:\n",
+        "df = pd.DataFrame.from_dict(data['response']['data'])\n",
+        "df.to_csv('electricity_data.csv')\n",
+        "print(df.shape)\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        },
+        "id": "Xr3WDkU5Fyt1",
+        "outputId": "ce6d3201-e945-4f67-c116-d0cf28c639ce"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(726, 13)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    period stateid stateDescription sectorid      sectorName customers  price  \\\n",
+              "0  2023-01      VA         Virginia      ALL     all sectors   4070280  11.76   \n",
+              "1  2023-01      VA         Virginia      TRA  transportation         1  12.21   \n",
+              "2  2023-01      VA         Virginia      RES     residential   3618436  14.03   \n",
+              "3  2023-01      VA         Virginia      OTH           other      None   None   \n",
+              "4  2023-01      VA         Virginia      IND      industrial      3675   9.39   \n",
+              "\n",
+              "     revenue        sales      customers-units             price-units  \\\n",
+              "0  1258.2641  10696.00511  number of customers  cents per kilowatthour   \n",
+              "1    1.81683     14.88526  number of customers  cents per kilowatthour   \n",
+              "2  630.32808   4491.90335  number of customers  cents per kilowatthour   \n",
+              "3       None         None  number of customers  cents per kilowatthour   \n",
+              "4  126.31832   1344.93917  number of customers  cents per kilowatthour   \n",
+              "\n",
+              "     revenue-units            sales-units  \n",
+              "0  million dollars  million kilowatthours  \n",
+              "1  million dollars  million kilowatthours  \n",
+              "2  million dollars  million kilowatthours  \n",
+              "3  million dollars  million kilowatthours  \n",
+              "4  million dollars  million kilowatthours  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-24071a2b-5c0f-4428-9bd5-bbd5114412d9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>period</th>\n",
+              "      <th>stateid</th>\n",
+              "      <th>stateDescription</th>\n",
+              "      <th>sectorid</th>\n",
+              "      <th>sectorName</th>\n",
+              "      <th>customers</th>\n",
+              "      <th>price</th>\n",
+              "      <th>revenue</th>\n",
+              "      <th>sales</th>\n",
+              "      <th>customers-units</th>\n",
+              "      <th>price-units</th>\n",
+              "      <th>revenue-units</th>\n",
+              "      <th>sales-units</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2023-01</td>\n",
+              "      <td>VA</td>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>ALL</td>\n",
+              "      <td>all sectors</td>\n",
+              "      <td>4070280</td>\n",
+              "      <td>11.76</td>\n",
+              "      <td>1258.2641</td>\n",
+              "      <td>10696.00511</td>\n",
+              "      <td>number of customers</td>\n",
+              "      <td>cents per kilowatthour</td>\n",
+              "      <td>million dollars</td>\n",
+              "      <td>million kilowatthours</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2023-01</td>\n",
+              "      <td>VA</td>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>TRA</td>\n",
+              "      <td>transportation</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.21</td>\n",
+              "      <td>1.81683</td>\n",
+              "      <td>14.88526</td>\n",
+              "      <td>number of customers</td>\n",
+              "      <td>cents per kilowatthour</td>\n",
+              "      <td>million dollars</td>\n",
+              "      <td>million kilowatthours</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2023-01</td>\n",
+              "      <td>VA</td>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>RES</td>\n",
+              "      <td>residential</td>\n",
+              "      <td>3618436</td>\n",
+              "      <td>14.03</td>\n",
+              "      <td>630.32808</td>\n",
+              "      <td>4491.90335</td>\n",
+              "      <td>number of customers</td>\n",
+              "      <td>cents per kilowatthour</td>\n",
+              "      <td>million dollars</td>\n",
+              "      <td>million kilowatthours</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2023-01</td>\n",
+              "      <td>VA</td>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>OTH</td>\n",
+              "      <td>other</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>number of customers</td>\n",
+              "      <td>cents per kilowatthour</td>\n",
+              "      <td>million dollars</td>\n",
+              "      <td>million kilowatthours</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2023-01</td>\n",
+              "      <td>VA</td>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>IND</td>\n",
+              "      <td>industrial</td>\n",
+              "      <td>3675</td>\n",
+              "      <td>9.39</td>\n",
+              "      <td>126.31832</td>\n",
+              "      <td>1344.93917</td>\n",
+              "      <td>number of customers</td>\n",
+              "      <td>cents per kilowatthour</td>\n",
+              "      <td>million dollars</td>\n",
+              "      <td>million kilowatthours</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24071a2b-5c0f-4428-9bd5-bbd5114412d9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-24071a2b-5c0f-4428-9bd5-bbd5114412d9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-24071a2b-5c0f-4428-9bd5-bbd5114412d9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a1e19da5-4c79-4e69-8b3d-15e1eb7ca1b6\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a1e19da5-4c79-4e69-8b3d-15e1eb7ca1b6')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a1e19da5-4c79-4e69-8b3d-15e1eb7ca1b6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 726,\n  \"fields\": [\n    {\n      \"column\": \"period\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 121,\n        \"samples\": [\n          \"2019-05\",\n          \"2019-02\",\n          \"2022-09\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stateid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"VA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stateDescription\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sectorid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"ALL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sectorName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"all sectors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customers\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 446,\n        \"samples\": [\n          \"3805165\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 362,\n        \"samples\": [\n          \"8.11\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 605,\n        \"samples\": [\n          \"1.2592\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 604,\n        \"samples\": [\n          \"1427.3179\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customers-units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"number of customers\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price-units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"cents per kilowatthour\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue-units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"million dollars\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales-units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"million kilowatthours\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['response']['description']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "Wrh1wSfAF1Up",
+        "outputId": "90e142d5-3367-43e5-fa66-2aa1a261ca23"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'Electricity sales to ultimate customer by state and sector (number of customers, average price, revenue, and megawatthours of sales).  \\r\\n    Sources: Forms EIA-826, EIA-861, EIA-861M'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QDL_4uFJF2zc",
+        "outputId": "c42823ea-7b6c-4e48-adb5-e6436a91bde4"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['period', 'stateid', 'stateDescription', 'sectorid', 'sectorName',\n",
+              "       'customers', 'price', 'revenue', 'sales', 'customers-units',\n",
+              "       'price-units', 'revenue-units', 'sales-units'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['price'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oNziiSsuF4Ln",
+        "outputId": "4dceb6d5-480f-4d81-f0b4-872b7cbaffc8"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['11.76', '12.21', '14.03', None, '9.39', '10.32', '11.55', '11.14',\n",
+              "       '13.81', '8.89', '10.25', '11.09', '12.33', '14.32', '8.79',\n",
+              "       '9.84', '11.22', '11.64', '14.41', '8.67', '10.3', '11.63',\n",
+              "       '10.86', '14.58', '8.56', '10.63', '11.68', '11.67', '14.14',\n",
+              "       '8.65', '10.6', '11.31', '10.7', '14.23', '8.36', '9.88', '10.66',\n",
+              "       '10.8', '13.32', '7.69', '9.58', '9.75', '10.39', '10.38', '12.82',\n",
+              "       '7.58', '9.76', '10.02', '12.66', '7.27', '8.87', '9.73', '10.65',\n",
+              "       '12.37', '7.12', '8.59', '10.2', '11.93', '7.2', '8.48', '9.85',\n",
+              "       '9.98', '11.97', '7', '8.57', '9.77', '10.15', '11.71', '7.82',\n",
+              "       '9.13', '10.17', '11.81', '6.67', '8.22', '9.31', '8.42', '6.97',\n",
+              "       '8.3', '9.41', '8.04', '6.94', '8.06', '8.15', '12.71', '6.68',\n",
+              "       '9.46', '8.19', '12.53', '6.41', '7.87', '8.1', '12.41', '6.13',\n",
+              "       '7.64', '8.49', '12.06', '6.11', '7.43', '8.08', '8.32', '5.47',\n",
+              "       '6.59', '7.75', '9.06', '8.44', '6.55', '7.48', '9.02', '11.24',\n",
+              "       '8.78', '8.37', '10.89', '5.93', '7.33', '9.01', '8.58', '11.17',\n",
+              "       '6.22', '7.4', '8.71', '8.92', '11.59', '6', '7.46', '8.64', '8.6',\n",
+              "       '6.2', '8.98', '12.02', '6.46', '12.27', '6.25', '7.55', '9.37',\n",
+              "       '9.33', '9.51', '6.23', '9.99', '12.31', '7.54', '9.08', '9.86',\n",
+              "       '12.3', '6.17', '7.67', '9.34', '12.67', '6.52', '7.88', '9.28',\n",
+              "       '8.14', '6.36', '8.03', '9.3', '8.28', '12.07', '6.37', '7.92',\n",
+              "       '6.35', '7.84', '9.53', '7.9', '11.73', '6.72', '8.11', '9.44',\n",
+              "       '12.12', '6.89', '8.17', '12.39', '6.78', '8.07', '12.14', '6.4',\n",
+              "       '8', '9.63', '8.62', '12.44', '6.85', '9.71', '12.16', '7.13',\n",
+              "       '9.69', '8.53', '12.56', '6.74', '9.5', '8.35', '12.46', '7.05',\n",
+              "       '8.26', '8.38', '9.49', '8.18', '11.78', '7.22', '9.54', '6.9',\n",
+              "       '8.34', '8.21', '11.48', '6.8', '10.97', '8.27', '8.39', '8.29',\n",
+              "       '11.6', '8.43', '11.84', '6.91', '8.25', '9.48', '12.01', '6.84',\n",
+              "       '12.26', '6.92', '8.33', '9.72', '12.22', '8.46', '11.79', '6.81',\n",
+              "       '9.4', '8.54', '11.41', '7.98', '10.88', '6.77', '8.24', '8.61',\n",
+              "       '10.76', '9.07', '11.47', '8.01', '9.32', '12.03', '9.6', '12.18',\n",
+              "       '6.66', '9.7', '6.57', '9.26', '11.96', '7.95', '9.04', '11.95',\n",
+              "       '6.31', '8.94', '9', '7.89', '11.53', '6.39', '7.85', '8.81',\n",
+              "       '11.15', '6.44', '7.51', '7.77', '10.41', '6.38', '8.83', '7.83',\n",
+              "       '10.67', '6.45', '7.66', '11.45', '6.51', '7.71', '7.6', '7.31',\n",
+              "       '9.27', '7.28', '11.66', '6.5', '7.8', '9.29', '6.61', '9.52',\n",
+              "       '6.58', '9.14', '6.54', '8.16', '9.11', '6.63', '8.13', '7.93',\n",
+              "       '11.12', '9.21', '10.84', '10.59', '9.03', '10.96', '6.64', '8.05',\n",
+              "       '9.16', '6.83', '8.96', '6.79', '7.81', '11.85', '6.86', '11.7',\n",
+              "       '6.96', '6.98', '11.82', '9.45', '8.12', '11.42', '10.94', '7.18',\n",
+              "       '8.45', '11.04', '7.26', '8.5', '9.47', '8.75', '10.81', '7.45',\n",
+              "       '9.35', '8.55', '11.46', '11.57', '7.03', '7.04', '8.4', '11.9',\n",
+              "       '11.89', '7.19', '8.31', '11.65', '8.85', '6.7', '11.32', '8.86',\n",
+              "       '11', '7.97', '10.52', '10.08', '8.82', '10.01', '6.71', '10.36',\n",
+              "       '6.56', '7.86', '8.66', '10.92', '9.2', '11.5', '11.49', '7.79',\n",
+              "       '9.15', '6.65', '8.8', '11.21', '7.94', '10.54', '6.49', '8.77',\n",
+              "       '10.42', '10.24', '8.02', '9.92', '8.09'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Coerce text to numbers:\n",
+        "df['price'] = pd.to_numeric(df['price'], errors='coerce')\n",
+        "df['revenue'] = pd.to_numeric(df['revenue'], errors='coerce')\n",
+        "df['sales'] = pd.to_numeric(df['sales'], errors='coerce')"
+      ],
+      "metadata": {
+        "id": "5bxBKWBqF6BB"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['price']*df['sales'] # equals revenue: only use p and q, not p*q"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
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+      },
+      "execution_count": 30,
+      "outputs": [
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+              "666     74319.792635\n",
+              "667       133.456500\n",
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+              "674     38128.122350\n",
+              "675              NaN\n",
+              "676      9472.653064\n",
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+              "684     99600.687484\n",
+              "685       131.728900\n",
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+              "687              NaN\n",
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+              "689     37026.015300\n",
+              "690     85630.897292\n",
+              "691       130.636350\n",
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+              "693              NaN\n",
+              "694      9838.908947\n",
+              "695     33900.353310\n",
+              "696     72702.164744\n",
+              "697       127.941960\n",
+              "698     31275.699341\n",
+              "699              NaN\n",
+              "700      9741.349933\n",
+              "701     31533.762722\n",
+              "702     68623.337379\n",
+              "703       142.344180\n",
+              "704     32920.557899\n",
+              "705              NaN\n",
+              "706      8331.238635\n",
+              "707     27218.311388\n",
+              "708     83364.878147\n",
+              "709       113.363900\n",
+              "710     43305.017443\n",
+              "711              NaN\n",
+              "712      9222.622670\n",
+              "713     30724.843027\n",
+              "714     81485.299675\n",
+              "715     28927.063915\n",
+              "716      9078.561930\n",
+              "717              NaN\n",
+              "718     43356.352307\n",
+              "719       134.198660\n",
+              "720     86139.708963\n",
+              "721     30757.398628\n",
+              "722      8825.240673\n",
+              "723              NaN\n",
+              "724     46422.389690\n",
+              "725       151.800760\n",
+              "dtype: float64"
+            ],
+            "text/html": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "    .dataframe thead th {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>125785.020094</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>181.749025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>63021.404000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>12628.978806</td>\n",
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+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>49992.941942</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>138117.210000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>140.158356</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>67331.150779</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>12242.629337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>58352.828622</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>117196.179708</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>120.903295</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>46488.512583</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>11702.778866</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>58844.681707</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>110020.614409</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>106.705393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>40233.556356</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>13078.416606</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>56609.477740</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>124374.208677</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>123.389148</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>49898.990326</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>11720.674284</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>62623.777132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>142991.933851</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>115.999333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>63777.299632</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>12463.321533</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>66581.318794</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>149056.565046</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>120.328883</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>69351.093347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>12621.377745</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>67032.046588</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>115432.946036</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>142.184376</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>51374.505535</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>11326.763644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>52551.811757</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>55681.305030</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>106311.111920</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>93.803437</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>39981.700796</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>10562.149296</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>54</th>\n",
+              "      <td>94663.572923</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>55</th>\n",
+              "      <td>92.489009</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>56</th>\n",
+              "      <td>36018.309832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>57</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>58</th>\n",
+              "      <td>10068.410493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>59</th>\n",
+              "      <td>48429.598348</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>60</th>\n",
+              "      <td>93988.518655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61</th>\n",
+              "      <td>121.869867</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>62</th>\n",
+              "      <td>42709.875250</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>63</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>64</th>\n",
+              "      <td>9883.599662</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>65</th>\n",
+              "      <td>41295.355287</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>66</th>\n",
+              "      <td>108936.582966</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>67</th>\n",
+              "      <td>128.904540</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>68</th>\n",
+              "      <td>55084.510209</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>69</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>9155.458368</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>71</th>\n",
+              "      <td>44604.764723</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>72</th>\n",
+              "      <td>120524.805865</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>73</th>\n",
+              "      <td>101.664164</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>74</th>\n",
+              "      <td>61123.869185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>76</th>\n",
+              "      <td>9893.759890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>77</th>\n",
+              "      <td>49357.055960</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>78</th>\n",
+              "      <td>38363.079745</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>79</th>\n",
+              "      <td>98504.748647</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>80</th>\n",
+              "      <td>99.951820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81</th>\n",
+              "      <td>49023.677638</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>82</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>83</th>\n",
+              "      <td>11023.098979</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>84</th>\n",
+              "      <td>93790.951778</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>85</th>\n",
+              "      <td>98.640457</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>86</th>\n",
+              "      <td>39721.742781</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>88</th>\n",
+              "      <td>11590.594601</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>42431.746696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>90</th>\n",
+              "      <td>89954.921775</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>91</th>\n",
+              "      <td>146.732140</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>92</th>\n",
+              "      <td>36626.483419</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>93</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>94</th>\n",
+              "      <td>12138.695574</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>41020.930640</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>97748.745473</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>128.751595</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>45827.408392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100</th>\n",
+              "      <td>11193.366796</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>101</th>\n",
+              "      <td>40639.492278</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>102</th>\n",
+              "      <td>116467.810857</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103</th>\n",
+              "      <td>155.349840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>104</th>\n",
+              "      <td>57562.242486</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>105</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>106</th>\n",
+              "      <td>12430.944464</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>107</th>\n",
+              "      <td>46333.042184</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>108</th>\n",
+              "      <td>115957.297293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>109</th>\n",
+              "      <td>112.649273</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>110</th>\n",
+              "      <td>59444.409002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>111</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>112</th>\n",
+              "      <td>11715.672636</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>113</th>\n",
+              "      <td>44638.664948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>114</th>\n",
+              "      <td>98787.522952</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>115</th>\n",
+              "      <td>124.416162</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>116</th>\n",
+              "      <td>48709.795916</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>117</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>118</th>\n",
+              "      <td>10481.370815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>119</th>\n",
+              "      <td>39497.643762</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>120</th>\n",
+              "      <td>80662.091400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>121</th>\n",
+              "      <td>115.083393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>122</th>\n",
+              "      <td>35644.744638</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>123</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>124</th>\n",
+              "      <td>10133.868749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>125</th>\n",
+              "      <td>34741.510741</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>126</th>\n",
+              "      <td>69711.555824</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127</th>\n",
+              "      <td>124.271430</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>128</th>\n",
+              "      <td>30870.115388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>129</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130</th>\n",
+              "      <td>9476.472892</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>131</th>\n",
+              "      <td>29272.047324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>132</th>\n",
+              "      <td>9149.107739</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>133</th>\n",
+              "      <td>33733.031765</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>134</th>\n",
+              "      <td>87266.615083</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135</th>\n",
+              "      <td>139.140490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>136</th>\n",
+              "      <td>44301.453851</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>137</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>138</th>\n",
+              "      <td>9646.450668</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>139</th>\n",
+              "      <td>33083.472343</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>140</th>\n",
+              "      <td>95960.396147</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>141</th>\n",
+              "      <td>110.351073</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>142</th>\n",
+              "      <td>53161.433139</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>143</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>144</th>\n",
+              "      <td>100118.075810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>126.991732</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>57166.743764</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>9018.785370</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>33845.921767</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>150</th>\n",
+              "      <td>98796.192692</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>151</th>\n",
+              "      <td>131.875458</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>152</th>\n",
+              "      <td>57048.681072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>153</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>154</th>\n",
+              "      <td>8700.077337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>155</th>\n",
+              "      <td>32883.841168</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>156</th>\n",
+              "      <td>77981.484103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>157</th>\n",
+              "      <td>113.871204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>158</th>\n",
+              "      <td>37662.166862</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>159</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>160</th>\n",
+              "      <td>9415.769940</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>161</th>\n",
+              "      <td>30746.370455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>162</th>\n",
+              "      <td>72961.744550</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163</th>\n",
+              "      <td>124.456534</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>164</th>\n",
+              "      <td>30004.142727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>165</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>166</th>\n",
+              "      <td>9293.858342</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>167</th>\n",
+              "      <td>33534.756072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>168</th>\n",
+              "      <td>85335.275018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>169</th>\n",
+              "      <td>125.476762</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>170</th>\n",
+              "      <td>41970.884883</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>171</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>172</th>\n",
+              "      <td>8403.794555</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>173</th>\n",
+              "      <td>34885.129446</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>174</th>\n",
+              "      <td>60067.850276</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>175</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176</th>\n",
+              "      <td>9588.205813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>177</th>\n",
+              "      <td>39088.917076</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>178</th>\n",
+              "      <td>108850.344005</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>179</th>\n",
+              "      <td>92.747477</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>180</th>\n",
+              "      <td>116270.573626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>181</th>\n",
+              "      <td>117.039380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>182</th>\n",
+              "      <td>65892.233131</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>183</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>184</th>\n",
+              "      <td>10463.415269</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>185</th>\n",
+              "      <td>39770.818126</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>186</th>\n",
+              "      <td>92919.632507</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>187</th>\n",
+              "      <td>79.665655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>188</th>\n",
+              "      <td>50454.173215</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>189</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>190</th>\n",
+              "      <td>8176.535278</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>191</th>\n",
+              "      <td>34232.947323</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>192</th>\n",
+              "      <td>76419.219942</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193</th>\n",
+              "      <td>102.772062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>194</th>\n",
+              "      <td>37430.260134</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>195</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>196</th>\n",
+              "      <td>9140.339805</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>29769.290585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>198</th>\n",
+              "      <td>74426.235985</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>199</th>\n",
+              "      <td>114.189626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>200</th>\n",
+              "      <td>35344.544442</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>201</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>202</th>\n",
+              "      <td>8726.694522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>203</th>\n",
+              "      <td>30233.888810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>204</th>\n",
+              "      <td>83822.214336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>134.205971</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>39911.674618</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>9164.202546</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>34580.369677</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>210</th>\n",
+              "      <td>88418.574852</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>211</th>\n",
+              "      <td>139.837194</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>212</th>\n",
+              "      <td>44836.584703</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>213</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>214</th>\n",
+              "      <td>9516.072803</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>215</th>\n",
+              "      <td>33912.515102</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>216</th>\n",
+              "      <td>97865.943489</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>217</th>\n",
+              "      <td>166.973886</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>218</th>\n",
+              "      <td>53679.021880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>220</th>\n",
+              "      <td>9052.313302</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>221</th>\n",
+              "      <td>34977.998810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>222</th>\n",
+              "      <td>96754.285719</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>223</th>\n",
+              "      <td>184.433321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>52412.648980</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>8689.431542</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>35423.897215</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>88827.544683</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>229</th>\n",
+              "      <td>98.075286</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>230</th>\n",
+              "      <td>42308.689435</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>231</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>232</th>\n",
+              "      <td>9922.646246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>233</th>\n",
+              "      <td>36493.743091</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>234</th>\n",
+              "      <td>82412.604794</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>235</th>\n",
+              "      <td>123.547981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>236</th>\n",
+              "      <td>36540.053495</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>237</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>238</th>\n",
+              "      <td>10578.818067</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>239</th>\n",
+              "      <td>35168.259133</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>240</th>\n",
+              "      <td>93129.379939</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>241</th>\n",
+              "      <td>138.608707</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>242</th>\n",
+              "      <td>45447.554691</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>243</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>244</th>\n",
+              "      <td>9851.404864</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>245</th>\n",
+              "      <td>37626.631440</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>246</th>\n",
+              "      <td>106410.431840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>247</th>\n",
+              "      <td>121.275814</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>248</th>\n",
+              "      <td>54215.086072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>249</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>250</th>\n",
+              "      <td>10885.446997</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>251</th>\n",
+              "      <td>41115.707978</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>252</th>\n",
+              "      <td>113250.204885</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>96.756223</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>254</th>\n",
+              "      <td>61637.421018</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>255</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>256</th>\n",
+              "      <td>10755.233313</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>257</th>\n",
+              "      <td>40745.579720</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>258</th>\n",
+              "      <td>97212.595233</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>259</th>\n",
+              "      <td>122.046643</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>260</th>\n",
+              "      <td>48120.251061</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>262</th>\n",
+              "      <td>10762.040743</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>263</th>\n",
+              "      <td>38188.629523</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>264</th>\n",
+              "      <td>88099.615150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>265</th>\n",
+              "      <td>139.238087</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>266</th>\n",
+              "      <td>39290.772648</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>267</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>268</th>\n",
+              "      <td>10093.932182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>269</th>\n",
+              "      <td>38589.501733</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>270</th>\n",
+              "      <td>33220.211031</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271</th>\n",
+              "      <td>76664.176856</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>272</th>\n",
+              "      <td>126.333311</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>273</th>\n",
+              "      <td>34165.419021</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>274</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>275</th>\n",
+              "      <td>9168.802892</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>276</th>\n",
+              "      <td>138.673759</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>277</th>\n",
+              "      <td>45201.644434</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>278</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>279</th>\n",
+              "      <td>10603.057567</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>280</th>\n",
+              "      <td>35029.635627</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>281</th>\n",
+              "      <td>90956.713682</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>282</th>\n",
+              "      <td>88351.985948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>283</th>\n",
+              "      <td>138.755442</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>284</th>\n",
+              "      <td>46965.896670</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>285</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>286</th>\n",
+              "      <td>9059.596293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>287</th>\n",
+              "      <td>32228.713277</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>288</th>\n",
+              "      <td>105048.399588</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>289</th>\n",
+              "      <td>141.211836</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>56858.813973</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>291</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>292</th>\n",
+              "      <td>10237.348388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>293</th>\n",
+              "      <td>37844.137854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>294</th>\n",
+              "      <td>50668.506740</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>295</th>\n",
+              "      <td>9201.570428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>296</th>\n",
+              "      <td>33844.452584</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>297</th>\n",
+              "      <td>93777.092190</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>299</th>\n",
+              "      <td>126.965870</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>300</th>\n",
+              "      <td>86257.113954</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>301</th>\n",
+              "      <td>124.499220</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>40602.216180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>304</th>\n",
+              "      <td>10263.051175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>305</th>\n",
+              "      <td>35197.514477</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>306</th>\n",
+              "      <td>83789.471434</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307</th>\n",
+              "      <td>150.804270</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>308</th>\n",
+              "      <td>37522.106230</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>309</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>10576.906275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>311</th>\n",
+              "      <td>35568.929962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>312</th>\n",
+              "      <td>95740.193220</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>313</th>\n",
+              "      <td>126.129300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>314</th>\n",
+              "      <td>45403.121151</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>315</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>316</th>\n",
+              "      <td>10409.613988</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>317</th>\n",
+              "      <td>39780.957216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>318</th>\n",
+              "      <td>107067.690147</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>319</th>\n",
+              "      <td>157.661020</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>55039.193109</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>321</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>322</th>\n",
+              "      <td>11006.912939</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>323</th>\n",
+              "      <td>40883.790082</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>324</th>\n",
+              "      <td>106199.567037</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>325</th>\n",
+              "      <td>137.800280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>326</th>\n",
+              "      <td>55498.872171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>327</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>328</th>\n",
+              "      <td>11000.659021</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>329</th>\n",
+              "      <td>39548.688861</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>330</th>\n",
+              "      <td>98330.078887</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>331</th>\n",
+              "      <td>149.431380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>332</th>\n",
+              "      <td>49188.480945</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>333</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>334</th>\n",
+              "      <td>10143.179540</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>335</th>\n",
+              "      <td>38838.990161</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>336</th>\n",
+              "      <td>87371.282742</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>337</th>\n",
+              "      <td>115.207040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>338</th>\n",
+              "      <td>38417.165246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>339</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>10878.860928</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>341</th>\n",
+              "      <td>37912.302623</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>342</th>\n",
+              "      <td>76833.132338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>343</th>\n",
+              "      <td>141.916500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>344</th>\n",
+              "      <td>36928.673370</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>345</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>346</th>\n",
+              "      <td>9225.108138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>347</th>\n",
+              "      <td>30568.301595</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>348</th>\n",
+              "      <td>90862.097076</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>349</th>\n",
+              "      <td>110.738180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>350</th>\n",
+              "      <td>44286.597949</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>351</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>352</th>\n",
+              "      <td>10414.859381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>353</th>\n",
+              "      <td>36027.009965</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>354</th>\n",
+              "      <td>83865.267053</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>355</th>\n",
+              "      <td>158.829630</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>356</th>\n",
+              "      <td>43624.445521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>357</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>358</th>\n",
+              "      <td>8860.445138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>359</th>\n",
+              "      <td>31261.507655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>360</th>\n",
+              "      <td>110323.140461</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>361</th>\n",
+              "      <td>143.408580</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>362</th>\n",
+              "      <td>65257.820032</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>363</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>364</th>\n",
+              "      <td>9705.833789</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>365</th>\n",
+              "      <td>35221.435864</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>366</th>\n",
+              "      <td>125.229092</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>367</th>\n",
+              "      <td>34569.069704</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>368</th>\n",
+              "      <td>92758.665787</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>369</th>\n",
+              "      <td>49292.408534</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>370</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>371</th>\n",
+              "      <td>8805.902408</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>372</th>\n",
+              "      <td>78138.430487</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>373</th>\n",
+              "      <td>116.431542</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>374</th>\n",
+              "      <td>36017.753456</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>375</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>376</th>\n",
+              "      <td>8899.113168</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>377</th>\n",
+              "      <td>33073.573634</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>378</th>\n",
+              "      <td>78536.381274</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>379</th>\n",
+              "      <td>150.910517</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>380</th>\n",
+              "      <td>36137.001987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>381</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>382</th>\n",
+              "      <td>10119.579588</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>383</th>\n",
+              "      <td>32065.850209</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>384</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>385</th>\n",
+              "      <td>9407.685260</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>386</th>\n",
+              "      <td>34860.091052</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>387</th>\n",
+              "      <td>82116.175834</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>388</th>\n",
+              "      <td>95.013243</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>389</th>\n",
+              "      <td>37730.632633</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>390</th>\n",
+              "      <td>99931.728576</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>391</th>\n",
+              "      <td>144.439505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>392</th>\n",
+              "      <td>51264.112847</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>393</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>394</th>\n",
+              "      <td>10200.762626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>395</th>\n",
+              "      <td>38372.587358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>396</th>\n",
+              "      <td>107131.558529</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>397</th>\n",
+              "      <td>133.125505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>398</th>\n",
+              "      <td>57941.610640</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>399</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>400</th>\n",
+              "      <td>9380.111925</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>401</th>\n",
+              "      <td>39610.049338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>402</th>\n",
+              "      <td>89633.861617</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>403</th>\n",
+              "      <td>101.639433</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>404</th>\n",
+              "      <td>44587.065380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>405</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>406</th>\n",
+              "      <td>10097.093550</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>407</th>\n",
+              "      <td>34900.784928</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>408</th>\n",
+              "      <td>76862.068086</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>409</th>\n",
+              "      <td>111.825293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>410</th>\n",
+              "      <td>34694.361388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>411</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>412</th>\n",
+              "      <td>9784.320522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>32243.160154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>8657.008349</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>29672.171370</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>70675.522772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>418</th>\n",
+              "      <td>108.840282</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>419</th>\n",
+              "      <td>32204.491127</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>420</th>\n",
+              "      <td>84350.950560</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>421</th>\n",
+              "      <td>117.602107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>422</th>\n",
+              "      <td>40725.673358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>423</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>424</th>\n",
+              "      <td>9863.605853</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>425</th>\n",
+              "      <td>33611.138859</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>426</th>\n",
+              "      <td>75384.767249</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>427</th>\n",
+              "      <td>117.751159</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>428</th>\n",
+              "      <td>38149.948669</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>429</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>430</th>\n",
+              "      <td>8244.609072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>431</th>\n",
+              "      <td>28839.024832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>432</th>\n",
+              "      <td>88053.134511</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>433</th>\n",
+              "      <td>120.194441</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>434</th>\n",
+              "      <td>49140.658275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>435</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>436</th>\n",
+              "      <td>8305.169594</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>437</th>\n",
+              "      <td>30510.594179</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>438</th>\n",
+              "      <td>87394.969062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>439</th>\n",
+              "      <td>119.157566</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>440</th>\n",
+              "      <td>46666.659415</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>441</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>442</th>\n",
+              "      <td>8515.026840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>443</th>\n",
+              "      <td>32083.872849</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>444</th>\n",
+              "      <td>73138.875528</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>445</th>\n",
+              "      <td>90.798489</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>446</th>\n",
+              "      <td>34069.411796</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>447</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>448</th>\n",
+              "      <td>9048.176348</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>449</th>\n",
+              "      <td>29915.282594</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>450</th>\n",
+              "      <td>70959.539463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>451</th>\n",
+              "      <td>121.610524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>452</th>\n",
+              "      <td>31501.750726</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>453</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>454</th>\n",
+              "      <td>9450.537776</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>455</th>\n",
+              "      <td>29929.314748</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>456</th>\n",
+              "      <td>85616.120039</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>457</th>\n",
+              "      <td>116.288138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>458</th>\n",
+              "      <td>43474.564597</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>459</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>460</th>\n",
+              "      <td>9541.316413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>461</th>\n",
+              "      <td>32499.949241</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>462</th>\n",
+              "      <td>104435.117427</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>463</th>\n",
+              "      <td>120.229855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>464</th>\n",
+              "      <td>56301.776945</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>465</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>466</th>\n",
+              "      <td>10565.275890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>467</th>\n",
+              "      <td>37403.778672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>468</th>\n",
+              "      <td>104105.243494</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>469</th>\n",
+              "      <td>114.292150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>470</th>\n",
+              "      <td>55759.374067</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>471</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>472</th>\n",
+              "      <td>10136.183820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>473</th>\n",
+              "      <td>38034.545176</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>474</th>\n",
+              "      <td>88897.238875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>475</th>\n",
+              "      <td>136.529547</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>476</th>\n",
+              "      <td>44797.544819</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>477</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>478</th>\n",
+              "      <td>9792.207160</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>479</th>\n",
+              "      <td>34182.908041</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>480</th>\n",
+              "      <td>74506.102205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>481</th>\n",
+              "      <td>100.213671</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>482</th>\n",
+              "      <td>32957.029820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>483</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>484</th>\n",
+              "      <td>9890.600203</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>485</th>\n",
+              "      <td>31589.094571</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>486</th>\n",
+              "      <td>70924.343910</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>487</th>\n",
+              "      <td>119.455106</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>488</th>\n",
+              "      <td>31126.717016</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>489</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>490</th>\n",
+              "      <td>10234.102874</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>491</th>\n",
+              "      <td>29409.091191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>492</th>\n",
+              "      <td>29600.103525</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>493</th>\n",
+              "      <td>75740.701011</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494</th>\n",
+              "      <td>125.992554</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>36751.151975</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>496</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>497</th>\n",
+              "      <td>9240.862386</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>498</th>\n",
+              "      <td>88419.580940</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>117.717084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>500</th>\n",
+              "      <td>49881.603401</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>501</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>502</th>\n",
+              "      <td>8591.021891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>503</th>\n",
+              "      <td>29873.793737</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>504</th>\n",
+              "      <td>10681.304325</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>505</th>\n",
+              "      <td>135.640480</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>506</th>\n",
+              "      <td>49809.139858</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>507</th>\n",
+              "      <td>36330.803170</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>508</th>\n",
+              "      <td>96907.761556</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>509</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>510</th>\n",
+              "      <td>78159.502365</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>511</th>\n",
+              "      <td>121.817214</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>512</th>\n",
+              "      <td>38782.382618</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>513</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>514</th>\n",
+              "      <td>9243.318705</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>515</th>\n",
+              "      <td>30015.294481</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>516</th>\n",
+              "      <td>71571.111041</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>106.574192</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>518</th>\n",
+              "      <td>33651.466763</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>519</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>520</th>\n",
+              "      <td>9012.726619</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>521</th>\n",
+              "      <td>28828.734311</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>522</th>\n",
+              "      <td>73290.983539</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>523</th>\n",
+              "      <td>136.842420</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>524</th>\n",
+              "      <td>31577.091549</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>525</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>526</th>\n",
+              "      <td>10617.079545</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>527</th>\n",
+              "      <td>30995.230191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>528</th>\n",
+              "      <td>84508.994100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>529</th>\n",
+              "      <td>122.976885</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>530</th>\n",
+              "      <td>40915.420248</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>531</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>532</th>\n",
+              "      <td>10042.042693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>533</th>\n",
+              "      <td>33415.668963</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>534</th>\n",
+              "      <td>96827.494428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>535</th>\n",
+              "      <td>118.425376</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>536</th>\n",
+              "      <td>49580.859042</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>537</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>538</th>\n",
+              "      <td>10869.819602</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>539</th>\n",
+              "      <td>36249.693318</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>540</th>\n",
+              "      <td>100954.415605</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>541</th>\n",
+              "      <td>132.827994</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>542</th>\n",
+              "      <td>53225.492327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>543</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>544</th>\n",
+              "      <td>10804.773643</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>545</th>\n",
+              "      <td>36744.945428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>546</th>\n",
+              "      <td>47336.485180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>547</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>548</th>\n",
+              "      <td>10693.881446</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>549</th>\n",
+              "      <td>35815.411715</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>550</th>\n",
+              "      <td>93998.401182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>551</th>\n",
+              "      <td>133.789339</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>552</th>\n",
+              "      <td>80736.913873</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>553</th>\n",
+              "      <td>128.254669</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>554</th>\n",
+              "      <td>35953.379412</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>555</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>556</th>\n",
+              "      <td>10745.574244</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>557</th>\n",
+              "      <td>33863.134228</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>558</th>\n",
+              "      <td>68939.311409</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>559</th>\n",
+              "      <td>138.887058</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>560</th>\n",
+              "      <td>29654.000293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>561</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>562</th>\n",
+              "      <td>10442.754522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>563</th>\n",
+              "      <td>28741.172487</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>564</th>\n",
+              "      <td>89254.649326</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>565</th>\n",
+              "      <td>147.346368</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>566</th>\n",
+              "      <td>49011.284019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>567</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>568</th>\n",
+              "      <td>9106.383015</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>569</th>\n",
+              "      <td>31002.511819</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>570</th>\n",
+              "      <td>101688.676702</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>571</th>\n",
+              "      <td>150.424719</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>572</th>\n",
+              "      <td>55902.856190</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>573</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>574</th>\n",
+              "      <td>10446.516584</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>575</th>\n",
+              "      <td>35178.439150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>576</th>\n",
+              "      <td>102662.471174</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>577</th>\n",
+              "      <td>154.786625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>578</th>\n",
+              "      <td>56516.421275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>579</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>580</th>\n",
+              "      <td>9834.122627</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>581</th>\n",
+              "      <td>36123.265139</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>582</th>\n",
+              "      <td>91562.532363</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>583</th>\n",
+              "      <td>153.520220</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>584</th>\n",
+              "      <td>49767.289655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>585</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586</th>\n",
+              "      <td>9932.448194</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>587</th>\n",
+              "      <td>31654.706443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>588</th>\n",
+              "      <td>83941.221596</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>589</th>\n",
+              "      <td>134.953200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>590</th>\n",
+              "      <td>40177.090163</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>591</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>592</th>\n",
+              "      <td>10566.697638</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>593</th>\n",
+              "      <td>32997.698519</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>594</th>\n",
+              "      <td>74812.752952</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>133.619140</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>31437.018583</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>10699.327411</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>32542.132259</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>600</th>\n",
+              "      <td>84696.886134</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>601</th>\n",
+              "      <td>144.839100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>602</th>\n",
+              "      <td>40173.724055</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>603</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>604</th>\n",
+              "      <td>10809.434381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>605</th>\n",
+              "      <td>33574.128000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>606</th>\n",
+              "      <td>93178.929977</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>607</th>\n",
+              "      <td>147.125360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>608</th>\n",
+              "      <td>45983.318122</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>609</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>610</th>\n",
+              "      <td>10970.177238</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>36050.718034</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>612</th>\n",
+              "      <td>51751.661839</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>613</th>\n",
+              "      <td>10767.967321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>614</th>\n",
+              "      <td>36619.005108</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>615</th>\n",
+              "      <td>99299.821474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>616</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>617</th>\n",
+              "      <td>139.567380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>618</th>\n",
+              "      <td>88313.610172</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>619</th>\n",
+              "      <td>134.106780</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>620</th>\n",
+              "      <td>43200.163957</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>621</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>622</th>\n",
+              "      <td>10346.884681</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>623</th>\n",
+              "      <td>34640.937867</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>624</th>\n",
+              "      <td>74362.040748</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>625</th>\n",
+              "      <td>31510.621514</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>626</th>\n",
+              "      <td>10447.033893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>627</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>628</th>\n",
+              "      <td>32307.948333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>629</th>\n",
+              "      <td>132.084480</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>630</th>\n",
+              "      <td>68699.873669</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>631</th>\n",
+              "      <td>130.596300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>632</th>\n",
+              "      <td>32031.553620</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>633</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>634</th>\n",
+              "      <td>9142.607567</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>635</th>\n",
+              "      <td>27419.191694</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>636</th>\n",
+              "      <td>85821.742566</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>637</th>\n",
+              "      <td>136.414520</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>638</th>\n",
+              "      <td>44606.003593</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>639</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>640</th>\n",
+              "      <td>9706.974271</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>641</th>\n",
+              "      <td>31358.844126</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>642</th>\n",
+              "      <td>83888.043255</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>643</th>\n",
+              "      <td>133.338150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>644</th>\n",
+              "      <td>47905.872336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>645</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>646</th>\n",
+              "      <td>8591.698768</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>647</th>\n",
+              "      <td>27332.700711</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>33541.876744</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>649</th>\n",
+              "      <td>99902.783837</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>650</th>\n",
+              "      <td>143.969870</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>651</th>\n",
+              "      <td>56166.086977</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>652</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>653</th>\n",
+              "      <td>9993.894197</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>654</th>\n",
+              "      <td>86934.595362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>655</th>\n",
+              "      <td>135.984840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>656</th>\n",
+              "      <td>47562.782896</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>657</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>658</th>\n",
+              "      <td>8827.395776</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>659</th>\n",
+              "      <td>30419.082757</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>660</th>\n",
+              "      <td>79072.364481</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>661</th>\n",
+              "      <td>122.686220</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>662</th>\n",
+              "      <td>37973.529812</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>663</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>664</th>\n",
+              "      <td>9653.807510</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>665</th>\n",
+              "      <td>31381.329547</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>666</th>\n",
+              "      <td>74319.792635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>667</th>\n",
+              "      <td>133.456500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>668</th>\n",
+              "      <td>31612.664694</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>669</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>670</th>\n",
+              "      <td>10058.802196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>671</th>\n",
+              "      <td>32486.418389</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>672</th>\n",
+              "      <td>80226.489336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>673</th>\n",
+              "      <td>131.218080</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>674</th>\n",
+              "      <td>38128.122350</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>675</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>676</th>\n",
+              "      <td>9472.653064</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>677</th>\n",
+              "      <td>32500.297046</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>678</th>\n",
+              "      <td>91692.596673</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>679</th>\n",
+              "      <td>134.330350</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>680</th>\n",
+              "      <td>46339.352346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>681</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>682</th>\n",
+              "      <td>10011.292353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>683</th>\n",
+              "      <td>35177.160235</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>684</th>\n",
+              "      <td>99600.687484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>685</th>\n",
+              "      <td>131.728900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>686</th>\n",
+              "      <td>51893.720378</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>687</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>688</th>\n",
+              "      <td>10566.894597</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>689</th>\n",
+              "      <td>37026.015300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>690</th>\n",
+              "      <td>85630.897292</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>130.636350</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>692</th>\n",
+              "      <td>41762.993217</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>693</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>694</th>\n",
+              "      <td>9838.908947</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>695</th>\n",
+              "      <td>33900.353310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>696</th>\n",
+              "      <td>72702.164744</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>697</th>\n",
+              "      <td>127.941960</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>698</th>\n",
+              "      <td>31275.699341</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>699</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>700</th>\n",
+              "      <td>9741.349933</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>701</th>\n",
+              "      <td>31533.762722</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>702</th>\n",
+              "      <td>68623.337379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>703</th>\n",
+              "      <td>142.344180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>704</th>\n",
+              "      <td>32920.557899</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>705</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>706</th>\n",
+              "      <td>8331.238635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>707</th>\n",
+              "      <td>27218.311388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>708</th>\n",
+              "      <td>83364.878147</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>709</th>\n",
+              "      <td>113.363900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>710</th>\n",
+              "      <td>43305.017443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>711</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>712</th>\n",
+              "      <td>9222.622670</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>713</th>\n",
+              "      <td>30724.843027</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>714</th>\n",
+              "      <td>81485.299675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>715</th>\n",
+              "      <td>28927.063915</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>716</th>\n",
+              "      <td>9078.561930</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>717</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>718</th>\n",
+              "      <td>43356.352307</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>719</th>\n",
+              "      <td>134.198660</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>720</th>\n",
+              "      <td>86139.708963</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>721</th>\n",
+              "      <td>30757.398628</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>722</th>\n",
+              "      <td>8825.240673</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>723</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>724</th>\n",
+              "      <td>46422.389690</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>725</th>\n",
+              "      <td>151.800760</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.plot.scatter(x='price',y='sales')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "UZBn8oNIF-sA",
+        "outputId": "487ff432-2913-4caf-b06a-8a32dfd9cce0"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='price', ylabel='sales'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sectors = ['residential','transportation','commercial','industrial']\n",
+        "for sec in sectors:\n",
+        "    df.loc[df['sectorName']==sec,:].plot.scatter(x='price',y='sales',title=sec)\n",
+        "    print( df.loc[df['sectorName']==sec,['price','sales']].corr() )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "GsOeRcwlGADt",
+        "outputId": "e9d4afd9-9595-4fa0-a3db-74404ed2bdb6"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "          price     sales\n",
+            "price  1.000000 -0.203288\n",
+            "sales -0.203288  1.000000\n",
+            "          price     sales\n",
+            "price  1.000000 -0.641344\n",
+            "sales -0.641344  1.000000\n",
+            "          price     sales\n",
+            "price  1.000000  0.540155\n",
+            "sales  0.540155  1.000000\n",
+            "          price     sales\n",
+            "price  1.000000 -0.208886\n",
+            "sales -0.208886  1.000000\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['period']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xKFWbkfjGCau",
+        "outputId": "490c558b-ddcf-40a9-dc35-b5bb1ab3c766"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0      2023-01\n",
+              "1      2023-01\n",
+              "2      2023-01\n",
+              "3      2023-01\n",
+              "4      2023-01\n",
+              "5      2023-01\n",
+              "6      2022-12\n",
+              "7      2022-12\n",
+              "8      2022-12\n",
+              "9      2022-12\n",
+              "10     2022-12\n",
+              "11     2022-12\n",
+              "12     2022-11\n",
+              "13     2022-11\n",
+              "14     2022-11\n",
+              "15     2022-11\n",
+              "16     2022-11\n",
+              "17     2022-11\n",
+              "18     2022-10\n",
+              "19     2022-10\n",
+              "20     2022-10\n",
+              "21     2022-10\n",
+              "22     2022-10\n",
+              "23     2022-10\n",
+              "24     2022-09\n",
+              "25     2022-09\n",
+              "26     2022-09\n",
+              "27     2022-09\n",
+              "28     2022-09\n",
+              "29     2022-09\n",
+              "30     2022-08\n",
+              "31     2022-08\n",
+              "32     2022-08\n",
+              "33     2022-08\n",
+              "34     2022-08\n",
+              "35     2022-08\n",
+              "36     2022-07\n",
+              "37     2022-07\n",
+              "38     2022-07\n",
+              "39     2022-07\n",
+              "40     2022-07\n",
+              "41     2022-07\n",
+              "42     2022-06\n",
+              "43     2022-06\n",
+              "44     2022-06\n",
+              "45     2022-06\n",
+              "46     2022-06\n",
+              "47     2022-06\n",
+              "48     2022-05\n",
+              "49     2022-05\n",
+              "50     2022-05\n",
+              "51     2022-05\n",
+              "52     2022-05\n",
+              "53     2022-05\n",
+              "54     2022-04\n",
+              "55     2022-04\n",
+              "56     2022-04\n",
+              "57     2022-04\n",
+              "58     2022-04\n",
+              "59     2022-04\n",
+              "60     2022-03\n",
+              "61     2022-03\n",
+              "62     2022-03\n",
+              "63     2022-03\n",
+              "64     2022-03\n",
+              "65     2022-03\n",
+              "66     2022-02\n",
+              "67     2022-02\n",
+              "68     2022-02\n",
+              "69     2022-02\n",
+              "70     2022-02\n",
+              "71     2022-02\n",
+              "72     2022-01\n",
+              "73     2022-01\n",
+              "74     2022-01\n",
+              "75     2022-01\n",
+              "76     2022-01\n",
+              "77     2022-01\n",
+              "78     2021-12\n",
+              "79     2021-12\n",
+              "80     2021-12\n",
+              "81     2021-12\n",
+              "82     2021-12\n",
+              "83     2021-12\n",
+              "84     2021-11\n",
+              "85     2021-11\n",
+              "86     2021-11\n",
+              "87     2021-11\n",
+              "88     2021-11\n",
+              "89     2021-11\n",
+              "90     2021-10\n",
+              "91     2021-10\n",
+              "92     2021-10\n",
+              "93     2021-10\n",
+              "94     2021-10\n",
+              "95     2021-10\n",
+              "96     2021-09\n",
+              "97     2021-09\n",
+              "98     2021-09\n",
+              "99     2021-09\n",
+              "100    2021-09\n",
+              "101    2021-09\n",
+              "102    2021-08\n",
+              "103    2021-08\n",
+              "104    2021-08\n",
+              "105    2021-08\n",
+              "106    2021-08\n",
+              "107    2021-08\n",
+              "108    2021-07\n",
+              "109    2021-07\n",
+              "110    2021-07\n",
+              "111    2021-07\n",
+              "112    2021-07\n",
+              "113    2021-07\n",
+              "114    2021-06\n",
+              "115    2021-06\n",
+              "116    2021-06\n",
+              "117    2021-06\n",
+              "118    2021-06\n",
+              "119    2021-06\n",
+              "120    2021-05\n",
+              "121    2021-05\n",
+              "122    2021-05\n",
+              "123    2021-05\n",
+              "124    2021-05\n",
+              "125    2021-05\n",
+              "126    2021-04\n",
+              "127    2021-04\n",
+              "128    2021-04\n",
+              "129    2021-04\n",
+              "130    2021-04\n",
+              "131    2021-04\n",
+              "132    2021-03\n",
+              "133    2021-03\n",
+              "134    2021-03\n",
+              "135    2021-03\n",
+              "136    2021-03\n",
+              "137    2021-03\n",
+              "138    2021-02\n",
+              "139    2021-02\n",
+              "140    2021-02\n",
+              "141    2021-02\n",
+              "142    2021-02\n",
+              "143    2021-02\n",
+              "144    2021-01\n",
+              "145    2021-01\n",
+              "146    2021-01\n",
+              "147    2021-01\n",
+              "148    2021-01\n",
+              "149    2021-01\n",
+              "150    2020-12\n",
+              "151    2020-12\n",
+              "152    2020-12\n",
+              "153    2020-12\n",
+              "154    2020-12\n",
+              "155    2020-12\n",
+              "156    2020-11\n",
+              "157    2020-11\n",
+              "158    2020-11\n",
+              "159    2020-11\n",
+              "160    2020-11\n",
+              "161    2020-11\n",
+              "162    2020-10\n",
+              "163    2020-10\n",
+              "164    2020-10\n",
+              "165    2020-10\n",
+              "166    2020-10\n",
+              "167    2020-10\n",
+              "168    2020-09\n",
+              "169    2020-09\n",
+              "170    2020-09\n",
+              "171    2020-09\n",
+              "172    2020-09\n",
+              "173    2020-09\n",
+              "174    2020-08\n",
+              "175    2020-08\n",
+              "176    2020-08\n",
+              "177    2020-08\n",
+              "178    2020-08\n",
+              "179    2020-08\n",
+              "180    2020-07\n",
+              "181    2020-07\n",
+              "182    2020-07\n",
+              "183    2020-07\n",
+              "184    2020-07\n",
+              "185    2020-07\n",
+              "186    2020-06\n",
+              "187    2020-06\n",
+              "188    2020-06\n",
+              "189    2020-06\n",
+              "190    2020-06\n",
+              "191    2020-06\n",
+              "192    2020-05\n",
+              "193    2020-05\n",
+              "194    2020-05\n",
+              "195    2020-05\n",
+              "196    2020-05\n",
+              "197    2020-05\n",
+              "198    2020-04\n",
+              "199    2020-04\n",
+              "200    2020-04\n",
+              "201    2020-04\n",
+              "202    2020-04\n",
+              "203    2020-04\n",
+              "204    2020-03\n",
+              "205    2020-03\n",
+              "206    2020-03\n",
+              "207    2020-03\n",
+              "208    2020-03\n",
+              "209    2020-03\n",
+              "210    2020-02\n",
+              "211    2020-02\n",
+              "212    2020-02\n",
+              "213    2020-02\n",
+              "214    2020-02\n",
+              "215    2020-02\n",
+              "216    2020-01\n",
+              "217    2020-01\n",
+              "218    2020-01\n",
+              "219    2020-01\n",
+              "220    2020-01\n",
+              "221    2020-01\n",
+              "222    2019-12\n",
+              "223    2019-12\n",
+              "224    2019-12\n",
+              "225    2019-12\n",
+              "226    2019-12\n",
+              "227    2019-12\n",
+              "228    2019-11\n",
+              "229    2019-11\n",
+              "230    2019-11\n",
+              "231    2019-11\n",
+              "232    2019-11\n",
+              "233    2019-11\n",
+              "234    2019-10\n",
+              "235    2019-10\n",
+              "236    2019-10\n",
+              "237    2019-10\n",
+              "238    2019-10\n",
+              "239    2019-10\n",
+              "240    2019-09\n",
+              "241    2019-09\n",
+              "242    2019-09\n",
+              "243    2019-09\n",
+              "244    2019-09\n",
+              "245    2019-09\n",
+              "246    2019-08\n",
+              "247    2019-08\n",
+              "248    2019-08\n",
+              "249    2019-08\n",
+              "250    2019-08\n",
+              "251    2019-08\n",
+              "252    2019-07\n",
+              "253    2019-07\n",
+              "254    2019-07\n",
+              "255    2019-07\n",
+              "256    2019-07\n",
+              "257    2019-07\n",
+              "258    2019-06\n",
+              "259    2019-06\n",
+              "260    2019-06\n",
+              "261    2019-06\n",
+              "262    2019-06\n",
+              "263    2019-06\n",
+              "264    2019-05\n",
+              "265    2019-05\n",
+              "266    2019-05\n",
+              "267    2019-05\n",
+              "268    2019-05\n",
+              "269    2019-05\n",
+              "270    2019-04\n",
+              "271    2019-04\n",
+              "272    2019-04\n",
+              "273    2019-04\n",
+              "274    2019-04\n",
+              "275    2019-04\n",
+              "276    2019-03\n",
+              "277    2019-03\n",
+              "278    2019-03\n",
+              "279    2019-03\n",
+              "280    2019-03\n",
+              "281    2019-03\n",
+              "282    2019-02\n",
+              "283    2019-02\n",
+              "284    2019-02\n",
+              "285    2019-02\n",
+              "286    2019-02\n",
+              "287    2019-02\n",
+              "288    2019-01\n",
+              "289    2019-01\n",
+              "290    2019-01\n",
+              "291    2019-01\n",
+              "292    2019-01\n",
+              "293    2019-01\n",
+              "294    2018-12\n",
+              "295    2018-12\n",
+              "296    2018-12\n",
+              "297    2018-12\n",
+              "298    2018-12\n",
+              "299    2018-12\n",
+              "300    2018-11\n",
+              "301    2018-11\n",
+              "302    2018-11\n",
+              "303    2018-11\n",
+              "304    2018-11\n",
+              "305    2018-11\n",
+              "306    2018-10\n",
+              "307    2018-10\n",
+              "308    2018-10\n",
+              "309    2018-10\n",
+              "310    2018-10\n",
+              "311    2018-10\n",
+              "312    2018-09\n",
+              "313    2018-09\n",
+              "314    2018-09\n",
+              "315    2018-09\n",
+              "316    2018-09\n",
+              "317    2018-09\n",
+              "318    2018-08\n",
+              "319    2018-08\n",
+              "320    2018-08\n",
+              "321    2018-08\n",
+              "322    2018-08\n",
+              "323    2018-08\n",
+              "324    2018-07\n",
+              "325    2018-07\n",
+              "326    2018-07\n",
+              "327    2018-07\n",
+              "328    2018-07\n",
+              "329    2018-07\n",
+              "330    2018-06\n",
+              "331    2018-06\n",
+              "332    2018-06\n",
+              "333    2018-06\n",
+              "334    2018-06\n",
+              "335    2018-06\n",
+              "336    2018-05\n",
+              "337    2018-05\n",
+              "338    2018-05\n",
+              "339    2018-05\n",
+              "340    2018-05\n",
+              "341    2018-05\n",
+              "342    2018-04\n",
+              "343    2018-04\n",
+              "344    2018-04\n",
+              "345    2018-04\n",
+              "346    2018-04\n",
+              "347    2018-04\n",
+              "348    2018-03\n",
+              "349    2018-03\n",
+              "350    2018-03\n",
+              "351    2018-03\n",
+              "352    2018-03\n",
+              "353    2018-03\n",
+              "354    2018-02\n",
+              "355    2018-02\n",
+              "356    2018-02\n",
+              "357    2018-02\n",
+              "358    2018-02\n",
+              "359    2018-02\n",
+              "360    2018-01\n",
+              "361    2018-01\n",
+              "362    2018-01\n",
+              "363    2018-01\n",
+              "364    2018-01\n",
+              "365    2018-01\n",
+              "366    2017-12\n",
+              "367    2017-12\n",
+              "368    2017-12\n",
+              "369    2017-12\n",
+              "370    2017-12\n",
+              "371    2017-12\n",
+              "372    2017-11\n",
+              "373    2017-11\n",
+              "374    2017-11\n",
+              "375    2017-11\n",
+              "376    2017-11\n",
+              "377    2017-11\n",
+              "378    2017-10\n",
+              "379    2017-10\n",
+              "380    2017-10\n",
+              "381    2017-10\n",
+              "382    2017-10\n",
+              "383    2017-10\n",
+              "384    2017-09\n",
+              "385    2017-09\n",
+              "386    2017-09\n",
+              "387    2017-09\n",
+              "388    2017-09\n",
+              "389    2017-09\n",
+              "390    2017-08\n",
+              "391    2017-08\n",
+              "392    2017-08\n",
+              "393    2017-08\n",
+              "394    2017-08\n",
+              "395    2017-08\n",
+              "396    2017-07\n",
+              "397    2017-07\n",
+              "398    2017-07\n",
+              "399    2017-07\n",
+              "400    2017-07\n",
+              "401    2017-07\n",
+              "402    2017-06\n",
+              "403    2017-06\n",
+              "404    2017-06\n",
+              "405    2017-06\n",
+              "406    2017-06\n",
+              "407    2017-06\n",
+              "408    2017-05\n",
+              "409    2017-05\n",
+              "410    2017-05\n",
+              "411    2017-05\n",
+              "412    2017-05\n",
+              "413    2017-05\n",
+              "414    2017-04\n",
+              "415    2017-04\n",
+              "416    2017-04\n",
+              "417    2017-04\n",
+              "418    2017-04\n",
+              "419    2017-04\n",
+              "420    2017-03\n",
+              "421    2017-03\n",
+              "422    2017-03\n",
+              "423    2017-03\n",
+              "424    2017-03\n",
+              "425    2017-03\n",
+              "426    2017-02\n",
+              "427    2017-02\n",
+              "428    2017-02\n",
+              "429    2017-02\n",
+              "430    2017-02\n",
+              "431    2017-02\n",
+              "432    2017-01\n",
+              "433    2017-01\n",
+              "434    2017-01\n",
+              "435    2017-01\n",
+              "436    2017-01\n",
+              "437    2017-01\n",
+              "438    2016-12\n",
+              "439    2016-12\n",
+              "440    2016-12\n",
+              "441    2016-12\n",
+              "442    2016-12\n",
+              "443    2016-12\n",
+              "444    2016-11\n",
+              "445    2016-11\n",
+              "446    2016-11\n",
+              "447    2016-11\n",
+              "448    2016-11\n",
+              "449    2016-11\n",
+              "450    2016-10\n",
+              "451    2016-10\n",
+              "452    2016-10\n",
+              "453    2016-10\n",
+              "454    2016-10\n",
+              "455    2016-10\n",
+              "456    2016-09\n",
+              "457    2016-09\n",
+              "458    2016-09\n",
+              "459    2016-09\n",
+              "460    2016-09\n",
+              "461    2016-09\n",
+              "462    2016-08\n",
+              "463    2016-08\n",
+              "464    2016-08\n",
+              "465    2016-08\n",
+              "466    2016-08\n",
+              "467    2016-08\n",
+              "468    2016-07\n",
+              "469    2016-07\n",
+              "470    2016-07\n",
+              "471    2016-07\n",
+              "472    2016-07\n",
+              "473    2016-07\n",
+              "474    2016-06\n",
+              "475    2016-06\n",
+              "476    2016-06\n",
+              "477    2016-06\n",
+              "478    2016-06\n",
+              "479    2016-06\n",
+              "480    2016-05\n",
+              "481    2016-05\n",
+              "482    2016-05\n",
+              "483    2016-05\n",
+              "484    2016-05\n",
+              "485    2016-05\n",
+              "486    2016-04\n",
+              "487    2016-04\n",
+              "488    2016-04\n",
+              "489    2016-04\n",
+              "490    2016-04\n",
+              "491    2016-04\n",
+              "492    2016-03\n",
+              "493    2016-03\n",
+              "494    2016-03\n",
+              "495    2016-03\n",
+              "496    2016-03\n",
+              "497    2016-03\n",
+              "498    2016-02\n",
+              "499    2016-02\n",
+              "500    2016-02\n",
+              "501    2016-02\n",
+              "502    2016-02\n",
+              "503    2016-02\n",
+              "504    2016-01\n",
+              "505    2016-01\n",
+              "506    2016-01\n",
+              "507    2016-01\n",
+              "508    2016-01\n",
+              "509    2016-01\n",
+              "510    2015-12\n",
+              "511    2015-12\n",
+              "512    2015-12\n",
+              "513    2015-12\n",
+              "514    2015-12\n",
+              "515    2015-12\n",
+              "516    2015-11\n",
+              "517    2015-11\n",
+              "518    2015-11\n",
+              "519    2015-11\n",
+              "520    2015-11\n",
+              "521    2015-11\n",
+              "522    2015-10\n",
+              "523    2015-10\n",
+              "524    2015-10\n",
+              "525    2015-10\n",
+              "526    2015-10\n",
+              "527    2015-10\n",
+              "528    2015-09\n",
+              "529    2015-09\n",
+              "530    2015-09\n",
+              "531    2015-09\n",
+              "532    2015-09\n",
+              "533    2015-09\n",
+              "534    2015-08\n",
+              "535    2015-08\n",
+              "536    2015-08\n",
+              "537    2015-08\n",
+              "538    2015-08\n",
+              "539    2015-08\n",
+              "540    2015-07\n",
+              "541    2015-07\n",
+              "542    2015-07\n",
+              "543    2015-07\n",
+              "544    2015-07\n",
+              "545    2015-07\n",
+              "546    2015-06\n",
+              "547    2015-06\n",
+              "548    2015-06\n",
+              "549    2015-06\n",
+              "550    2015-06\n",
+              "551    2015-06\n",
+              "552    2015-05\n",
+              "553    2015-05\n",
+              "554    2015-05\n",
+              "555    2015-05\n",
+              "556    2015-05\n",
+              "557    2015-05\n",
+              "558    2015-04\n",
+              "559    2015-04\n",
+              "560    2015-04\n",
+              "561    2015-04\n",
+              "562    2015-04\n",
+              "563    2015-04\n",
+              "564    2015-03\n",
+              "565    2015-03\n",
+              "566    2015-03\n",
+              "567    2015-03\n",
+              "568    2015-03\n",
+              "569    2015-03\n",
+              "570    2015-02\n",
+              "571    2015-02\n",
+              "572    2015-02\n",
+              "573    2015-02\n",
+              "574    2015-02\n",
+              "575    2015-02\n",
+              "576    2015-01\n",
+              "577    2015-01\n",
+              "578    2015-01\n",
+              "579    2015-01\n",
+              "580    2015-01\n",
+              "581    2015-01\n",
+              "582    2014-12\n",
+              "583    2014-12\n",
+              "584    2014-12\n",
+              "585    2014-12\n",
+              "586    2014-12\n",
+              "587    2014-12\n",
+              "588    2014-11\n",
+              "589    2014-11\n",
+              "590    2014-11\n",
+              "591    2014-11\n",
+              "592    2014-11\n",
+              "593    2014-11\n",
+              "594    2014-10\n",
+              "595    2014-10\n",
+              "596    2014-10\n",
+              "597    2014-10\n",
+              "598    2014-10\n",
+              "599    2014-10\n",
+              "600    2014-09\n",
+              "601    2014-09\n",
+              "602    2014-09\n",
+              "603    2014-09\n",
+              "604    2014-09\n",
+              "605    2014-09\n",
+              "606    2014-08\n",
+              "607    2014-08\n",
+              "608    2014-08\n",
+              "609    2014-08\n",
+              "610    2014-08\n",
+              "611    2014-08\n",
+              "612    2014-07\n",
+              "613    2014-07\n",
+              "614    2014-07\n",
+              "615    2014-07\n",
+              "616    2014-07\n",
+              "617    2014-07\n",
+              "618    2014-06\n",
+              "619    2014-06\n",
+              "620    2014-06\n",
+              "621    2014-06\n",
+              "622    2014-06\n",
+              "623    2014-06\n",
+              "624    2014-05\n",
+              "625    2014-05\n",
+              "626    2014-05\n",
+              "627    2014-05\n",
+              "628    2014-05\n",
+              "629    2014-05\n",
+              "630    2014-04\n",
+              "631    2014-04\n",
+              "632    2014-04\n",
+              "633    2014-04\n",
+              "634    2014-04\n",
+              "635    2014-04\n",
+              "636    2014-03\n",
+              "637    2014-03\n",
+              "638    2014-03\n",
+              "639    2014-03\n",
+              "640    2014-03\n",
+              "641    2014-03\n",
+              "642    2014-02\n",
+              "643    2014-02\n",
+              "644    2014-02\n",
+              "645    2014-02\n",
+              "646    2014-02\n",
+              "647    2014-02\n",
+              "648    2014-01\n",
+              "649    2014-01\n",
+              "650    2014-01\n",
+              "651    2014-01\n",
+              "652    2014-01\n",
+              "653    2014-01\n",
+              "654    2013-12\n",
+              "655    2013-12\n",
+              "656    2013-12\n",
+              "657    2013-12\n",
+              "658    2013-12\n",
+              "659    2013-12\n",
+              "660    2013-11\n",
+              "661    2013-11\n",
+              "662    2013-11\n",
+              "663    2013-11\n",
+              "664    2013-11\n",
+              "665    2013-11\n",
+              "666    2013-10\n",
+              "667    2013-10\n",
+              "668    2013-10\n",
+              "669    2013-10\n",
+              "670    2013-10\n",
+              "671    2013-10\n",
+              "672    2013-09\n",
+              "673    2013-09\n",
+              "674    2013-09\n",
+              "675    2013-09\n",
+              "676    2013-09\n",
+              "677    2013-09\n",
+              "678    2013-08\n",
+              "679    2013-08\n",
+              "680    2013-08\n",
+              "681    2013-08\n",
+              "682    2013-08\n",
+              "683    2013-08\n",
+              "684    2013-07\n",
+              "685    2013-07\n",
+              "686    2013-07\n",
+              "687    2013-07\n",
+              "688    2013-07\n",
+              "689    2013-07\n",
+              "690    2013-06\n",
+              "691    2013-06\n",
+              "692    2013-06\n",
+              "693    2013-06\n",
+              "694    2013-06\n",
+              "695    2013-06\n",
+              "696    2013-05\n",
+              "697    2013-05\n",
+              "698    2013-05\n",
+              "699    2013-05\n",
+              "700    2013-05\n",
+              "701    2013-05\n",
+              "702    2013-04\n",
+              "703    2013-04\n",
+              "704    2013-04\n",
+              "705    2013-04\n",
+              "706    2013-04\n",
+              "707    2013-04\n",
+              "708    2013-03\n",
+              "709    2013-03\n",
+              "710    2013-03\n",
+              "711    2013-03\n",
+              "712    2013-03\n",
+              "713    2013-03\n",
+              "714    2013-02\n",
+              "715    2013-02\n",
+              "716    2013-02\n",
+              "717    2013-02\n",
+              "718    2013-02\n",
+              "719    2013-02\n",
+              "720    2013-01\n",
+              "721    2013-01\n",
+              "722    2013-01\n",
+              "723    2013-01\n",
+              "724    2013-01\n",
+              "725    2013-01\n",
+              "Name: period, dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>period</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2023-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>2022-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>2022-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>2022-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>2022-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>2022-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>2022-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>2022-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>2022-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>54</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>55</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>56</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>57</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>58</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>59</th>\n",
+              "      <td>2022-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>60</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>62</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>63</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>64</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>65</th>\n",
+              "      <td>2022-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>66</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>67</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>68</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>69</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>71</th>\n",
+              "      <td>2022-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>72</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>73</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>74</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>76</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>77</th>\n",
+              "      <td>2022-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>78</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>79</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>80</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>82</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>83</th>\n",
+              "      <td>2021-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>84</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>85</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>86</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>88</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>2021-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>90</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>91</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>92</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>93</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>94</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>2021-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>101</th>\n",
+              "      <td>2021-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>102</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>104</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>105</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>106</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>107</th>\n",
+              "      <td>2021-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>108</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>109</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>110</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>111</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>112</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>113</th>\n",
+              "      <td>2021-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>114</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>115</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>116</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>117</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>118</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>119</th>\n",
+              "      <td>2021-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>120</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>121</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>122</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>123</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>124</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>125</th>\n",
+              "      <td>2021-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>126</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>128</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>129</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>131</th>\n",
+              "      <td>2021-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>132</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>133</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>134</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>136</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>137</th>\n",
+              "      <td>2021-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>138</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>139</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>140</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>141</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>142</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>143</th>\n",
+              "      <td>2021-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>144</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>2021-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>150</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>151</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>152</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>153</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>154</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>155</th>\n",
+              "      <td>2020-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>156</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>157</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>158</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>159</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>160</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>161</th>\n",
+              "      <td>2020-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>162</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>164</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>165</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>166</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>167</th>\n",
+              "      <td>2020-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>168</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>169</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>170</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>171</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>172</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>173</th>\n",
+              "      <td>2020-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>174</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>175</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>177</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>178</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>179</th>\n",
+              "      <td>2020-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>180</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>181</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>182</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>183</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>184</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>185</th>\n",
+              "      <td>2020-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>186</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>187</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>188</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>189</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>190</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>191</th>\n",
+              "      <td>2020-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>192</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>194</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>195</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>196</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>2020-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>198</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>199</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>200</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>201</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>202</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>203</th>\n",
+              "      <td>2020-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>204</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>2020-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>210</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>211</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>212</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>213</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>214</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>215</th>\n",
+              "      <td>2020-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>216</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>217</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>218</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>220</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>221</th>\n",
+              "      <td>2020-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>222</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>223</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>224</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>226</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>227</th>\n",
+              "      <td>2019-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>228</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>229</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>230</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>231</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>232</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>233</th>\n",
+              "      <td>2019-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>234</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>235</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>236</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>237</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>238</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>239</th>\n",
+              "      <td>2019-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>240</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>241</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>242</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>243</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>244</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>245</th>\n",
+              "      <td>2019-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>246</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>247</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>248</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>249</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>250</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>251</th>\n",
+              "      <td>2019-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>252</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>254</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>255</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>256</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>257</th>\n",
+              "      <td>2019-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>258</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>259</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>260</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>262</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>263</th>\n",
+              "      <td>2019-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>264</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>265</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>266</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>267</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>268</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>269</th>\n",
+              "      <td>2019-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>270</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>272</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>273</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>274</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>275</th>\n",
+              "      <td>2019-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>276</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>277</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>278</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>279</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>280</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>281</th>\n",
+              "      <td>2019-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>282</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>283</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>284</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>285</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>286</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>287</th>\n",
+              "      <td>2019-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>288</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>289</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>291</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>292</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>293</th>\n",
+              "      <td>2019-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>294</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>295</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>296</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>297</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>299</th>\n",
+              "      <td>2018-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>300</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>301</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>304</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>305</th>\n",
+              "      <td>2018-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>306</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>308</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>309</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>311</th>\n",
+              "      <td>2018-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>312</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>313</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>314</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>315</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>316</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>317</th>\n",
+              "      <td>2018-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>318</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>319</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>321</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>322</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>323</th>\n",
+              "      <td>2018-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>324</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>325</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>326</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>327</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>328</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>329</th>\n",
+              "      <td>2018-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>330</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>331</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>332</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>333</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>334</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>335</th>\n",
+              "      <td>2018-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>336</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>337</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>338</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>339</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>341</th>\n",
+              "      <td>2018-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>342</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>343</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>344</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>345</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>346</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>347</th>\n",
+              "      <td>2018-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>348</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>349</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>350</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>351</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>352</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>353</th>\n",
+              "      <td>2018-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>354</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>355</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>356</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>357</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>358</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>359</th>\n",
+              "      <td>2018-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>360</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>361</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>362</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>363</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>364</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>365</th>\n",
+              "      <td>2018-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>366</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>367</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>368</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>369</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>370</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>371</th>\n",
+              "      <td>2017-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>372</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>373</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>374</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>375</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>376</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>377</th>\n",
+              "      <td>2017-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>378</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>379</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>380</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>381</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>382</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>383</th>\n",
+              "      <td>2017-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>384</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>385</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>386</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>387</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>388</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>389</th>\n",
+              "      <td>2017-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>390</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>391</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>392</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>393</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>394</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>395</th>\n",
+              "      <td>2017-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>396</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>397</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>398</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>399</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>400</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>401</th>\n",
+              "      <td>2017-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>402</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>403</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>404</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>405</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>406</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>407</th>\n",
+              "      <td>2017-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>408</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>409</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>410</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>411</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>412</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>2017-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>418</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>419</th>\n",
+              "      <td>2017-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>420</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>421</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>422</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>423</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>424</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>425</th>\n",
+              "      <td>2017-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>426</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>427</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>428</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>429</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>430</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>431</th>\n",
+              "      <td>2017-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>432</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>433</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>434</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>435</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>436</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>437</th>\n",
+              "      <td>2017-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>438</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>439</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>440</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>441</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>442</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>443</th>\n",
+              "      <td>2016-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>444</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>445</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>446</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>447</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>448</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>449</th>\n",
+              "      <td>2016-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>450</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>451</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>452</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>453</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>454</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>455</th>\n",
+              "      <td>2016-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>456</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>457</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>458</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>459</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>460</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>461</th>\n",
+              "      <td>2016-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>462</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>463</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>464</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>465</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>466</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>467</th>\n",
+              "      <td>2016-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>468</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>469</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>470</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>471</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>472</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>473</th>\n",
+              "      <td>2016-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>474</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>475</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>476</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>477</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>478</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>479</th>\n",
+              "      <td>2016-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>480</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>481</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>482</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>483</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>484</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>485</th>\n",
+              "      <td>2016-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>486</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>487</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>488</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>489</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>490</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>491</th>\n",
+              "      <td>2016-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>492</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>493</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>496</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>497</th>\n",
+              "      <td>2016-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>498</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>500</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>501</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>502</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>503</th>\n",
+              "      <td>2016-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>504</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>505</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>506</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>507</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>508</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>509</th>\n",
+              "      <td>2016-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>510</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>511</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>512</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>513</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>514</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>515</th>\n",
+              "      <td>2015-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>516</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>518</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>519</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>520</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>521</th>\n",
+              "      <td>2015-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>522</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>523</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>524</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>525</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>526</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>527</th>\n",
+              "      <td>2015-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>528</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>529</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>530</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>531</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>532</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>533</th>\n",
+              "      <td>2015-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>534</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>535</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>536</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>537</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>538</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>539</th>\n",
+              "      <td>2015-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>540</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>541</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>542</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>543</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>544</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>545</th>\n",
+              "      <td>2015-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>546</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>547</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>548</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>549</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>550</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>551</th>\n",
+              "      <td>2015-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>552</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>553</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>554</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>555</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>556</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>557</th>\n",
+              "      <td>2015-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>558</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>559</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>560</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>561</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>562</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>563</th>\n",
+              "      <td>2015-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>564</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>565</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>566</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>567</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>568</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>569</th>\n",
+              "      <td>2015-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>570</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>571</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>572</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>573</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>574</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>575</th>\n",
+              "      <td>2015-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>576</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>577</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>578</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>579</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>580</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>581</th>\n",
+              "      <td>2015-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>582</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>583</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>584</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>585</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>587</th>\n",
+              "      <td>2014-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>588</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>589</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>590</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>591</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>592</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>593</th>\n",
+              "      <td>2014-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>594</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>598</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>599</th>\n",
+              "      <td>2014-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>600</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>601</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>602</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>603</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>604</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>605</th>\n",
+              "      <td>2014-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>606</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>607</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>608</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>609</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>610</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>2014-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>612</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>613</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>614</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>615</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>616</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>617</th>\n",
+              "      <td>2014-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>618</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>619</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>620</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>621</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>622</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>623</th>\n",
+              "      <td>2014-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>624</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>625</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>626</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>627</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>628</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>629</th>\n",
+              "      <td>2014-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>630</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>631</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>632</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>633</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>634</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>635</th>\n",
+              "      <td>2014-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>636</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>637</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>638</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>639</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>640</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>641</th>\n",
+              "      <td>2014-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>642</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>643</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>644</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>645</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>646</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>647</th>\n",
+              "      <td>2014-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>649</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>650</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>651</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>652</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>653</th>\n",
+              "      <td>2014-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>654</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>655</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>656</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>657</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>658</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>659</th>\n",
+              "      <td>2013-12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>660</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>661</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>662</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>663</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>664</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>665</th>\n",
+              "      <td>2013-11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>666</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>667</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>668</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>669</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>670</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>671</th>\n",
+              "      <td>2013-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>672</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>673</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>674</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>675</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>676</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>677</th>\n",
+              "      <td>2013-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>678</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>679</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>680</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>681</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>682</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>683</th>\n",
+              "      <td>2013-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>684</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>685</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>686</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>687</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>688</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>689</th>\n",
+              "      <td>2013-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>690</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>692</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>693</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>694</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>695</th>\n",
+              "      <td>2013-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>696</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>697</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>698</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>699</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>700</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>701</th>\n",
+              "      <td>2013-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>702</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>703</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>704</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>705</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>706</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>707</th>\n",
+              "      <td>2013-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>708</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>709</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>710</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>711</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>712</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>713</th>\n",
+              "      <td>2013-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>714</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>715</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>716</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>717</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>718</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>719</th>\n",
+              "      <td>2013-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>720</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>721</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>722</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>723</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>724</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>725</th>\n",
+              "      <td>2013-01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['time'] = pd.to_datetime(df['period']) # Create time variable\n",
+        "df.sort_values('time',inplace=True)"
+      ],
+      "metadata": {
+        "id": "j0V2-voEGEng"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "temp = df['period'].to_list()\n",
+        "df['month'] = [ temp[j][5:7] for j in range(len(temp)) ]\n",
+        "df['month'] = pd.to_numeric(df['month'], errors='coerce')\n",
+        "df['month'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 491
+        },
+        "id": "9618q_QEGF6k",
+        "outputId": "55902b54-ae57-44a6-daa2-5ec814711a73"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "month\n",
+              "1     66\n",
+              "2     60\n",
+              "3     60\n",
+              "4     60\n",
+              "5     60\n",
+              "6     60\n",
+              "7     60\n",
+              "8     60\n",
+              "9     60\n",
+              "10    60\n",
+              "11    60\n",
+              "12    60\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>66</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "covid_marker = df['time'][220]"
+      ],
+      "metadata": {
+        "id": "dE-vKGUIGHH3"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['sectorName'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "dm9n5FeyGIRQ",
+        "outputId": "5bfec32d-653f-4a2e-8f93-1f4cf1fee2e4"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "sectorName\n",
+              "transportation    121\n",
+              "all sectors       121\n",
+              "commercial        121\n",
+              "residential       121\n",
+              "other             121\n",
+              "industrial        121\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sectorName</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>transportation</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>all sectors</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>commercial</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>residential</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>other</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>industrial</th>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize time series:\n",
+        "sectors = df['sectorName'].unique().tolist()\n",
+        "vars = ['price', 'sales']\n",
+        "for var in vars:\n",
+        "    i = 1\n",
+        "    plt.figure()\n",
+        "    for sector in sectors:\n",
+        "        plt.subplot(len(sectors), 1, i)\n",
+        "        plt.axvline(x=covid_marker,color='cyan', linestyle='--')\n",
+        "        plt.plot( df.loc[ df['sectorName']==sector, 'time'], df.loc[ df['sectorName'] == sector, var] )\n",
+        "        plt.title(var+', '+sector, y=0.5, loc='right')\n",
+        "        i += 1\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 848
+        },
+        "id": "vhEjhRCIGKQd",
+        "outputId": "9beb936b-02d5-4ce0-f3e1-a9486bce09b5"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 6 Axes>"
+            ],
+            "image/png": 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tW2p9fjD1cVGr1RbLvL298ff3t2hGA1OenZUrV5rfl5WVsXLlSry8vOjcuTMAw4cPJzMz02KEk16vZ/ny5Tg6OtK/f/9qy3I8KZ/t5zIol5mmm/jzdCIAg1p7E+hmb65pGdjKCxuFKTyI9Hdi35avKx3r2nurkMsY3s4PZ1vrStspFAqGDBnCTz/9ZJGQOCsri++++44+ffrc9snCRY2PIAiCYCEhIYHRo0fzwAMPcOjQIdauXcvEiROJioqq0/HeeustduzYQf/+/Zk2bRpt2rQhIyODDRs2sH//flxdXXnppZf4+eefGTlyJFOmTKFz584UFxdz9uxZNm7cSGJionlI+pQpU1izZg0JCQk3rIWqCBZeeeUVHnvsMaytrRk1alSlfjTXGjFiBB988AEPPPAAEydOJDs7m08//ZTmzZtz5syZWl97YWEhgYGBPPLII0RFReHo6MjOnTs5duwY77//vsW2/v7+vP322yQmJtKyZUvWrVvHqVOn+OKLL7C2NgUV06ZNY+XKlUyZMoXjx48TGhrKxo0bOXDgAMuWLcPJyanKcmw9nc4LP5zEKIFk0GNl58juLd/i2LUUf+8IEhI8ad26NeHh4byx4D90HfkPjqVryfjrLEZt5aSGFff2+eefZ+jQoSgUCh577LEqz/3GG2/wxx9/0KdPH2bMmIGVlRUrV65Ep9Pxzjvv1Pqe3rI6jwe7g4nh7IIgCJVVDGc/f/689Mgjj0hOTk6Sm5ubNGvWLKm0tNS8XZFkGsrNzJlSURXH4brhzJIkSUlJSdKkSZMkLy8vSalUSs2aNZNmzpwp6XQ68zaFhYXS/PnzpebNm0s2NjaSp6en1KtXL+m9994zD+eWJEkaO3asZGdnJxUUFNz0mpYsWSIFBARIcrncYmg7UO0Q8//+979SixYtJKVSKbVu3VpatWqV+d5cf51VHSMkJESaPHmyJEmmIekvvfSSFBUVJTk5OUkODg5SVFSU9Nlnn1ns079/fykyMlKKjo6WevbsKdna2kohISHSJ598Uun4WVlZ0pNPPim5unlIMoW15N+slXloeoWK4ezvvvuu9Pu5DPNw8+5v7pRC5m6TvB5+TbL2CJZkcoXF0Pbz589LgwcPlhwdHSVPT0/pmWeeMQ/Rv/Ycer1eeu655yQvLy9JJpNZ3Juqfv8nTpyQhg4dKjk6Okr29vbSwIEDpYMHD1psUzGc/fq0B7t375YAaffu3fXy/JZdLeQ9RaPR4OLiglqtvu1VbIIgCE3VokWLWLx4MTk5OebalaqUYJquAuA8pkzOt5OPjw+TJk3i3Xffvc1nbjgDBgwgNzf3pn2hKiTlFTP6kwOoS8tRyGX8NLM3bQNcKm2351IOz6yJpsxg5KGOAbw3LoqCkjIOX8njbKqa4e38iApyreeraTj18fwWfXwEQRCEWrEHEq++bnfQExMTQ2lpKXPnzr3NZ246inV6nvk6GnVpOVZyGQajxPzNZ9EbLEddHb6Sx7PfmIKeYW19efeR9ijkMjwdlYxs78/84W3uqKCnvjRo4LN3715GjRqFv78/MpmsUucwSZJYsGABfn5+2NnZMXjwYOLi4m563E8//ZTQ0FBsbW3p3r07R48ebaArEARBEJqSyMhINBrNDWuk7mZGo8SL609zKasIbyclW2b2xtnWirNpalYdSDRvt/tiNlNWHUVbbuS+1t589FhHrBSirgMaOPApLi4mKiqq0giBCu+88w4ff/wxK1as4MiRIzg4ODB06NAbzpC7bt065syZw8KFCzlx4gRRUVEMHTq0QZJbCYIgCEJD+zM2iyyNlkKtnp3nsziWmE+ZvnLOnGKdnte3nWd7TCY2CjkrnuhM2wAXXhlhSl74/h8XSc4r4adTaTyzJhptuZEBrbz47PFO2FiJoKfCbevjI5PJ+PHHHxkzZgxgqu3x9/fnxRdf5N///jcAarUaHx8fVq9eXW3v8O7du9O1a1c++eQTwJT0KigoiOeee4558+bVqCyij48gCELdlQL9rv68F6i/NIb3nnNpakYu319puaejkondg3m8ezAeDjasi05h2c44cgpNQ+DfHtuOR7uaMhxLksTEL49w6EoeoR72JOWXIEnwYAd/3hsXhfVdVNNTH8/vRhvOnpCQQGZmpkWyKhcXF7p3786hQ4eqDHzKyso4fvw48+fPNy+Ty+UMHjyYQ4cOVXsunU5nkS+hqrTbgiAIQs0Ygehrfq6rIp2evy5m82dsNlGBrkzuFXrrhbvDHLqcB4Cfiy1hng4U6/SkFpSSW6Tj411xfLY7Hm8nJelqU0tIsLs984a1Zng7P/MxZDIZbz3cjqHL9pKYZ5pJbVLPEBaNikQur30Sx7tdowU+mZmZgKl3/rV8fHzM666Xm5uLwWCocp+KrJVVWbp0KYsXL77FEguCIAj1YV9cDmsOJrE3LsfcpLP5RBrOdlY81DHwJnvfXaKT8gGY1DOU6QNMk52WG4z8HpPJ1weTOJqYT7pai5u9Nc8PasHj3UOqbLYK83TgtZERLP31AtP6NeOFQS3qlLn6XnBPJDCcP38+c+bMMb/XaDS3PUW2IAiCAEcT8pmy6hiGq7N5h3jYE+rhwJ5LOczddJZwL0faB7o2biFvE0mSOJ5kmsy0S6ibebm1Qs7I9v6MbO/P+XQN8TlFDGjlVWVm5Gs90SOEid2CUYhanhtqtMDH19c0lX1WVhZ+fn9X2WVlZdGhQ4cq9/H09EShUJCVlWWxPCsry3y8qiiVSos5VwRBEITbL69Ix/Pfn8RglBjcxocXh7Skta8TkgTTvolm54Vspn19nJ+f6423k21jF7fBJeWVkFtUho1CTrsqcvAARPg7E+Ff874sIui5uUbr8RQWFoavry+7du0yL9NoNBw5coSePXtWuY+NjQ2dO3e22MdoNLJr165q9xEEQRAan9EoMWf9aTI1Wpp5OfDRYx1o4+eMTCZDLpfx4aMdaO7tSKZGy/S1J9DpDTc/6B0u+mptT9sAZ2ytq56RXKh/DRr4FBUVcerUKU6dOgWYOjSfOnWK5ORkZDIZs2fP5o033uDnn3/m7NmzTJo0CX9/f/PIL4BBgwaZR3ABzJkzhy+//JI1a9Zw4cIFpk+fTnFxMU8++WRDXoogCIJwC1bsvcyeSzkoreR89ngnHJSWDQ5OttZ88URnnGytOJ5UwLCP9vHtkSS05XdvAHT8av+eLqHujVySe0uDNnVFR0czcOBA8/uKfjaTJ09m9erVvPzyyxQXFzNt2jRUKhV9+vRh+/bt2Nr+XcV5+fJlcnNzze8fffRRcnJyWLBgAZmZmXTo0IHt27dX6vAsCIIgNJzapA+MTszn/R2XAHj9wUha+1bddNPMy5HPHu/EjG9PcCWnmFd+PMf7Oy7xRI8QZt3X/K4alg0QnWiq8ekc4naTLYX6JObqEnl8BEEQSFeVMul/RxnV3p8XBreo12OPW3GQY4kFjOngz4ePdrjpaKMinZ71x1L434EEUgtKAXjx/pY8N6h+y9WY1CXlRL2+A4DoVwfj6Sj6odaEmKtLEARBqBffHUkmPruIz/6KR11aXm/HzVRrOXa1ZmPusNY1GmLtqLRiap8w/vr3AOY+0BqAtUeSKDfcStagpuVEsumehHk6iKDnNhOBjyAIwj1OkiS2nEoDQKc3su1Mer0d+9ezGQB0CXHDz6V2OZ6tFHKe6hOGp6OSLI2OHTFZN9/pDlGRv0c0c91+IvARBEG4xx1PKjA3KQFsiE694falwICrr9Ibbgm/XA18RrT3u8mWVbOxkjOhmynv2teHEm9crjID/92fYM6N05RV9O/pIgKf204EPoIgCPe4H0+aansGtvLCSi7jVIqKuKzCarc3Anuuvm7U+JSuKuV4UgEyGQxrW7fAB2Bid1NSviMJ+cRmVj3lUEp+CY+sOMiSbecZv/IQ64+l1Pl8Da3cYOR0qgqwTFwo3B4i8LkHGI3SXdU2vvtiNoM/2MNHO+O4B/vmC0K9KtMbzbUyT/VpxoBW3gBsOH7jWp+aqGjm6hrijq9L3RMS+rnYMSTCNHL3m0NJldbvj8tl9Cf7iUnXYKOQYzBKvLzpDB/suNgkPyNi0jVoy4242lvTzNOxsYtzzxGBz11MW26q9u2xdBd9397NuTR1Yxfplm2ITuHpNdHEZxfx4c5LLPgpBqOx6X2wCQ3LYJS4klPEr2cz+GDHRVbuuWyeAkGo3s+n03l963k02r87L++5lIOqpBxvJyU9wz0Y38U0V9bmE2m3/IXpVpu5rvVEzxDAVDtVUX69wchnf8Uz6X9HKCgpp32gC7tfGsCsgc0B+PjPeF5cf7rJ5QKKTrzavyfYTUwi2gjuibm67jXacgNrDyexcu8Vcgr/npX+0ZWHWPlEF/q0qE0GjupptOUcuZJPz3APHJX186eUrdGy4XgqG6JTKNLpebBDAI93DybM04FPd8fz3tVcIN1C3TmWlM83h5Mo1ul555H2WN1lOT6Eygq15Xy0M47vjiZTUmb5MLO1VlQ5u7ckSVzOKWbnhSx2XcgiJb+Ulr5OtAtwpl2AC52C3fB2vvOnRygp07PxeCpbTqZxf4Qvz/ZrZvFQ/d/+BF7fdh6Ac2lq1kzthp2Ngi1Xm7lGR/mjkMsY2NobT0cbcot07LmYw+CIuuVIS1OVcjJZdbWZq/ophWqqZzMPWng7EpddxKbjqbTxc2bRzzHEZpqa5MZ1DmTJmLbYWiv499BWBLjZ8eqWc2w+mcahK3k8P6gFj3QOrFMuIKNRYm9cDjq9kSERPrc8+WdFH6TOopmrUYg8Pk0gj09pmYHFW2P4Mzabf93fkse6BtX5P5a23MBjXxzmVIoKgABXO/45IJzfzmZw8HIe1goZ742L4sEOAbdU5uxCLY9/eYS47CIclVaM7RTAEz1Dae5d+2pbbbmBPZdy2HwilZ0Xsqv85l7xgQcwfUA4Lw9txc+n05mz/jQGo8TQSB9mDWxBsIc9LnbWFOv0HE8q4EhCHufSNPQM9+CZvs3EPDYNaNeFLBJyi2nt60xrP6d6HaIrSRI/nkzjrV9jyS0yBfO21nJa+TjhYm/D3ks5ONgo+GNOf/xd/x45lJJfwrRvjnMho+p+IQAymemhOqZjAMPa+uJ0k4kga+Kvi9n832+xhHk6MDrKn4Gtvc1TEqSrSjl0OY+SMj0j2vvj7mBzS+fK0mhZfTCR744kWwxDHxLhw/vjo3CytearfVd445cLAFgrZJQbJAa08uKD8R3ouXSXaSTXc31oe3W+qDe2neer/QkMifDhi0ldKp2zGKj4n14EOFRRropzdg9zZ92z9TOl0DeHEnntpxjsrBWUXq3FcbW3Zv6w1ozvUvlzc8+lHOZtOkOGWguYJkSdNbA53cM8CHSzQy6XYTRKHLicy/roVPbF5dDC25H7WvswqI03/q52bDqeyqoDCSTmlQB/f/7cymd0n7d3k1ukY920HnRv5nELd+TeUx/PbxH4NHLgk5BbzPS1x83fWsD07Wjpw+1wta/dB6IkSby88QwbjqfiYmf6MHi4UyA2VnJ0egMvrj/NtjOmqucX72/JjIHN6xQIZKq1TPzqMFdyilHIZRaBSvcwd3qFe9I11I0Owa7Y21RdE6QqKePwlXx+O5fBrgvZFOn05nWdQ9yY0C0Ydwdrvj2czJ8Xs5Ek0wNq0ahIi2/1f5zPYua3Jyi7pkre1d6aIq0e/XUBVN8Wnix7tAMeImdGvdsRk8m0b45bLPN0VPL8oOZM6hl6S8c+kVzA0l8vmHPBhHk6sGBkBP1aeqG4+uB6ZMVBTiSrGNTam68md0Emk1FQXMbYFQe5klOMjUJOj3APBrfxprWvMxezCjmXquZMmtoiKFJayekV7kGPZh50b+ZBW3/nWtUklhuMvL/jEiv2XLZY7qi0okczD+KzC80PUDAFb2M7BTK1TxjhXrX70nA6RcX/DiTwy5kM8996iIc9g1r7sPZwEmUGI+FeDgyJ9OXzv0zlmTWwOf1befHEf4+gLTcS4mFPUl4Jzb0d+eNf/cwP84uZhQxdthcruYzD/xlUKYi9PvCx0hvYdSEbhVxG/5Ze2ForGPPpAU6lqFjyYCRP3OLfQIUinZ4eb+2iSKdHLjN1en7x/la43SB41JYb+O5IMp/ujievuMy83N5GQQsfJ3ILdaSpqh6bZiWXme+to9LK/Dk1tXcYr41sU+vgp7TMwLRvotkXl4uzrRVHXxks5uiqJRH41FFTCHwkSWL7uUxe3niGQp0eT0cbxnQIYPXBRPRGCT8XW5Y92qFW3wa+OZzEa1vOIZfB11O7V2rSMhollvxynlUHEgHoGurGB+M7EORuX+NzpKlKmfjlYZLySvB3seXbZ3qQVlDKmkOJ7LqQxbWxhkIuI8zTgQBXOwLd7PBzsSUpr4TjyQVcySm2OK6fiy3D2/kxvksQrXydLNal5Jfw8+l02gW40K+lV6UyHbqcx4c7L3Elp4jcor8/2AJc7ejezJ1gd3tW7rlCabkBPxdbPnqsIzZWck4kFXAiuQBVSTlDIn14MCoAF/tb/7Z/r4nLKmTMpwcoLjPQPtCFIq2ehLxiKj5ZVj7RmaGRtW/qOJuq5oM/LrL7Yg4AdtYKnhvUnKf6hKG0snxYxGUVMvzjfZQbJD6Z2JFBrX2Y+NVhTiar8HexZeP0XhY1Qdeq+Pv68WQa8VdrFSs42CgIcrfHx9kWX2dbgtztGNbOr8ogJbWghOe/P8mJZBUAj3cPxtHWim2nMywerHIZtA90pdxgJCb976BreDtf3hzT7oYP8SKdnj/OZ7L2cLLFkO2uoW483bcZg9v4oLg6Kuuf3xwnU6M1b/Pcfc2Zc39LZDIZf13M5pmvoyk3mH5J/x7Skln3WWZFfvCT/ZxOVTOtXzPmX5d4sBjwBmTFZSw+nMT3h5LMNXEudtYMifBhw/FUZDI48p9B9TrT+rYz6fwZm83U3mHmGqqaKNbpWX0wkV/OZBCfU0SZ/u8vS062VozpEMDwdn7EZxeyKzabg5fzKNMbaebpwJO9Q3m4UyCbT6bx2pZzAPyjRzCLR7clMa+Y44kFnEtXY2ejwNvJFh9nJX4utkT6u5gDm5IyPU+tjubQlTzsbRT8d3JXeoaL2p7aEoFPHTVU4PPb2Qx+PJlGa18nWvo60crHiQA3O3ILy8hQl5KuLiU2o5CzaWrOpanRaE3fHrqGuvHJxE74ONtyJlXFCz+cIiG3GLkM/m9se8Z3CbI4jyRJXMgoxM5GQYi7PXK5jOjEfB774jB6o8T8Ya15tn94lWWUJIkNx1NZ/HMMxWUGHGwULBwVyYDWfwcU1nI5rvbWFh90GepStp/L5Kt9CaSpSglyt+O7p3tYBE2pBSX8GZtNdGIBx5MKqv0WVSHM04H7WnszvJ0fHYNc66WTX5FOT0p+CY5KK4uyXcwsZPq3xysFXNeysZLzQKQvk3qG1PukgQfjc/loVxwj2/vV27ffpkBdWs6YTw+QkFtM9zB31j7dHWuFnJIyPW//FsuaQ0k42CjYMrM3LXycqjyG3mDkjV8usDcuBxuFHGuFHAmJc2mmoEAhl/FIp0BeGNyi2uAFYNnOSyzbGYenow1tA1z462IOLnbWbPxnz2rPfS1JkojNLORAfC5HEvI5ciXP/H/0eh2DXRnbKZAW3o4cuJzHvrgcTqeoMEqmh+g7Y9szrJ2pQ6/RKHEiuYBjiQW09HGka5g7zrbWSJLEkYR8vtqXwK7YLCTJFKx/+ngnOgS5ms+l0xvYHZvD1tPp7IrNQltuemBbK2SMau/Pk73DaBdYOQDIKdQx67sTHEnI54VBLZg9uIXF/+lfzmTw3PcnTIHQvwdU+gK06XgqL244DcAjnQN586G25oAzXVXK539dZn10CrqrAYSvsy0yGeZmJYAezdz5YVr9NHPVJ73BSGJeCRczC1HIYUAr70o1LyVletJVpTTzdLT4bFp/LIW5m88gSabAuLis+o7TSis53Zt50K+FJ7/HZHIssQBHpRWrnuxKVzExaZ2IwKeOGirwWbw1xlybUhM2VnKe7BXKv4e2suhwV6zT88qPZ9lyypQ99ZXhbXimXzPAFFy88uM59lwyfQt2UloRGeBMfLaptmNEez8+mdDxplWwKfklzFl/ytx8cD0XO2taeDvSzMuBi1lFnL7aZwgg1MOe757pccOHEJiCpSs5xaQWlJBWUEqaSouPs5LOIW50DHa75b4NtVWk0zNv0xm2ncnA1d6ajkGudAp2w9ZawaYTqRbNjSPb+/HKiDa1zjR7vWKdnv/7LZZvDv89BPe/k7swqI1lh1Gd3kBpmaFS86a6pJy1R5LYdiaDUA97RkX5c1/ryh/SjcFglHhqzTH+uphDgKsdP8/qbdGMWG4wMum/Rzl0JY9QD3t+mtUHFzvLGrVrm2evJ5PBmA4BvDCoBaGeVfUisaTTGxjx8X5zrY3SSs63T3evcxBbMXIsTVVKtkZHlkbLyRQVey7lVDuCrGuoG++P60CwR81rUcHU2XjWdydIzCvBWiFjwcgIuoS6sz46hS0n0ygo+bvvTpinAw928Gdit+CbdsqWJIncojK8nKpu3j1yJY9yg1TlgAdJklh1IJE3fjmPUYJOwa4sHBXJ+ugU1kenmGuL2gY480zfZgxv54dcJuPg5Vw2n0jjRHIBb4xpS98WlWtp73Q/nUoz9y+0sZLTIdCVDsGu6A0S2YVasjU6EvKKLQaXgCko/npqNzoGi07NdSUCnzpqqMDnXJqaown5XMoqJDazkEtZhZSUGVBayfF3tcPHWUm4lyPtAlxoG+BCSx8nbKyq7j8gSRJLf4vli71XAFOHOl9nW97ZHktxmQFrhQyZTGZRXdvKx4kfZ/aqtl/N9QxGiZV7L/P57ssUlf39zbaqvwiZzJRh9IG2foztFFDr/kdNSZFOj4ONwiI4lCSJs2lq1h5OYuPxVIySqQ/Ac/eZRoIoreXYKEyvmtZMHbqcx8ubTpOSb6r5auPnzIUMDU62Vvw8qw9hVx/mCbnFPLnqKIlX+1r0aOZO11B3zqSq+b6K0UuOSisGtPLCydYKnd5IuUHCx0nJSw+0qtQEdDOSJJFTqEOnN149lhE7awWBbnbmvi2SJBGTruH3mEx2X8ymtMyAjZUCvcFIXHYRSis5m6b3qrLZIa9Ix+hPDpCmKmVAKy/+O7mrRb+ypb9dYOWeK8hl8MaYdgS721NuMFJmMNLKx6lGAc+1jifl88iKQ8iAz/9Rtya2m8ku1PLzqXQ2nUgjp1BHj2bu9G3hSZ8WXgTc5MvAjWi05by04TS/VzEtg4+zktFR/oyOCqBtgPMtjyqqjX1xOcz89kSl2q8ezdx5/r4W9Az3uK3laSouZhZSXKYn0t+5yv93kiQRl13E3ks57IvLJadQx9tj21dZOyfU3F0R+ISGhpKUVDkh1YwZM/j0008rLV+9ejVPPvmkxTKlUolWq620bXVuVx8fo1GiqEyPk9Kqzh8Mn/91mbe3x1os6xrqxv+NbU+wuz3x2UWcTVOTkl/CxO7Bt1xDAabOgFdyionLLuRyTjHeTkqGRPjcFUN+a+JcmpoFP50z99W4npVchrVCjo2VqUlwQrdgnugRgsPVIf35xWUs/fWCuRYjwNWOdx5pT9dQdyZ8eZjjSQW08nFi84xexGYW8vSaYxbf6K/X2teJyb1CScwrrtRf5FozBoTz8tUJHW8kt0jHgfhc9l7KZV9cDtnXfSutuMZgD3tCPRyIyy40B29VWfZoB8Z0rH6U4Lk0NY+sOIi23EgbP2ce6xrEmA4BrItO5q1fTX/b74xtz/iuQdUeozaiE/OxVsiJuqa56E4hSRJf7Uvg/7bHIpfB/RE+jOsSRL8WXo06IjEht5hnvjblz+rZzIPpg1vw9tX+h5uAe+OTQWgK7orAJycnB4Ph72+0586d4/7772f37t0MGDCg0varV6/mhRde4OLFi+ZlMpkMH5+a55poCp2ba+P7o8m88uNZ7KwVzBvWmse7h4ikVw3MaDQNn35/x0XS1TcPqt0dbHi6bxhu9ja8vT0W1dVAZkK3YP4zvLV5iHSWRsvI5fvJKdTRJcSNs2lqdHoj7QNd+GB8FJdzijl8JY/oxALcHGyY2juU/i29zIFzRX+Rg5fzkAHWVnIKSsrMtSabZ/S26B9yvdUHEljyywWLphqZDGytFFgrZNhYKSjUlpv7bVSwtZYzoKU3D7T1xdfFljK9kTK9aVRQTfrP/Ho2g9nrTplrKG2s5Oaf5z7QmukDqu6Tdq9KV5ViZ624YUfn202nN5Cu0hLm6VCj4eyC0BDuisDnerNnz2bbtm3ExcVVWUuyevVqZs+ejUqlqvM57rTAByAprxgnW+vb3i9GuDrlh9FoftiXG0xTgOj0Rk4mF/Dp7niLIcpgqqV586G2dA6p3L/kWGI+E652RAcY3Mabjyd0rHETZVVe+OEkP51KJ9zLgV+e71tlH6CKHCgV5evfyot+LbzoHOJmsb3RKJGp0ZKQW0xCbjGejkr6tfS8pfIBFBSX8dOpNNZFp5qHkD/TN4z/DK/9sGChcYnAR2gsd13gU1ZWhr+/P3PmzOE///lPldusXr2ap59+moCAAIxGI506deKtt94iMjKy2uPqdDp0ur+r8zUaDUFBQXdU4CM0XXqDkZ9Pp/PJn/FkabT86/6WTOkVesP8L98fTeb1red5tGsQr42MuOVmjILiMoYs20tOoY5n+zVj/vA2Fut/OJrMvM1nAfhn/3DmPlD3BGy3qqLPUIZay6DW3qL28g4kAh+hsdx1gc/69euZOHEiycnJ+Pv7V7nNoUOHiIuLo3379qjVat577z327t1LTEwMgYGBVe6zaNEiFi9eXGm5CHyE+iRJEuUGqdoO69fTG4z1Os3GH+ezeObraGQyWDetJ+FeDhSUlHPwci4Lf45BkuCpPmG8OkLUsAi3RgQ+QmO56wKfoUOHYmNjw9atW2u8T3l5OW3atGHChAksWbKkym1EjY9wr5iz7hSbr869dL1JPUNYPDpSBD3CLROBj9BY6iPwaTKTlCYlJbFz5042b95cq/2sra3p2LEj8fHx1W6jVCpRKsU0BcLdb+GoSI4l5ZtHYTnZWuFmb8PoKH9z1l5BEIR7WZMJfFatWoW3tzcjRoyo1X4Gg4GzZ88yfPjwGu9TUcml0VQ/caEg3IlkwOanO1Kk1eNiZ23RlFZUVFj9joJQC9fmP9cA1ecuFoT6VfHcvpXGqiYR+BiNRlatWsXkyZOxsrIs0qRJkwgICGDp0qUAvP766/To0YPmzZujUql49913SUpK4umnn67x+QoLTQ+AoKD6yRsiCIJwr6q6N6YgNKzCwkJcXOqWDLJJBD47d+4kOTmZqVOnVlqXnJyMXP73t9aCggKeeeYZMjMzcXNzo3Pnzhw8eJCIiIgan8/f35+UlBScnJxE1T9/93lKSUkRfZ4akLjPt4e4z7eHuM+3h7jPliRJorCwsNoBUDXRpDo3C43jTsxrdCcS9/n2EPf59hD3+fYQ97n+1d9YWkEQBEEQhCZOBD6CIAiCINwzROAjoFQqWbhwoRjy38DEfb49xH2+PcR9vj3Efa5/oo+PIAiCIAj3DFHjIwiCIAjCPUMEPoIgCIIg3DNE4CMIgiAIwj2jSSQwvN2MRiPp6ekigaEgCIIg3EGuTWB4bXLj2rgnA5/09HQxXYUgCIIg3KFSUlIIDAys0773ZODj5OQEIFKAC4Ig1EEJ0O3qz0cB+0Ysi9D0nE1V8davsXz4aBS+Lnb1euyKKTwqnuN1cU8GPhXNW87OziLwEQRBqCUFkHL1ZyfAoRHLItSPnEId3xxKxMtJSQsfJ1r5OOHmYFOrYxiMEiv3XuaDHZfQGyU+PZDOR491bJDy3ko3lXsy8BEEQRAEwURbbuDpNcc4naq2WB7qYc8nEzvRNuDms6BnqEv517pTHL6SD8CIdn68Prptg5T3VolRXYIgCMJdSSaTsWjRosYuRo399ddfyGQy/vrrL/OyKVOmEBoa2mDnlCSJhT/FcDpVjYudNfe19ibQzdQ8lZhXwvPfn6S0zHDDY5xLU/PAsn0cvpKPvY2Cdx5pzycTO+Jib91g5b4VosZHEARBEO5R3x1NZl10CjIZfDyhI/1begGQW6RjxMf7uJJbzNLfLvD6g5a1N9999x3Z2dnMeu55/r3hNOrSctoFuPDxhI6EeTbtxk8R+AiCIAh3pdLSUqysxGOuOseTClj0cwwA/x7Syhz0AHg6KnlvXBRP/PcoXx9KYnAbH/pds/67777j3LlzOHYeTWxmIa721qx+sisejk1/TjHR1CUIgiDcNYxGI1qtFgBbW1sR+FQjr0jHjG+PU26QeCDSlxkDwitt07eFF5N7hgDw0sbTqErKLNYbjBIf/HEJgPnDWt9y0KPX6ykrK7v5hrdIBD6CIAhCrciAiKuvhkgBu2jRImQyGbGxsYwfPx5nZ2c8PDx44YUXzEGNuSwyGbNmzeLbb78lMjISpVLJ9u3bzeuu7+OTlpbGU089hb+/P0qlkrCwMKZPn27xwFWpVMyePZugoCCUSiXNmzfn7bffxmg01ul6kpKSmDFjBq1atcLOzg4PDw/GjRtHYmJinY5XlejoaIYOHYqnpyd2dnaEhYUxdepUi22MRiPLli0jMjISX3dnot8cR/meFbxyf3ClUVK//fYb/fv354N/9CR12XhOLZ/BQy++g6qkjAEDBvDLL7+QmpLMhSXDSHp7JHPH9TPvm52dzVNPPYWPjw+2trZERUWxZs0ai+MnJiYik8l47733WLZsGeHh4SiVSs6fPw/A8uXLiYyMxN7eHjc3N7p06cJ3331XL/dKhMKCIAhCrdgDMbfhPOPHjyc0NJSlS5dy+PBhPv74YwoKCvj6668ttvvzzz9Zv349s2bNwtPTs9rOwOnp6XTr1g2VSsW0adNo3bo1aWlpbNy4kZKSEmxsbCgpKaF///6kpaXx7LPPEhwczMGDB5k/fz4ZGRksW7as1tdx7NgxDh48yGOPPUZgYCCJiYl8/vnnDBgwgPPnz2Nvf2uZkLKzsxkyZAheXl7MmzcPV1dXEhMT2bx5s8V2zz77LKtXr2bS5Clomg1Gk5NO/ulfGTt6BAcOHMDa2tQZefXq1UydOpXIyEjmz59PkWTDJxt2cmzfn/Rc2oVOAyYSnJ5DamoqnoOf4cUhrWgZaGoGKy0tZcCAAcTHxzNr1izCwsLYsGEDU6ZMQaVS8cILL1iUadWqVWi1WqZNm4ZSqcTd3Z0vv/yS559/nkceecQc7J45c4YjR44wcuTIW7pXAEj3ILVaLQGSWq1u7KIIgiAI11m4cKEESKNHj7ZYPmPGDAmQTp8+bV4GSHK5XIqJial0HEBauHCh+f2kSZMkuVwuHTt2rNK2RqNRkiRJWrJkieTg4CBdunTJYv28efMkhUIhJScn1/p6SkpKKi07dOiQBEhff/21ednu3bslQNq9e7d52eTJk6WQkJAbHv/HH3+UgCqvq8K+ffskQPr222+lH44mSSFzt0m9lu6SfvnlV/NySZIklUolOTk5Sd27d5dKS0vN++88nykN/XCPFDJ3mxQyd5tkF95VUjh7S2/9et7iPMuWLZMAae3ateZlZWVlUs+ePSVHR0dJo9FIkiRJCQkJEiA5OztL2dnZFsd48MEHpcjIyCqvoz6e36KpSxAEQWiSZs6cafH+ueeeA+DXX3+1WN6/f38iIiJueCyj0ciWLVsYNWoUXbp0qbS+oqlnw4YN9O3bFzc3N3Jzc82vwYMHYzAY2Lt3b62vw87u7+zF5eXl5OXl0bx5c1xdXTlx4kStj3c9V1dXALZt20Z5eXmV22zYsAEXFxcGDx7Miu2nMJSoGRvhTLduXXF0dGT37t0A/PHHHxQWFjJv3jxsbW3N+w9q48NvL/Tlu2e6M6i1NwBWChkvDGphcZ5ff/0VX19fJkyYYF5mbW3N888/T1FREXv27LHYfuzYsXh5eVksc3V1JTU1lWPHjtXthtyEaOoSBEEQaqUE6Hr152M03JQVLVpYPlTDw8ORy+WV+saEhYXd9Fg5OTloNBratr1xUr24uDjOnDlT6WFcITs7+6bnul5paSlLly5l1apVpKWlIUmSeZ1arb7BnjXTv39/xo4dy+LFi/nwww8ZMGAAY8aMYeLEiSiVpg7HcXFxqNVqfHx8zPu9uBxevPpzxXVdvnwZoMr7JJPJ6BXuSa9wT5J/8OTShWzsbSzDiKSkJFq0aFFpAtE2bdqY11+rqt/d3Llz2blzJ926daN58+YMGTKEiRMn0rt371rcleqJwEcQBEGoFQk4f83Pt0t10xRcW6Nyq4xGI/fffz8vv/xyletbtmxZ62M+99xzrFq1itmzZ9OzZ09cXFyQyWQ89thjde4wfS2ZTMbGjRs5fPgwW7du5ffff2fq1Km8//77HD58GEdHR4xGI97e3nSctICTKSqGRHgzudffQUd1gV51lFZybmHWCLOqfndt2rTh4sWLbNu2je3bt7Np0yY+++wzFixYwIsvvljFUWpHBD6CIAhCkxQXF2dRIxAfH4/RaKxTJmMvLy+cnZ05d+7cDbcLDw+nqKiIwYMH1/oc1dm4cSOTJ0/m/fffNy/TarWoVKp6OwdAjx496NGjB2+++Sbfffcdjz/+OD/88ANPP/004eHh7Ny5k/NGP+xCQ3l9xoAqEw2Gh5uGtZ87d47mzZtXe67qgtCQkBDOnDmD0Wi0qPWJjY01r68JBwcHHn30UR599FHKysp4+OGHefPNNys1f9aF6OMjCIIgNEmffvqpxfvly5cDMGzYsFofSy6XM2bMGLZu3Up0dHSl9RXNT+PHj+fQoUP8/vvvlbZRqVTo9fpan1uhUFg0b4HpWgyGG08FUVMFBQWVjt+hQwcAdDodYLoug8GA+uAPDGrtbRH06PV6cxA2ZMgQnJycWLp0aaXUAdeew8HBocpmuuHDh5OZmcm6dessjr98+XIcHR3p37//Ta8nLy/P4r2NjQ0RERFIklRtH6baEDU+giAIQpOUkJDA6NGjeeCBBzh06BBr165l4sSJREVF1el4b731Fjt27KB///5MmzaNNm3akJGRwYYNG9i/fz+urq689NJL/Pzzz4wcOZIpU6bQuXNniouLOXv2LBs3biQxMRFPT0/ANI/WmjVrSEhIuGEt1MiRI/nmm29wcXEhIiKCQ4cOsXPnTjw8POp0Hddbs2YNn332GQ899BDh4eEUFhby5Zdf4uzszPDhwwGQ+UXg0nEY6sMbOK/MZ5lqJNbW1sTFxbFhwwY++ugjHnnkEZydnfnwww95+umn6dq1KxMnTsTNzY3Tp09TUlJizsfTuXNn1q1bx5w5c+ja1dRBetSoUUybNo2VK1cyZcoUjh8/TmhoKBs3buTAgQMsW7YMJyenm17PkCFD8PX1pXfv3vj4+HDhwgU++eQTRowYUaP9b6rO48HuYGI4uyAIQt0VSZLE1VdRAxy/Yjj7+fPnpUceeURycnKS3NzcpFmzZlkMsZYk05D1mTNnVnkcrhvOLkmSlJSUJE2aNEny8vKSlEql1KxZM2nmzJmSTqczb1NYWCjNnz9fat68uWRjYyN5enpKvXr1kt577z2prKzMvN3YsWMlOzs7qaCg4IbXU1BQID355JOSp6en5OjoKA0dOlSKjY2VQkJCpMmTJ5u3q+tw9hMnTkgTJkyQgoODJaVSKXl7e0sjR46UoqOjJUmSpJ9PpUnN//OLaQj7lP9InTt3luzs7CQnJyepXbt20ssvvyylp6dbHPPnn3+WevXqJdnZ2UnOzs5St27dpO+//968vqioSJo4caLk6uoqARZlzMrKMl+vjY2N1K5dO2nVqlUWx68Yzv7uu+9Wup6VK1dK/fr1kzw8PCSlUimFh4dLL730kqRWq+vl+S2TJOl29k1rEjQaDS4uLqjVapydnRu7OIIgCHeUYsDx6s9FQH1PSblo0SIWL15MTk6OuXalKfLx8WHSpEm8++67jV2Uan1zKJEFP8cgSTCyvR8fjO+AjdWd28ulPp7foqlLEARBqBUZEHLNz/eimJgYSktLmTt3bmMXpVpf7r3Cm79eAOCJHiEsGh2JQn6v/sb+JgIfQRAEoVbsgcTGLkQji4yMRKPRNHYxqpVTqOPdHRcBeP6+5vzr/pbVjsS61zS5+q69e/cyatQo/P39kclkbNmyxWK9JEksWLAAPz8/7OzsGDx4MHFxcY1TWEEQBEFogr45lEiZ3kiHIFcR9FynyQU+xcXFREVFVRrGWOGdd97h448/ZsWKFRw5cgQHBweGDh1aadidIAiCcGdatGgRkiQ16f49TVlpmYFvDpsyJD/Tt5kIeq7T5Jq6hg0bVm2OBkmSWLZsGa+++ioPPvggAF9//TU+Pj5s2bKFxx577HYWVRAE4Z5UCvS7+vNeoP7yJgv1YeOJVApKyglyt2NopM/Nd7jHNLkanxtJSEggMzPTIqOmi4sL3bt359ChQ9Xup9Pp0Gg0Fi9BEAShboxA9NXXrU+4INQno1Hif/sTAJjaOwwrxR31mL8t7qg7kpmZCWAxyVrF+4p1VVm6dCkuLi7mV1BQUIOWUxAEQRAaw84LWSTkFuNsa8X4LuJZV5U7KvCpq/nz56NWq82vlJSUxi6SIAiCIFRLbzByPKmAZTsvMWfdKX44mkx24c37sn657woAj/cIwUHZ5HqzNAl31F3x9fUFICsrCz8/P/PyrKws87wkVVEqlSiVyoYuniAIdxm9wUi6SktiXjFJecVkF+q4NuVrmKcDD3UMQC5yowj1JKdQx6KtMey9lEOh9u95wTafTEMmgw5BrvRt7kmguz3+Lnb4OCvRaPVka7TEZxdxLLEAa4WMKb1CG+8imrg7KvAJCwvD19eXXbt2mQMdjUbDkSNHmD59euMWTrCgLTfw7w2nsVbImT+sNd7Oto1dJKEJ+eVMBttjMunf0oshkT4421o3dpEq2R2bzQs/nESjvfGklIeu5PH22PZNIjFclkbLV/uukKYqRVVSjqqkHL3RSOcQN/q28KJXuAeu9jaNXUzhBhZvjeGXMxkAuNhZ06e5J2GeDuyLy+F0qpqTySpOJqtueIxRUf74iM/cajW5wKeoqIj4+Hjz+4SEBE6dOoW7uzvBwcHMnj2bN954gxYtWhAWFsZrr72Gv78/Y8aMabxCC5V8tjuebVf/8+6+mM0bY9oysr1/I5dKaAqu5BTxr/WnKNMb2Xo6HZvNcvq19GJCtyAGtWkaI1AuZGiY9d0JissM2FjJCXG3J8TDAX9XW+RXhwbr9AbWR6ey8XgqpeUGlj3aAetG6kgqSRLrjqXw5q8XLGoJKlzKKuL7oynIZdA9zINlj3UQD8YGZDRKFOr0ONta1WooeZqqlN/Omfqr/m9KF/q39DYH1P8e2oosjZadF7I4m6omXa0lQ1VKlkaLk601Ps5KfJxtCXSzY1q/8Aa5rrtFk5ur66+//mLgwIGVlk+ePJnVq1cjSRILFy7kiy++QKVS0adPHz777DNatmxZ43OIuboaVlxWIcM/3ke5QSLQzY7UglLANE/MhG7B5hT3tjYKOga51jnHhN5gJCZdw7HEfC7nFNMhyIWBrb3xdqr7B3qhthwruRw7G0Wdj9HQdHoDBy/n0T3MHXubJvfd5YaMRonHvjzM0YR8Wvs6YTBKxGUXmde/NLQVMwc2b8QSmpoaxnx6gDRVKb3CPVj9ZLdq5zbafi6D574/SblBYnAbbz6Z2Alb69r/7WRptJxOUZFVqCNLrSW7UIuLnTUR/s5E+LkQ7uVgMTrHYJTQlhvQlhvI0uhYsu08h67kARAV5MpDHfxxc7DBxc4avUHi4OU89sXlmO91a18n1k3riYt9zWraSsr07LmYQ6ZGy4RuwRisFYReXZdI/c/VdacqKC5j4/FUvj2SRGJeCb2be7BgZCStfGs2o/hbv17gi71X6BXuwXfP9Gjg0t6Z6uP53eQCn9tBBD4Nx2iUGL/yENFJBQxq7c3n/+jMJ3/G8elflzEYK/+pjWjnxycTO9Y4+NHpDWw/l8mmE2lEJ+ZTUmaotE1UkCtTe4fyYIeAmx4vKa+YdcdSiM0sJDZDQ7pai41CTs9wD+6P8GFwGx98XZrON2OjUeKpNcfYfTGH1r5OfPFEF4I97Bu7WDX27ZEkXvnxHPY2Cn6f3Y8gd3suZhay9nCSOeHajAHhvDS01S0lXUvKK2ZfXC774nI4lliAt5OSke39GNnen1DP6h/T2nIDE788zIlkFWGeDvw4o9dNm4b+upjNs98cR6c30j3MnY8ndKxxbYokSXx3NJkl286jLb+1geF21gpeHNKSJ3uHVdvsFp9dyIQvj5BTqKNLiBvfPNW92iBfW27glzMZ/HYuk31xOej0pvJF+Dmz8onOBLnfOX93DU1VUsaSbRfYeiadMr3l71Ehl/GP7sH86/6WN/xbKtLp6fnWLgp1ev43pQv3tW4atZ9NjQh86kgEPg3nuyPJ/OfHs9jbKPhjTn8CXE2pzU6nqHjn91hyCnXmbRNyiyk3SLw6og1P921mXi5JEt8eSeZEUgGBbnaEeDjg52rLnks5bIhOJb+4zLyti501XULcCPd25MiVPE6nqgGQyWDDsz3pEupebVnjsgp59IvDFserSoSfM31betKvhRedQ9zq9I2+vizfFcf7f1wyv3e1t+bTiZ3o3bzpZ7jNUJdy/wd7KdLpWTAygql9wizWf7H3Mm/9GgvAlF6hLBwVUevgp1in58lVxziamF/tNi28HQlytzc3DTjbWlNxmgPxuey8kI2zrRVbZvammZdjtce51uEreTy1+hjFZQbc7K1Z+nA7HmjrZ7GNwShZBCQFxWXM3XSGHeezzOVq5uWAj7MtXo5Kcot0nM/QcD5dQ3EVAX4FG4WcXs09eH102xoFwRcyNIxfeYhCrZ77Wnuz8onOFk10RqPEllNpvL/jEmmqUvPyYHd7inR68ovLcLGz5qPHOjCglXeN7k9NpOSX8NelHPZczOFkcgGu9taEeDgQ7G5PhJ8zYzoGNMlZxbXlBv7x1RGikwoAiPR35h89QugU7MaHf1xie4yp6crZ1oqRUf48GOVP11D3Sh3iVx1IYPHW8zTzcmDnv/qLDvPVEIFPHd1pgU9OoY41BxMpKTPQyteRlj5OtPBxwrGJDVXM1mgZ9MEeCrV6XhsZwVPXPdiu982hRF77KQaFXMYP03rQNdQdnd7A3I1n2HIqvdr9fJ1tmdAtmKFtfWjp7WTxAZGl0fL6tvP8ciaDZl4O/Pp83yoDleS8EsatPEiWRkeEnzMTugXRyteZVj5O5BTp+ON8FjsvZHEiucBiFI+ttZxXhrfhiZ6htb4/t2pfXA6T/ncUSYK5D7Tmt3MZnElVo5DLmDWwOW0DXHC1t8bFzppgd/tGDdCuJ0kSz3wdzc4L2XQIcmXT9F5V1kp8cziJ17acA6B9oAsBrna42lvj4aBkbOdAwm5QWyNJErPXneKnU+lYyWVXO/R60jPcg/jsIradyeDg5bwqax6vpZDL+Hpqt1oHk/HZRcxed5JzaaYEqeM6B9LK14nTqWrOpqpIzCvB09GGEA8HQtztOXA5lyyNDmuFjLkPtGZq77AqH3ZGo0R+SZnF36FCLsPWWo7SSlGnTtXHEvP5x1dH0OmN9G7uQecQd3ydbbG1lvPVvgTOZ5iuwdfZlse6BTE00pfWvk5kqLVM//YEp1NUyGTwWNcgPBz+HjFray3H1d4GV3tr7KwVJOQWcymrkItZRUiSxLwHWtPruvuakFvM89+f5Gya+oZlfqRzIO+Ni6r1tTYkSZJ44YdT/Hw6HSdbK76a1IVuYe4WAfvB+Fxe33ae2MxC8zI/F1se7RrEs/3CsbNRYDBKDHhvNyn5pbwxpi3/6BHSGJdzRxCBTx3dKYGPttzA/w4k8NnuyxTpKndYDPWwp32gK+0DXegY7EqnYLdGm5NFkiSmrz3B9phM2ge68OOM3jf9QL72QeXtpOTbp7vznx/PciyxAIXcNByzpMxAcn4xKfmlhHk6MLF7MINae98wG6m6pJzBH+4hp1DHjAHhvPxAa4v1mWot41YeJCW/lJY+jqyb1hM3h6qroHOLdByIz2XvJVOzSXahDrkMvnmq+y3VsmjLDRxLzKdziFuN+umkqUoZ+fE+CkrKmdAtiKUPt0dbbuA/m8+y+WRape1d7a1ZMDKChzoGNPo8PUajxCe74/ngj0tYK2T88nxfWvpU3+dh0/FUXtp4muvjEwcbBe+Ni2JYO78q9/v+aDLzN59FIZexblqPKmv78op0nE5VkaXRkaXRkqXRUqT7uzZFLoOHOwXSv6VXna61TG9k2c5LfL7nMjX5ZA33cuCjxzrSNsClTue7FX/GZvHM18erDASdlFZMHxjO1N5hlQJond7Agq3nWXckudbntFbIeOeR9jzUMRAw1ZT9c+1xVCXlKK4Gq/1betGjmQclZXqS80u4nF3M6oMJGCV495H2jGtCSfne33GR5X/GYyWXsfrJbvRpUfVngsEocehyHj+dSmP7uUwKr36eB7rZsXh0JOUGI/9cewJXe2sOzRvUpPsYNjYR+NTRnRD47I7N5tUt58xVze0DXegc4kZcVhGXsgrJvqbJqMLYToG8+0j7RqkiXX8shZc3ncFKLmPLzN41/iAv1ul58NMDxGcXIZOBJIGTrRWfP9652g+Rmth+LpN/rj2OQi7jp2vKk1pQwuT/HeVyTjGhHvasf7ZnjYfaS5LEyxvPsOF4Kh4ONvzyfF9z/x+DUWLt4SQuZRUytU8Y4TdoItl7KYcFP50jMa+EZp4OfDyh8oPPYJTIKdSRqdGSqdby+V/xnE5V0zbAmY3/7GV+GEmSxA/HUvjtXCbqkjLUpeXkFZWZP1j7tfTirYfaEujWOP0x8op0zFl/mj2XcoCad16Ozy4iJl2NutQ0JHvvpRxzU8Kz/Zrx0tBWFsHv+XQNYz47QJneyLxhrfln/8Yd1XLkSh6f7I7H3kZh/nLSwtuJ3CIdSXklJOYVo7SSM7F7cKN2UD+VomLvJVOn5Sy1ltziMrqGuDFjYHPcq/kyAFAMeMVkYns5jxn8PTy4tMxAQUkZqtJyinV6gt3taenjREsfJ349l2Eepv3S0Fb4ONsyf/MZyg0SUUGufPlE52r/L1Y08dpay/l5Vp8bBs63y4boFF7aeAaAd8a2Z3zXmgVk2nIDv8dk8vZvsaSrTQkJHZVWFOn0zBwYzktDW9/kCPc2EfjUUVMPfI5cyeMf/z1CuUHCz8WWlx9oxYNRlknS8ovLOJem5myamtMpKnbFZmMwSjzdJ4xXRrS5rd/yE3KLGfHxPkrKDLz8QCtmDKjdqJz47EJGf3KAkjIDQe52rJrSlebet/7BNvPbE/xyNoMIP2feHtue/x1IYOvpdPRG033d8M+etQ4ItOUGHvrsIBcyNHQJceP7aT3IUGn594bT5n4lCrmMRzoF8sLgFvi7/j19Y6Zay5Jfzps//CvYKOTMHdaaST1DOHIln21n0k2BTGm5xXbOtlb88nzfm3YqLTcY+WLvFT7aFUeZ3oi9jYI3H2pr/pZ9uxxLzOe5706SqdGitJKz5MG2jOsSWKe/Tb3ByDu/X+SLvaastD2auTOhWzCtfJ3wcbLl4c8PkpBbzH2tvflqUhfRP6KBFQMVoX0RNRvVZTRK/N/2WPPvsMKIdn68Pz7qhk2zRqPE5FVH2ReXS3NvR36a2bveshJLkkRecRlXcorJLdLRt4UnTjfJK3Uxs5CRy00jV+sarJSU6floVxz/3ZeA3ihhrZCxf+59Is3ATYjAp46acuCTlFfMmE8PUFBSzrC2vnwwvkONqj03HU/lxQ2nAVP/j+kDTN94Y9LVbIhORS6T8XTfMIsHcX0o0xt5ZMVBzqSq6dHMnW+f7lGnPgdHE/LZdSGLZ/o1w9OxfrJs5xTquP/DPahKLAOIns08eOvhdjfsL3IjibnFjFq+n0Kdnr4tPDmeVEBJmQF7GwUdg105EG8aVmxjJaeNnzOa0vKrNRdlGCVTc8qUXmE82TuU17ed54+rnVvtbRQWo9QUchneTqYOuAGudjzTrxkdglxrXM7LOUXM23TGnMn199n9atxZtyZ2xGRyIllFK19H2gW40szTAVVpOb+ezeDnU+nmQDDcy4FPH+9Ea99b/7/2y5kMXtp4usrRfP4utvzyfN9qmy2F+lOXwKfC6gMJLN52HkmCmQPDefH+VjUKVHOLdIz4eB9ZGh0Pdwzg/fFRNw2iTyQX8PXBRJ7oGULnEMumz5IyPYt/Ps+v5zIsch9N7B7MWw+1q/aYBqPEIysOcjJZVS+B9sXMQlbsuUzXUHcmdg+u83HuFSLwqaOmGvhotOU8/NlB4rOLaB/owrppPWvV1vvl3iu8+esFwDQr78mUAosMn0orOdP6NeOf/cPr7dvS29tj+fyvy7jYWbN9dl/8XOo3sLpVP51K44UfTiGXwfB2fjzbL5x2gbfen6KiKa1CtzB33nskimAPe44n5fPO9oscSag8sqhjsCtvjGlLpL+pDJJkaiJb8ssFyvRG3OyteaCtH6Pa+9EtzP2WZ1Y2GiWmrjnGXxdz6NvCk6+ndrN4WBiNEmmqUvxd7SoFrJIkkV2ow85GYZFZuVBbzoKfYvjxur5FjkortOUG9Ff7jMhkpubXxaMj63XOoPjsIlYfTOBCRiEXMwsp0umxUcj5floPOoe41dt5hOrdSuADpia20jIDPcM9arXfkSt5TPjyMEYJFo2KYErv6gdQnEpR8fiXhykuM2All/HqiDZM7hWKTCYjTVXKM2uizZ24ZTLwclSSXajDw8GGY68MrjaYWXMwkYU/x+CotOKPOf2a3Gfe3U4EPnXUFAMfvcHI1DXR7L2Ug6+zLT/N6l2nKs//+y2WFXsum99bK2QMifQlp1DH0asPYi8nJQ9E+uLrYmuuUQhytyfA1c48XDQuq5CtZzLYEZOJqqQcW2s5ttYKlFZylNYKbK0V2Chk7IrNRpJgxT86VRq+21ScSDblcanvfi6f/BnH6oNJ/LN/s0ojciRJ4kSyirwiHS521uaRLt5Oyiq/pSblFZOmKqVrqHu9Z/9Nyivm/g/3UqY38unEToxob/o9acsNTF97nN0Xc3CwUdAu0IWoIFdsrRScTVNzJlVNbpGpM3eHIFf6tPCihbcj7/weS0p+KXIZjGjvT7qqlJh0tTkPTdsAZx6MCmBklF+DPxQkyRS4WcnlTSrf0t3uVgOfW7Fyz2WW/haLXAarn+xGvyo6osdmanh05WHUpeV4OtqQW2RKWTGmgz8PdwrkX+tOkVdchoeDDR882oHuYe7IZTI6L/mDQp2ezTN60Sm4chCdrirl/g/2UFxmYMmDkY0yuvNeJwKfOrodgU9OoY710SkcTcjHWiHD1lqBnbWCYHd72gW60D7QFXcHGy7nFPF7TCa/nMkgJl2DnbWCDf/sWedRHpIk8eYvF9gXl8voDv6M7xKEl5MSSZL4PSaLpb9dICmvpMp95TLwd7XDRiHnSm5xjc/5WNcg/m9s+zqVV7g9PvzjEh/tisPX2ZZdL/ZHAp5afazKWqlryWVUGl0FEOBqx0ePdTCPnNIbjMTnFGFnrSDEQ+Txvds1ZuAjSRL/3nCGTSdScbK14scZvWnu/XcTbkJuMeNWHCK3SEfHYFe+eao7PxxNZulvsRYj2CL8nPlychdzrjGAmd+d4JczGVX227k2JUPnEDc2PNtT9CVrBCLwqaOGCnwkSeLwlXzWHkni93OZ5ir/6rjZW1NwTf8TG4Wcjyd05IG2vvVWpuuV6Y1sO5POlZxi03DeQh2Z6lKS80ssMsdaK2T0b+nFyPb+NPd2vJoe34i23IBOb/pXqzdgLZfzYEd/lFZi+GVTpi03MOTDvSTnl/B492Bi0jWcSlHhpLTiy8ldcLO34VRKAadS1JTpjbQLcKZdoCuR/s7kFZexPy6HfXG5nE5V0SPMg9dGRTTJiUWF26MYqEhdmM3tn7JCpzfw+JempIEhHvasmtKVS1lFHEvMZ+vpdLILdbTxc+aHZ3qYp+U4fCWPWd+dILeojBHt/Hh3XPtKI+p+PJnKv9adprWvE9tn97NY98uZDGZ+dwJrhYxfn+9LiyYwsuxeJAKfOmqowGfOulMWOVU6BrvyUMcAbBRySssNFOv0xGUXcTZVba5RsZLL6NXck6GRPtwf4XNL80zdCkkyDZ9Oyi9BXVJO1zB3XOzEg+1u8mdsFlNXR5vfu9pb8/XUbrQPdG28QglCHeUV6Xjw0wPmuQCv1czLgfXP9qw0UCK/uIxLWYV0vy7JYIWC4jI6v/EHRgn2zx1obh7Xlhvo985usgt1vDCoBf+6v+ZzQwr1qz6e300r9e8drn8rL7bHZDKmYwCPdw82d2CtikZbzuXsIpp5OTaJAEMmk+HtbFvjnDbCnee+1qbg+o/zWXg6Kln7dLd6GWklCI3Bw1HJfyd3ZdyKg2i0elp4O9I1zJ1uoe4MjvCpMrO9u4MNPZpV36HazcGGziFuHEss4M/YbCZd7cOzITqF7EId/i62zBgoZj6/04nApx4Na+vHfa29b5oDAsDZ1pqOVXSeE4SG9N64KDZEpzA00ldMMinc8Vr5OrFv7n0YjVK9pTEY1MaHY4kF7LpgCnzKDUZW7DHlHnq2f7ho1r8LNL0Z3+5gNlbyGgU9gtBYXOysebpvMxH0CLdEC4y4+tI2cllc7KzrNXfToNam3kuHLudRrNPz48k00lSleDoqebSG2ZmFpk3U+AiCIAi1YgB+vebnu0lzb0eC3e1Jzi9h76UcPv/LlB7kmb6V5y0T7kx3XI2PwWDgtddeIywsDDs7O8LDw1myZAn3YB9tQRAEoZ7JZDIGtTHV+rzxywUScotxsbPmcTFj+l3jjqvxefvtt/n8889Zs2YNkZGRREdH8+STT+Li4sLzzz/f2MUTBEEQ7nCDWvuw6kCieZLoJ3uHVtlZWrgz3XG/yYMHD/Lggw8yYsQIAEJDQ/n+++85evRoI5dMEARBuBt0C3PHSWlFoU6Po9KKKb1CG7tIQj2645q6evXqxa5du7h06RIAp0+fZv/+/QwbNqzafXQ6HRqNxuIlCIIgCFWxsZJz39Xmrid6huBqLya+vZvccTU+8+bNQ6PR0Lp1axQKBQaDgTfffJPHH3+82n2WLl3K4sWLb2MpBUEQhDvZgpER9GnuyYMdAhq7KEI9u+MyN//www+89NJLvPvuu0RGRnLq1Clmz57NBx98wOTJk6vcR6fTodPpzO/VajXBwcGkpKQ0mUlKBUEQ7hTFgP/Vn9O5/VNWCPcujUZDUFAQKpUKF5e6zWmJdIcJDAyUPvnkE4tlS5YskVq1alXjY6SkpEiAeImXeImXeImXeN2Br5SUlDrHEXdcU1dJSQlyuWXXJIVCgdForGaPyvz9/UlJScHJyanK+VruNRURtKgBa1jiPt8e4j7fHuI+3x7iPluSJInCwkL8/f1vvnE17rjAZ9SoUbz55psEBwcTGRnJyZMn+eCDD5g6dWqNjyGXywkMDGzAUt6ZnJ2dxX+s20Dc59tD3OfbQ9zn20Pc57/VuYnrqjsu8Fm+fDmvvfYaM2bMIDs7G39/f5599lkWLFjQ2EUTBEEQBKGJu+MCHycnJ5YtW8ayZcsauyiCIAiCINxh7rg8PkL9UyqVLFy4EKVS2dhFuauJ+3x7iPt8e4j7fHuI+1z/7rjh7IIgCIIgCHUlanwEQRAEQbhniMBHEARBEIR7xh3Xubk+GI1G0tPTRR4fQRAEQbiDXJvH5/qcfjV1TwY+6enpBAUFNXYxBEEQBEGog5SUlDrn47snAx8nJycAkQlTEAShDkqAbld/PgrYN2JZhPplNEqoSsvJL9ZhYyUn2L1pzcRWkcm64jleF/dk4FPRvCUyYQqCINSeAki5+rMTYpLSu8GxxHxeXH+adFUpeuPfg72fu685Lw5p1Yglq9qtdFMRnZsFQRAE4R73zvZYkvNLzEGPq701AMv/jGfFnsuNWbR6d0/W+AiCIAiCYHIqRcWxxAKsFTK2PdeXME8HbKzkfP7XZd7eHsv//RaLo9KKf/QIaeyi1gtR4yMIgiDcEWQyGYsWLWrsYtx1vtp3BYBRUf608nXCxsoUGkwfEM7MgeEAvPbTOX48mXrD4xiMEhczC1l3LJl5m85wPCnfYv2AAQMYMGBAncpYn7/7Bg189u7dy6hRo/D390cmk7FlyxaL9ZIksWDBAvz8/LCzs2Pw4MHExcXd9LiffvopoaGh2Nra0r17d44ePdpAVyAIgiAId6/UghJ+O5cJwNN9mlVa/+8hrZjcMwRJgpc2nOF0iqrSNin5JTy56ihRi3cwdNle5m46yw/HUtgXl9vQxa+TBg18iouLiYqK4tNPP61y/TvvvMPHH3/MihUrOHLkCA4ODgwdOhStVlvtMdetW8ecOXNYuHAhJ06cICoqiqFDh5Kdnd1QlyEIgiA0AaWlpbz66quNXYy7yuoDiRiMEn2aexLhX3mwj0wmY+GoSIa19UVvlPjXulOUlOnN60vLDDzzdTS7L+ZQpNNjb6OgRzN3/tk/nL4tPC2OtWPHDnbs2NHg13QzDdrHZ9iwYQwbNqzKdZIksWzZMl599VUefPBBAL7++mt8fHzYsmULjz32WJX7ffDBBzzzzDM8+eSTAKxYsYJffvmF//3vf8ybN69hLkQQBEEwkwER1/zckIxGI2VlZdja2mJra9vAZ7u3aLTl/HDMND7vqb5h1W4nl8tY+nA7TiaruJJbzFu/XuCNMe0AmL/+GLGZhXg6Kvnv5C5E+jtjpai6TsXGxqb+L6IOGq2PT0JCApmZmQwePNi8zMXFhe7du3Po0KEq9ykrK+P48eMW+8jlcgYPHlztPgA6nQ6NRmPxEgRBEOrGHoi5+qpJDp9FixYhk8mIjY1l/PjxODs74+HhwQsvvFCphl8mkzFr1iy+/fZbIiMjUSqVbN++3bzu+n4eaWlpPPXUU/j7+6NUKgkLC2P69OmUlZWZt1GpVMyePZugoCCUSiXNmzfn7bffxmg01vke1OS8V65cYdy4cbi7u2Nvb0+PHj345ZdfLI7z119/IZPJWL9+PYsXLyYgIAAnJyceeeQR1Go1Op2O2bNn4+3tjaOjI08++SQ6na7Ke7ZhwwYiIiKws7OjZ8+enD17FoCVK1fSvHlzbG1tGTBgAImJiQCsO5pCkU5PC29H7Aqu8MADD+Di4oK9vT39+/fnwIED5nO42tsQkfE7SW+P5H/b9jNo5MM4Orvw2YuPI5fBxxM6cHbPNnr17IG9vT1ubm7069fPoobn+j4+ZWVlLFiwgM6dO+Pi4oKDgwN9+/Zl9+7ddf691ESjjerKzDS1Kfr4+Fgs9/HxMa+7Xm5uLgaDocp9YmNjqz3X0qVLWbx48S2WWBAEQbgV48ePJzQ0lKVLl3L48GE+/vhjCgoK+Prrry22+/PPP1m/fj2zZs3C09OT0NDQKo+Xnp5Ot27dUKlUTJs2jdatW5OWlsbGjRspKSnBxsaGkpIS+vfvT1paGs8++yzBwcEcPHiQ+fPnk5GRwbJly2p9HTU5b1ZWFr169aKkpITnn38eDw8P1qxZw+jRo9m4cSMPPfSQxTGXLl2KnZ0d8+bNIz4+nuXLl2NtbY1cLqegoIBFixZx+PBhVq9eTVhYGAsWLLDYf9++ffz888/MnDnTfLyRI0fy8ssv89lnnzFjxgwKCgp45513mDp1Kjv+2MmqAwkAdLdNp3//KXTu3JmFCxcil8tZtWoV9913H/v27aNbN1O6ymAPU5ibu+X/OO4VgEOfJ1DqjfxrcEv+WPspixYtolevXrz++uvY2Nhw5MgR/vzzT4YMGVLlfdRoNHz11VdMmDCBZ555hsLCQv773/8ydOhQjh49SocOHWr9u6kR6TYBpB9//NH8/sCBAxIgpaenW2w3btw4afz48VUeIy0tTQKkgwcPWix/6aWXpG7dulV7bq1WK6nVavMrJSVFAiS1Wl33CxIEQRBqZOHChRIgjR492mL5jBkzJEA6ffq0eRkgyeVyKSYmptJxAGnhwoXm95MmTZLkcrl07NixStsajUZJkiRpyZIlkoODg3Tp0iWL9fPmzZMUCoWUnJxc6+upyXlnz54tAdK+ffvM6woLC6WwsDApNDRUMhgMkiRJ0u7duyVAatu2rVRWVmbedsKECZJMJpOGDRtmcfyePXtKISEhFssASalUSgkJCeZlK1eulADJ19dX0mg05uXz58+XAOm5ldulkLnbpI6Lf5eaN28hDR061Fx2SZKkkpISKSwsTLr//vvNyyp+j74dB0khc7dJIXO3SZP+e0S6ePGSJJfLpYceesh8XdffD0mSpP79+0v9+/c3v9fr9ZJOp7PYvqCgQPLx8ZGmTp1a6RoXLlwoqdXqW35+N1pTl6+vLwBZWVkWy7Oysszrrufp6YlCoajVPgBKpdKcpVlkaxYEQbg1JUDk1VdJLfarqI2o8NxzzwHw66+/Wizv378/ERER3IjRaGTLli2MGjWKLl26VFpfkdl3w4YN9O3bFzc3N3Jzc82vwYMHYzAY2Lt3by2uoObn/fXXX+nWrRt9+vQxr3N0dGTatGkkJiZy/vx5i/0mTZqEtbW1+X337t2RJImpU6dabNe9e3dSUlLQ6/UWywcNGmRRM9a9e3cAvNr142JeuXl5166m2pt1u48jk8HjLYzEx8cxceJE8vLyzPenuLiYQYMGsXfv3kpNgm+/+iK21nICXO348NEO/PzzTxiNRhYsWFBp4tAbZVhWKBTmfj9Go5H8/Hz0ej1dunThxIkT1e53qxqtqSssLAxfX1927dplrs7SaDQcOXKE6dOnV7mPjY0NnTt3ZteuXYwZMwYw3axdu3Yxa9as21RyQRCEe5sEnL/m55pq0aKFxfvw8HDkcrm5z0mFsLDqO9pWyMnJQaPR0LZt2xtuFxcXx5kzZ/Dy8qpyfW1HBNf0vElJSebg41pt2rQxr7/2GMHBwRbbubi4AFSaUNvFxQWj0YharcbDw6Pa/RW2pmaplDJ7HllxiEGtvZk9uCVrT+aYNtAW8f64KPTxBwGYPHlytdeiVqtxc3Mzvx/YtS0PDPbC1kaBs601ly9fRi6X3zRYrcqaNWt4//33iY2Npbz87wCtJn8DddWggU9RURHx8fHm9wkJCZw6dQp3d3eCg4OZPXs2b7zxBi1atCAsLIzXXnsNf39/c1ADpij2oYceMgc2c+bMYfLkyXTp0oVu3bqxbNkyiouLzaO8BEEQhDtDdbUBdnZ29XYOo9HI/fffz8svv1zl+pYtW9bbuW6FQqGo1XJT60/12+29ZMqhY2tthUIuY1dsNrtis9Emm5ZP7RPGw50C+eGSqTbn3XffrbZPjaOjo8V7Ozs7PJ1vfYTd2rVrmTJlCmPGjOGll17C29sbhULB0qVLuXy54abJaNDAJzo6moEDB5rfz5kzBzBFlqtXr+bll1+muLiYadOmoVKp6NOnD9u3b7cYsnj58mVyc/9OgvToo4+Sk5PDggULyMzMpEOHDmzfvr1Sh2dBEAShaYmLi7P4Jh8fH4/RaKy28/KNeHl54ezszLlz5264XXh4OEVFRRajgW9FTc8bEhLCxYsXKy2vGIgTEtKw0z/8cd40SKh7Mw8++Fc/PvjjEtvOZGB1tSmqc4ipBic83JSZ2dnZuc73KDw8HKPRyPnz52vVIXnjxo00a9aMzZs3WwTBCxcurFM5aqpB+/gMGDAASZIqvVavXg2Yov3XX3+dzMxMtFotO3furBR9JyYmVhq+OGvWLJKSktDpdBw5cqTK6kRBEAShabk+me3y5csBqs33diNyuZwxY8awdetWoqOjK62vqBEZP348hw4d4vfff6+0jUqlqtRXpr7OO3z4cI4ePWqRaqW4uJgvvviC0NDQOjUL1VR2oZboxAIAIv2daeblyCcTO7Hrxf68N669xbadO3cmPDyc9957j6KiokrHysnJuen5xowZg1wu5/XXX6/UH+j6mqlrVdRSXbvNkSNHbpiepj6ISUoFQRCE2yIhIYHRo0fzwAMPcOjQIdauXcvEiROJioqq0/HeeustduzYQf/+/Zk2bRpt2rQhIyODDRs2sH//flxdXXnppZf4+eefGTlyJFOmmIZsFxcXc/bsWTZu3EhiYiKenqYMw1OmTGHNmjUkJCTcsBaqJuedN28e33//PcOGDeP555/H3d3dfOxNmzZV6gRcn7aezjD3vXJz+DtpYLiXIykuls2Icrmcr776imHDhhEZGcmTTz5JQEAAaWlp7N69G2dnZ7Zu3XrD8zVv3pxXXnmFJUuW0LdvXx5++GGUSiXHjh3D39+fpUuXVrnfyJEj2bx5Mw899BAjRowgISGBFStWEBERUWUQVl9E4CMIgiDcFuvWrWPBggXMmzcPKysrZs2axbvvvlvn4wUEBHDkyBFee+01vv32WzQaDQEBAQwbNgx7e1PnXnt7e/bs2cNbb73Fhg0b+Prrr3F2dqZly5YsXrzY3IkYTP1S7ezscHV1veXz+vj4cPDgQebOncvy5cvRarW0b9+erVu3MmLEiDpfc038fCqtVtsPGDCAQ4cOsWTJEj755BOKiorw9fWle/fuPPvsszU6xuuvv05YWBjLly/nlVdewd7envbt2/PEE09Uu8+UKVPIzMxk5cqV/P7770RERLB27Vo2bNjAX3/9VatrqA2ZdKN6qLuURqPBxcUFtVothrYLgiDUUgl/T1lxnptnb160aBGLFy8mJyfHXLvSFPn4+DBp0qRbCsYaW0JuMQPf+wuFXMaR/wzC01HZ2EWqV/Xx/BY1PoIgCEKt2AOJjV2IehYTE0NpaSlz585t7KLcki0nTbU9fZp73nVBT30RgY8gCIJwz4uMjLzj53GUJImfrjZzjeno38ilaboaLXOzIAiCIAj150yqmsS8EuysFQyJqH42g3udCHwEQRCEWikFul59ldZg+0WLFiFJUpPu33OnyynU8dpPptxC90f44KAUDTrVEXdGEARBqBUjEH3Nz0Ljis8uZMqqY6QWlOJmb83Mgc0bu0hNmgh8BEEQBKGJ0WjLiU7M58iVfLILdYzrEkiv8Mo1Zgcv5/LPb46j0eoJ9bBn1ZPdCPN0aIQS3zlE4CMIgiAITcSZVBULforhTKoK4zXJZn48mUbfFp68NLQVkf4uHIjPZcPxVH47m4HeKNE5xI0vJ3XB/ZqEhULVROAj3FEkSSJNVcqZVDXx2UVczjG9MtU6gtztaOXjREsfJ/q38iLcy/HmBxQEQWgiruQUMfl/RykoMc1SHuphT/cwDxQKGRuiU9gXl8u+uFw8HGzIKy4z7zeyvR/vjYvC1rrqCU0FSyLwERqETm9g6a+xyGTwdN9mBLj+nSbdaJT49VwG289l0sbPmbGdAvF1MU1Mqy038PPpdL4+lEhSXgkBrnYEutkT4GpLulrLqRQVOYW6Ks+ZW6TjZLIKAJvf5LzxUFvGdwlq8GsVmqZyg5Hvjybz2e7LBLvbM31gOANaelU7I/i1LmUVsupAIn+czyTI3Z6BrbwZ2MqbCH9nMjVaLmcXcSWnCJ3eSGs/Z9r4OeHtdOuzVVeQJKnKckqSxF8Xc8jUaBncxgcvJ5Gn5W6RXahl8ipT0NM+0IUV/+iM/zWfm9P7h/Phzkv8eDKNvOIynG2teLBDAOO6BNIuwKVGf9eCicjcLDI31zuDUeK570/w61nT7MDWChnjugQxvX84p1JUfLwrjrjsv+dhkctgQCtvWng7svF4qsU3mapYyWW08XOmta8TzbwcCfdywNfFluT8Ei5lFnLoSh7Hrk7QN6VXKK+MaIO1QgxgvJNpyw01/jYrSRK7LmTz1m8XuJJTbLGubYAzk3qG4uWoRGktR2mlQCYzHV+nN6IqKWPT8TT2x+dWeWyFXIbBWPVHpqejkrYBznQOdqNTiBvtAl3IUGk5mVzAqRQV8dlF+LrY0tzbkebejvi52FFuMKLTG9GVG0hTlXI+XcP5DA1xWUUEuNkxsVsw47oE4mxrzY7zmXy0K54LGaZcM1ZyGfe19ubRrkG42ltzPqOQ8+kaUgtKeLBDAI90DqzFHa6dYqCiPrUIED1Kak6SJLadySBdVUqXUHfaB7qg0xt57ItDnEvTEOJhz6bpvapNPnglp4g0VSldQ93vyRqe+nh+i8BHBD71SpIkXtlyju+OJGOtkNEhyNUchFzLydaKcZ2DOJumqrTe38WWSb1CGdDKi0y1lpSCUtIKSvFwsKFjsCttA1xu+B/eaJT4+M84lu2MA6BHM3eWPNiWME8HrK4GQJIkkVpQyonkAjLUWvxcbAl0syfI3Q4vR2WT+/YkSRJJeSUcvpLHkYR8ruQU8Z/hbejezKOxi1YnBqPEmVQVp1JUnEw2/VtuMNIl1J0ezdzpHuZBQUkZf13MZndsDuczNIxo58f/jW2Hk611lcdMyivmj/NZ/HI2w1zz5+5gw3P3NSddVcq3R5IpKTPUqHxyGQyJ8GVC92DSVaX8dTGb/XG5FJcZsJLLCPV0oJmnA9ZWcmIzNFzJLaahPkmVVnL8Xe1IyDUFcY5KK0I97TmXduNke3Pub8lz9zVvkL/lYiD06s+JiMCnpjTacl7acJrfY7LMy+xtFLg72JB69TNu0/RehIrOydUSgU8d3WmBj05vIF2lRZIknO2scba1xkou42JWIYev5HH4Sh4x6RqUVnJc7KxxtrPGRiGnUKtHoy1Hoy2nlY8zb49th0cDpzD/YMdFPv4zHpkMPpnQiRHt/TiWmM9HO+PYH5+Ls60VT/VpxpTeobjYmR5gV3KKWB+dSkp+CSPa+zEkwsccoNyKHTGZ/GvdKYqvPuxsFHJCPe3xcbblQoaG3KKqa5Zc7KyJCnKlY5ArHYNdLZowZDII8bDH3saylTivSMefsdnkFOnoEuJOhyBXbKzkSJLE2TQ1285k8GdsNvY2CsKv1lKFeznSwseREA8Hc41UsU7PmVQ1p1JUJOcXk63RkV2oI0NdWqm8no42/Pp8X7yd66+J5Xa4klPEc9+fJCa99llym3k6sOKJzrT0cQJMuUvWHUtm6+kMLmYVmrezsZIztXcYMwaG43w1UCooLmPVwUQOXc5FW25EpzfV8hglCVsrBUprObZWCjqFuPFEjxCC3C1noCrTG8nSaPF1sa1Ug1hSpic2s5DTKSpOJKs4kVRAmqoUexsF7QNd6BjsRisfJzI1WuKzi4jPLiKvWIfSSoHSSo7SSo67g5IIf2ci/Jxp4ePI0YR8vjmUxPmrNTxOSium9A7lqT5huNrbcDGzkA3RKfx8Oh2ASH9nIvydKdYZWH0wEYCpvcN4dUQb5PKaBz9pqlL+iMnkjwtZHEsswM/Flkh/ZyL9XegS4ka3MPcm98XgTnA+XcOMb4+TmFeCjUJO7+YenEpRmfvz2Fkr+GFaD6KCXBu3oE2cCHzqqKkHPpeyCtl0IpXjiQWkFJSQpancp8VKLkNfTZV7dVp4O/LtM93rtS9ChQx1Kf/dl8BX+xMAeGNMW/7RI8Rim0y1Fidbq9uaWCsuq5BXt5zjTKqa0nLLb/vWChkR/i6EetiTodaSml9ChkZ702/uCrmMCD9nOgW74u1sy56LOUQn5VuMwLC1ltM5xI2U/FKS80tueDxrhYwwTwfkMhmXsgqp7tdqo5DTIciVHs3c2XE+i9jMQnqFe/DNU91R1OLB1ph+PJnKKz+eo6TMgKPSim5h7lcDTDfkcjhyJZ8jCXmcSFZhZ62gf0svBrTywtNRydxNZ8hQa7GzVvDikJacSVXz27kMyg2mG6aQy+ge5s79ET4Ma+tn7jfWWFQlZTjZWt/S70aSJE6mqEjMLWZQax9c7Kuu7bre//Yn8Pq28wCMaOdHoJvd1YEAxWjLDXQKdqNHM3d6NPPA1lrBieQCTiQVcDSxwNyUVp2J3YNZPDryhs3H+cVlxKSriQpyNQee9wpJkvjjfBYbj6dSZjBeXQaHr+Sh0xsJcLXjs8c7ERXkitEoEZddxPGkAtoHutA2wOUmRxdE4FNHtzPwqa6T4rWMRonEvGL2xeWy6UQqZ1LVlbaxs1ZgpZBRqNWbl9nbKMxNA52C3ZAkUJeaanjK9MartUOmIGPeprNkarSEeTrw7dPd8Xe1o7TMwJ+x2UQn5dMt1J0hkb7VfkjnF5dxOaeIpLwSrOSyqzVLVhQUl/PDsRT+jM0yP7Dn3N+S5we1qOMdaxhGo0S6upT47CKyNFpa+DgR4edcqclMpzdwKbOIkykFnEpWcSpVRdE197zMYER19Rva9doGOBPsbs/RhHyL2hlbazmD2vgwop0fchlczik2PYSufvMvvq75JcDVjg5BrrTwccTbyRZvJyXezkpaeDthZ2Mq7+WcIkYt309JmaFJ3G9tuYFDV/IoKC7Dy0mJt5Mtno42lJQZyCnSkVOo4/dzmWy+OoFij2bufPRYR3yqqa3SG4zIZTKLmoq8Ih0v/HCqUv+bjsGuTOgWzJAIH1ztxVDeCpuOp/LypjPV9kmqjkwGXUNMAWTflp7kFpqCmDNpan49m4EkQa9wDz57vJPF/dbpDeyOzWbTiTT+uphNuUHCSWnFxB7BPNU77I6rmayLyzlFLN56nr2XcqpcP6CVFx+O74CbGHJeZyLwqaOGDHyMRonTqSp2nM/ij/NZJOQW0yHIlb4tPOnbwgullZzUglJSC0pIyS/hfIaG8+kai4eflVzGgFbeDG/nSzMvR4Lc7HB3sEEmM3WsLNLpKdLp8XZS1rjTbnJeCRO/OkxqQSmBbnZ0CnZj54Usiz4PzTwdeKZfMx7qGEBcVhF743LYH5dLbKbGXB17Iz2auTO5ZygPtPW9q6vC01WlHE8qMPUPUmnpGe7B4Agf88g1STJ9izuSkI+bvTUDW3lXW8slSRLpai2Xsgop1xuJCnKtNhi43o8nU/nXutPIZfDdMz3oUc/9fTTacj7/6zKBbnY83j2k0vpinZ7fYzL543wWey7l1Kj/jFwGLwxqyaz7mtepJsRglFi28xLfH01hUGtvnugZIr4l38DeSzmsj07B01Fpbl6Vy2UcS8jncEIex5MK0BskIgNc6BTsSucQN3o086i2Y+2uC1k8//1JU18nTwfch7dhYlYhx6+YjnXt34C7gw35Vwcq2CjkjO0cwJz7W92VI9FKywws23mJ/x1IoNwgYaOQM6V3KC28/06p4emopH9Lr1o1OwqVicCnjhoq8Fm55zL/3Z9AdjXDrW9EaSUn0t+ZEe39ebCDf7UfPLciXVXKxC8Pk5j3d9NLoJsd3cM82HkhC3WpKbipbuRKgKsdoZ6mfg/q0nI0pXoMRokH2voyoVswzb1F3pzb7aUNp9lwPBUnpRXh3o7mWj43exu8nJSml6OSjsGuVfbvKi0zkJRfTLiXo0UQHZ2Yz+x1p0gtMM3EtPThdkzoFmxery4pZ+yKg8RfMzrPz8WWZl4O5BSaangKSsqxsZLj5Wgqh5+LLVN6hd6xHbLvRuUGUx8npVXNRwddyNAwdU00GarKs3T5udjyYIcAHu4UQHMvR3ZfzGbFnsvmAQzuDjYsebAtI9r71ds11Kea1NBf71JWIbO+O8GlLNP/hftae7NgZITooNxA7orAJzQ0lKSkpErLZ8yYwaefflpp+erVq3nyySctlimVSrRabY3P2VCBz6e743n394s4Kq3o38qLIRE+RPo7E51YwN64HA5dzkMhlxPgZkfg1VcrHyci/V0I93Kolw69N5Ot0fLmrxfwdFQyKsqfqEBT/ocinZ4fjibz1b4EMjVaHJVW9Az3oF8LTzoGu9HMy6FSh16h8ZWU6Rn7+aGb9suws1bwTL9mTOvXDEelFWV6Iz8cS2b5n/HkFOrwcLBhRHs/Rkf5sy8ul+V/xmGUTKPvCrV6rOQy1kztRu/mnpTpjUz+31EOXcnD01HJhG5BDInwpW2As8VDQ28wopDL7urav3tVUqGO7t+fxDq7iAdC3egd5k6PcA9aejtVWaMRnZjPaz/FmP9OR7b34/UH2zapLMPfHkni7d9ieWVEGx7tGnzT7SVJ4odjKSzeGoO23IiXk5KlD7VjcITPbSjtveuuCHxycnIwGP6uHj137hz3338/u3fvZsCAAZW2X716NS+88AIXL140L5PJZPj41PyPraECnzRVKXFZhfQM96jVN6impExvJDm/hBAPe5H75g5RpjdyMrngav8uPerSclQlZeaal4S8YnM+G09HG8Z3CWLrmXRS8k3f2Kur4Xu4YwCLHoxkwZZzbDmVjpOtFT/O6MUXe6+wPjoVBxsFG6f3oo1f0xsgIDSsuuTxKdMb+eTPOD796zIGo4STrRVjOwUysXuweZReY5Akic/+usy7v5ueKe0DXfh5Vp8b7pNfXMaCn86x7UwGAH1bePLB+A53ZTNeU3NXBD7Xmz17Ntu2bSMuLq7Kb4qrV69m9uzZqFSqOp+jqY/qEoT6JEkS289l8s7vF825YAC8nJQ8f19zHukcxNHEfH46mcbvMZnIZTLeeKgtD3YIAEwdlx//6gjHkwpwVFpRpNMjl8F/J3dlYGvvxrosoRHdSgLDs6lqXtp4mtjMv9MPdAlxIyrI1ZRioNxImcFoMbqyYoDAwFbe2FjV3xcyo1HirV8vmEejgqlz9/FX76+yNkqSJH45m8HCn2LIKy5DIZfx7yGteLZfM9F35za56wKfsrIy/P39mTNnDv/5z3+q3Gb16tU8/fTTBAQEYDQa6dSpE2+99RaRkZHVHlen06HT/d3vRqPREBQUJAIf4Z5SbjDyw9FkNp9MY3AbH57sHVqp+VJbbsAoSVXmKRrz2QFzLdHrD0YyqWfo7Sq60MTcauZmo1Fif3wu3x1J5o8LWTUeeebuYMPoKH+GRPigNI/IlCjU6k01nEU68opMIwsrcg9V15x2IUPDh39cYsd5UzLB10ZGsPF4KhcyNHz0WAdz4F8hW6Pl1S3nzNu39HHknUei6CDy7txWd13gs379eiZOnEhycjL+/v5VbnPo0CHi4uJo3749arWa9957j7179xITE0NgYNUp2hctWsTixYsrLReBjyDUXHx2IXM3neW+1t7MHNi8sYsjNKL6nLIiS6Plp1Np5BeXo7SSY2utwFohQ35NjX+6qpSfT6fXaeCIv4utKSFpsClflN4g8cXey+y+aBpyLpfB22PbM65LEEt/u8DKPVd4uFMAH4zvYD6G0SgxZNle4rOLsJLLmDGwOTMHht+xXRruZHdd4DN06FBsbGzYunVrjfcpLy+nTZs2TJgwgSVLllS5jajxEQRBqD/FQEUjZza3Z8oKvcHIvvhcNh1P5VyammsfXPY2Vng7KfF0VOLuYE26SktMutpiBOv15DIY1s6P6f3DzSkRDsbnMvGrI3g5KTn6n0Hm7hYVy52UVqz/Z0/Rr60R1Ufg02SG6SQlJbFz5042b95cq/2sra3p2LEj8fHx1W6jVCpRKkWnM0EQhPrggCn4uZ2sFHIGtvJmYKua9yvTaMuJSdNwKkXFqZQCTiar0GjLebhTINP6Nqs05LxzqBt21gpyCnVcyCgkwt/0YN14IhWAkVH+Iui5CzSZwGfVqlV4e3szYsSIWu1nMBg4e/Ysw4cPb6CSCYIgCHciZ1treoZ70DPclDuqooGjuhQLSisFPcM9+DM2m71xOVfnPtOz/VwmAI90DqhyP+HO0iTGKxuNRlatWsXkyZOxsrKMxSZNmsT8+fPN719//XV27NjBlStXOHHiBP/4xz9ISkri6aefvt3FFgRBEO4gMtnN80r1a+EJYJ524rdzmZSUGQjzdKBTsFuDl1FoeE2ixmfnzp0kJyczderUSuuSk5ORy/+OzwoKCnjmmWfIzMzEzc2Nzp07c/DgQSIiIm5nkQVBEO5ZWmDs1Z83AXfTLFz9WnoBEJ1YQLFOz6bjpmauhzsGiGScd4km1bn5dhF5fARBEOquPkd1NTWSJNH3nd2kFpSy5MFIXvspBoD9cwcS6GbfyKUT6uP53SSaugRBEAShKZDJZOZan7e3m7I592zmIYKeu4gIfARBEAThGv1amAKfIp0egLGdq84RJ9yZROAjCIIgCNfo1dwDxdUpKOxtFAxr69vIJRLqkwh8BEEQBOEazrbWdAp2BeCBtr44KJvEOCChnojARxAEQRCuM3twS/o09+T5+1o0dlGEenZPhrEVA9k0Gk0jl0QQBOHOc23WZg1gaKyCNKB23jZ8Nr4NYBDPiiak4ndxKwPS78nAp7CwEICgoKBGLokgCMKdrerppAWhYRUWFuLi4lKnfe/JPD5Go5H09HScnJxEQir+nrQ1JSVF5DVqQOI+3x7iPt8e4j7fHuI+W5IkicLCQvz9/S2SG9fGPVnjI5fLCQwUwxOv5+zsLP5j3QbiPt8e4j7fHuI+3x7iPv+trjU9FUTnZkEQBEEQ7hki8BEEQRAE4Z4hAh8BpVLJwoULUSqVjV2Uu5q4z7eHuM+3h7jPt4e4z/XvnuzcLAiCIAjCvUnU+AiCIAiCcM8QgY8gCIIgCPcMEfgIgiAIgnDPuCfz+IgEhoIgCIJw5xEJDOsoPT1dTFchCIIgCHeolJSUOicivicDHycnJwCRAlwQBKEOSoBuV38+Ctg3YlmEhrEhOoUfjqUwvJ0v47sE4WRr3dhFAv6ewqPiOV4X9+Rwdo1Gg4uLC2q1WgQ+giAItVQMOF79uQhwaMSyCPXvj/NZTPsmmorowNnWikk9Q5ncKxQvp8bNJ1Qfz28R+IjARxAEoVZE4HP3iklXM27FIUrKDAxq7U1iXjGXc4oBkMmglY8T3cPc6RbmQf9WXjgqb2/DUX08v8WoLkEQBOGuIpPJWLRoUWMX47YJDQ1lypQpN91u9erVyGQyEhMTAbiYWUhqQYl5fbZGy9NroikpM9CnuScrnujMH//qz4p/dCIqyBVJgtjMQtYcSmLmdycY+fE+NNryGpdz0aJFdR5QNGDAAAYMGFCnfa93T/bxEQRBEIR72Z5LOUz+31EAAlzt6B7mzsWsQjLUWsK9HPj08U5YK0x1Iw+09eOBtn7kFOo4lpjP0YR8tp3JIDGvhIU/xfDhox3Mxy0pKeGdd96p10ClvonARxAEQbirlJaWYmV17zzeLl68WOuh3asPJJh/TlOVsvlkGgBu9tb8b0pXXOwqd2b2clIyvJ0fw9v5MSrKj3ErDvHjyTQGtvZmdJQ/YAp8Fi9ezLk0Nb379jMHTwCvvvoq8+bNq8sl1ivR1CUIgiDc8YxGI1qtFgBbW9smG/gUFxfX+zGVSiXW1jUfdZWp0bLnUg4Avzzfh7VPdee5+5ozNNKH/03pSojHzXttdQ5xZ9Z9LQB49cezpKtKATiakAfAzgtZrDmYaLGPlZUVtra2NS5nQxGBjyAIglArMiDi6qs+U8BW9AGJjY1l/PjxODs74+HhwQsvvGAOasxlkMmYNWsW3377LZGRkSiVSrZv325ed30fn7S0NJ566in8/f1RKpWEhYUxffp0ysrKzNuoVCpmz55NUFAQSqWS5s2b8/bbb2M0Gm/pes6fP8/EiRNxc3OjT58+5vVr166lc+fO2NnZ4e7uzmOPPUZKSorFMeLi4hg7diy+vr7Y2toSGBjIY489hlqtNm9TVR+fmJgY7rvvPuzs7AgMDOSNN94wX8cvp9MxStCjmTuR/i4Uxh9jy5KnWfvP/vSLDGbEiBHExMRYHG/KlCk4OjqSlpbGmDFjcHR0ZOnE3lgdW4u6RMfsH07xxEfbGNGtNQDqA9/zTL9wi99FVX18Vq1axX333Ye3tzdKpZKIiAg+//zzOt3vmmqaIbEgCILQZNkDMTfdqu7Gjx9PaGgoS5cu5fDhw3z88ccUFBTw9ddfW2z3559/sn79embNmoWnpyehoaFVHi89PZ1u3bqhUqmYNm0arVu3Ji0tjY0bN1JSUoKNjQ0lJSX079+ftLQ0nn32WYKDgzl48CDz588nIyODZcuW1fl6xo0bR4sWLXjrrbeoGEj95ptv8tprrzF+/HiefvppcnJyWL58Of369ePkyZO4urpSVlbG0KFD0el0PPfcc/j6+pKWlsa2bdtQqVS4uLhUeb7MzEwGDhyIXq9n3rx5ODg48P/t3XdcVfX/wPHXHXDZSzayRHHgXkhuxdScqWk21LK0nGVl2q9UWmaZTcuvDS1HmZaWZo7cAzfuBYKCIBvuZV4u957fH+jVGzhANp/n43Ee3nvu55zz+RyB876fuXTpUiwtLQH4+8wNkDswuqMPK1asYOzYsfTt25cFCxaQm5vLt99+S5cuXYiIiDC5p3q9nr59+xIcHMzChQv5999/+f33X3FX1eOIvD+GAi31+k4ibes3DBw8hFFPjACgZcuWd7033377LUFBQQwePBilUsnGjRuZNGkSBoOByZMnl/me35NUB6nVagmQ1Gp1VWdFEARBuGnu3LkSIA0ePNhk/6RJkyRAOnXqlHEfIMnlcuncuXPFzgNIc+fONb4fM2aMJJfLpaNHjxZLazAYJEmSpPfee0+ytraWLl++bPL5rFmzJIVCIcXGxpa5PKNHjzbZf/XqVUmhUEgffPCByf4zZ85ISqXSuD8iIkICpLVr197zOr6+vtLYsWON71955RUJkA4fPmzcl5ycLNnb20uA5PXSD1KrsK1SSnqm5ODgIL344osm50tMTJTs7e1N9o8dO1YCpHfffdckbZs2baQGTVtI/rM2SSO+PSDtPxNd7P7/937cKTc3t1i6vn37Sg0aNDDZ1717d6l79+7l8vwWTV2CIAhCtfLfb/pTp04FYPPmzSb7u3fvTrNmze55LoPBwIYNGxg0aBDt27cv9vmtppe1a9fStWtXHB0dSU1NNW6hoaHo9Xr27t1b5vK89NJLJu//+OMPDAYDI0eONLmWu7s7jRo1YteuXQDGGp2tW7eSm5tb7Lx3s3nzZjp16kTHjh2N+1xcXHj66aeN7x9v48W+3TvJzMxk9OjRJvlQKBQEBwcb83GvsnTt2pWMxOucmdeX3yaG0Ni9dDMq36qFAlCr1aSmptK9e3eio6NNmvPKk2jqEgRBEEolF+hw8/VRyn/JikaNGpm8DwgIQC6XG+efucXf3/++50pJSUGj0dC8efN7pouMjOT06dO4uLiU+HlycvJ9r3U3/81nZGQkkiQVK+cttzoq+/v7M2PGDBYtWsSqVavo2rUrgwcP5plnnrlrMxfAtWvXCA4OLrbf07eB8fXojj5s+OlvAHr16lXief47QaCFhUWx++Po6EhGRgbWZZzI8MCBA8ydO5fw8PBiwZ1arb5nOctKBD6CIAhCqUjA+TteV7S7TXp3Z23BwzIYDPTp04eZM2eW+HlgYGCZz/3ffBoMBmQyGf/88w8KhaJYehsbG+PrTz/9lHHjxvHnn3+ybds2pk2bZuz7VNpFOk/GZQLQ3MueQDdbY2fnFStW4O7uXiz9f0fGlZTXh3HlyhV69+5NkyZNWLRoEd7e3pibm7N582Y+++yzMncqvx8R+AiCIAjVSmRkpEktSVRUFAaD4a6dl+/FxcUFOzs7zp49e890AQEBZGdnExoaWuprlFZAQACSJOHv7/9AAVWLFi1o0aIFb7/9NgcPHqRz584sWbKE999/v8T0vr6+REZGmuzTFuo5cPw0AINaehjzAeDq6lpu5S7NzMwbN25Eq9Xy119/4ePjY9xfUhNbeRJ9fARBEIRqZfHixSbvv/rqKwD69+9f6nPJ5XKGDh3Kxo0bOXbsWLHPpZujrEaOHEl4eDhbt24tliYzM5PCwsJSX/tuhg0bhkKhICwszHj9O/OTllY0F45Goyl23RYtWiCXy9FqtXc9/2OPPcahQ4c4cuSIcd/bvxwk8cQOAHo1cQWgb9++2NnZ8eGHH6LTFV96IiUlpdRls7IqavjMzMy8b9pbNUh33gO1Ws2yZctKfd3SEDU+giAIQrUSExPD4MGD6devH+Hh4axcuZKnnnqKVq1alel8H374Idu2baN79+5MmDCBpk2bcuPGDdauXcv+/ftxcHDgjTfe4K+//mLgwIGMGzeOdu3akZOTw5kzZ1i3bh1Xr17F2dkZKJrT5qeffiImJqZMtVABAQG8//77zJ49m6tXrzJ06FBsbW2JiYlh/fr1TJgwgddff52dO3cyZcoUnnjiCQIDAyksLGTFihUoFAqGDx9+1/PPnDmTFStW0K9fP6ZPn871LAPLf/wepZ0LupQcLM2LHv12dnZ8++23PPvss7Rt25Ynn3wSFxcXYmNj+fvvv+ncuTNff/11qcpmaWlJs2bNWLNmDYGBgTg5OdG8efMS+1g9+uijmJubM2jQICZOnEh2djbfffcdrq6u3Lhxo3Q3tRRE4CMIgiBUK2vWrGHOnDnMmjULpVLJlClT+OSTT8p8Pi8vLw4fPsw777zDqlWr0Gg0eHl50b9/f2MNhZWVFXv27OHDDz9k7dq1/Pzzz9jZ2REYGEhYWJhJJ9vs7GwsLS1xcHAoc55mzZpFYGAgn332GWFhYQB4e3vz6KOPMnjwYABatWpF37592bhxI/Hx8VhZWdGqVSv++ecfOnXqdNdze3h4sGvXLqZOncr8jz5Cb2aDdet+9GrbmPVfvGOS9qmnnsLT05OPPvqITz75BK1Wi5eXF127duW5554rU9m+//57pk6dyquvvkpBQQFz584tMfBp3Lgx69at4+233+b111/H3d2dl19+GRcXF55//vkyXftByKT/1rPVAeWxrL0gCEJdlQPc6n6bDdx/gYMHM2/ePMLCwkhJSTHWrlRHbm5ujBkz5qGCscqg0xt4Ykk4J+Myae3twNqXQkzWzqqJyuP5XaF3YO/evQwaNAhPT09kMhkbNmwwfqbT6XjzzTdp0aIF1tbWeHp6MmbMGBISEu55zltTXt+5NWnSpCKLIQiCINxBBvje3MpzyYqa4Ny5c+Tl5fHmm29WdVbuKS49l9d+O8XJuEzsLJR8NbpNjQ96ykuFNnXl5OTQqlUrnn/+eYYNG2byWW5uLidOnOCdd96hVatWZGRkMH36dAYPHlxiB7Q7BQUF8e+//xrfV9fF6ARBEGojK+BqKdJLkkRKthZX26pfoLI0tIV6VErTIdxBQUFoNJq7HiNJEvsiU9kXmcKYED+8ncp7lqN7OxGbwQ/7Yvjn7A0MN9tzPh7RqtLzUZ1VaMTQv3//u/bCt7e3Z/v27Sb7vv76azp27EhsbKzJ0Lb/UiqVJc45IAiCUNdpC/WYK+SlGlZckQr1BiasOM7Oi8nM7NeYST0aVnWWHsjSvVdYsOUSX49uQ/8WHvdNL0kSOy4k89WuKE7dnC/n79M3WDMxpFKCjrwCPf+34Qx/nIg37uvayJmXugfQuWH1bTasCtWqqkStViOTye7bYSwyMhJPT08sLCwICQlh/vz59wyUBEEQaiNJksjT6YmIzWRfZCr7o1I4l6BhaGsvPn2iFXJ51Qc/H2+9xM6LRbMef7zlEuYKOS90bVBi2nnz5hVbVb0qSJLE8gNX0Rsk3vnzHJ0bOWNnYVZi2vjMPP45c4N1x69zMTELAAszOQ6W5iSo8xn93SF+mxiCp0P5Tbb4X9fScpi44jgXE7OQy2BY2/q80NWfJu6iD2tJqk3gk5+fz5tvvsno0aPv2WEpODiY5cuX07hxY27cuEFYWBhdu3bl7Nmz2NqWvEaIVqs1mfPgXtWUgiAI1dmi7ZfZEBFPVr6ObG0hOn3x8SnrI+KxtzRj7qBmxpofbaGeRdsvcz5Bw9xBzWjoavr3UpIkTsRm4FfPmno2qnvmIQ/odvP1XuBuj/QNEfEs3RsNwKPN3Nh2Pon3/76AuVLOmBC/By/0XUiSxF+nEnCyNqdro5KXmiiLiLhMEtT5AKRma/l8eyRzBt1eE0ySJH47FsevR+OIiM007rc2VzDmET/Gd/FHb5AY9b9wrqblMvq7Q6yZEIK7vQWSJKHO02FprijWjFYWOy4k8cqak2TlF+JsY85Xo9sSElDvoc9bm1XaqC6ZTMb69esZOnRosc90Oh3Dhw/n+vXr7N69u1Q9tTMzM/H19WXRokWMHz++xDS3Rgr8lxjVJQhCSeLSc9lxIYlj1zJws7MgyNOO5l72NHC2xiBBgd5AQaEBa1X5PLwe1PFrGQz/9mCx/a62Kro0cqZrI2fyCgy8tf4MALP6N+Gl7gHEpecyefUJTl8vWvTRRqXk81GtCW3mBhQ93Gf9foZ/LyThYGXGJyNa0efmZyV5kFFdZ66rGbHkINpCA5N6BPBG38Ys3HaJxbuuAPBS9wCcbcwp0BvQFUrUd7SkayNnXO1M+wEZDBIFegMWZsXv8+JdUXyy9RJmChn73+yFm1359CF6b9N5ftgfQ4CLNVdSclDIZfw9rQtN3O0wGCTe3XSe5QevAiCTQQc/Jwa08GBIa08crMyN50nIzGPU0nDi0vNwsVVho1JyQ51Hvs6Al4Mlf0/rYpK+tP46lcC0XyIAaOvjwDdPt8Pdvmb1oyqt8hjVVeWBj06nY+TIkURHR7Nz507q1St9pNqhQwdCQ0OZP39+iZ+XVOPj7e0tAh9BqCKFegNX03JNZmx1tlHhaF32h8DDKig08O3uK2w6nUBkcvYDHWNroWTOwGaMaFe/wvvUSJLEsG8PEhGbyeBWnkzp1RAblRIbCyW2KqXJ9b/fF837f18A4LnOfvx+/Dqa/EIcrMzwrWdt7IMyo08gTdxtmf3HGdJyCkyuNzbEl9mPNS0x4Lhf4BOVnMWYH46QoM6nZ2MXvh/bAYVchiRJfLj5At/ti7lrOZu429KpQT0ycwuISsnmSnIOBXoDz3byZWa/xljdnHxv1eFr/N/628tQTOoRwMx+Dz/C12CQ6LJgJwnqfJY+2471EfH8czaRjn5OrHoxmFm/n+H3E9cBeDU0kNEdvYsFa3eKS8/lyaWHiM/MK/bZsDZeLBrVukz5jErOYvDXB8gt0PNkB2/eHdIcc2XtH7VV4wOfW0FPZGQku3btuuuquPeSnZ2Nj48P8+bNY9q0aQ90jJjHR6ipUrO1fL0zin8vJDFvUJDxG3tNsvdyCnP+PMvVNNOVmJVyGaFN3Xi6kw+dA5wrtX+KJEm8tf4MvxyJA0Ahl9He15EuDZ1JyyngXIKacwkacgv0JR7fL8idD4e1wKkCA7eNpxKY+ksEVuYKdr3e4761Gx9uvmBsZgJo4+PA10+1xdVWxXubzvNz+DWT9E3cbVkwvCUbTyXw/f4Y474lz7TDz9k0tCkp8NEW6tl6LonVh69xKDodgAbO1qyf3Bl7y9v9YyRJ4qeDVzkck465Uo65Qo5CLuNcgoYz8ep7lsmvnhULn2hFkkbLlF9OIEnQpaEz+6NSsbNQEj67d5lXCb/lVq2ajUrJsbdDScspIPTTPeTp9DRxt+ViYhYKuYyFT7Tk8TYPtkioOldHeHQqDlbmeNhbkHiz749Bgu/HtC/173FuQSFDvj5AZHI2jwTUY8X4YBTVoD9XZaj2gU92djZRUVEAtGnThkWLFtGzZ0+cnJzw8PBgxIgRnDhxgk2bNuHmdvs/3snJCXPzoj8gvXv35vHHH2fKlCkAvP766wwaNAhfX18SEhKYO3cuJ0+e5Pz58w8cOInAR6hpsrWFfL8vmu/2RpNz8+HrYGXGtle63fPbZnWSqM7nvb/P8/fpoqnoLczkWN6sTZCAzNzbawX5OFnxYld/ng72rZQA6If9Mby36TwyGbw7pDmDW3pib2XamdVgkEjPLcBMLsdcWfSw/m5fNJ9tv0yhQcLFVsWHj7cgtKlrudf+5Ov09P50D/GZeczoE8i03o3ue4zBIDHrj9OsO36d5zv7M7NfE5MagV+PxPLOn2cpNEi82LUBM/oEGmt3dl9K5vW1p0jNLsDT3oJ1Lz9i0jn3v4FPljqfkf8LJza9KJiVy6BnY1fmDgrCp96Dj2hKy9ayPyqViNhMXGxVNHS1oaGrDbHpucz+/QyJmnxkMlDIZBQaJJ4K9uG9Ic0JXbSHmNQc5g5qxnOd/e9/oXt4d+N5fjwQw9DWnnz+ZBvgdpMagLlCzldPtaFv0MONLJ6/+QL/2xuNq62K7a92L/bzdjeSJDHjt1Osj4jH1VbF39O64mJ77z5ZtUm1D3x2795Nz549i+0fO3Ys8+bNM1l99067du2iR48eAPj5+TFu3DhjT/8nn3ySvXv3kpaWhouLC126dOGDDz4wrjL7IETgUznUuTriMnLRFhb1h8gv1BOdksP5BA3nEtTEpucyuWdDJvesGcNbK9Pqw7EciUkjLaeAtOwC4jJyycovWqywZX17tDoDl5Ky6N3Ele/Htq82Q5dvic/MI+yvc8Sm51JQaEBbaCA1W4u20IBcBmMf8WNGn0Bs7xgpczFRw+rDsaw/EU+WtqisHfwc+WREq2I1DuVp18Vkxv90FIMEbw9oetcRR3dzNl7NK2tOEnWzeay5lx0vd29Iv+bu5fYt/NvdV1iw5SIe9hbsfK0HluYP3q8ot6DQ2Dz0X1dTi5qRAt2KDwxJ1hTVSlxJySHAxZq1Lz1irNG6M/BJ0hby3P/COZegwdlGxVPBPjzZwbvcRzGp83S8v+k8a48XNTMNaOHBl6PboJDLjM1e9R0t2f16D5RlnKjPYJDovGAnN9T5fDemvbGfk7ZQzxNLwolJyeHbZ9rRpdHDDw/P1+l57Mt9RKfkMLxtfT4dWXwdMk2+jnc3nmfzmRvYW5rhbm+BjUrJvshUFHIZq18IJrhB3erIXO0Dn+pKBD4Vy2CQWH7wKh9vvUi+znDf9B883pyng30rIWfFnUtQcyUlh+6NXO75jSu3oJBNp26w7Xwig1t7MbiVZ4Xl6URsBsO+Kd6B1a+eFW/0bcJjLdy5nJTNoK/2U6A38PGIloxs711h+SmtjJwCRiw5yJWUnGKftfFx4P2hzQnytC/hyCK5BYWsORrHwq2XyCnQY2EmZ2bfJox7xK/ca38uJ2Ux7JuDZGsLebKDN/OHtShTEJmv0/PZv5f5+eA18nRFNXL+zta80bcx/Zu7P1RgmpKlpefC3WRrC/lsVKsHbl4pDwmZeYz49iAJ6nxa1bdn1YudsFEpbwc+BonnVh5n5/kk6lmbs2Fy5wqfs2ZfZAqXErN4NsTX2LE8X6fnkY92kp5TwNdPtWFgy7L9fh6/ls7wb8OxVSk5+naoSf+mgkIDBkkqsc9TWR2/ls6IJeFIEnzxZGsGtfQ0/owfiUnn1TUnS+wbBLc7rtc1IvApIxH4VJyrqTnMXHeaI1eL2vidbcyxMldippBhrlTg42RJMw97mnnacexaOv/bE41cBt8+0+6hq45L4/i1DBbvijLOL2JlruCJdvV5rrM/fs7WxmaN2PRc/oyI5487aiEA3ujbmEk9AiqkpmXiimNsPZdE10bODGntRT0bc1xsVDR2tzWZcn7Jnit89M9FbFVKtrzaDa8KnCfkQeUV6Hn6+0OciM3Ew96CDx5vjrW5EnOlHGuVkoYuNg8cvMSl5/Lm76c5eCUNgF5NXPlqdJuH7sOhztXx74Uk/jmbyN7IFAoKDQT7O7FifPBDdw5Nzylg+cGr/HTwKuq8oqa7R5u58d7Q5mUacZSclc+ra05yICqNlvXt2TCpc6XPzROVnM0TSw6SkaujUwMnZvRpTKCPA40Ucsz+uYD5nmjMFXJ+mRBMO1+nSs3bnT7/9zKf/xtJq/r2bJjcuUy/m2Ebz7HswFUeb+PFZ2XsdFxa7286b+xT5WhlRpdGLthbKll1OBZJAm8nSz58vAW2FmYkqvNIVOdjrVIyvG39ajFPU2UTgU8ZicCnYvxyJJawjefI1xmwNlcw+7GmPB3sc9c/QJIkMev3M6w5FodKKWflC8F08KvYP5ynr2eyYMtFDkQVPUzlMqjvaGXsmyCTgae9JSlZWgr0prVVPk5WtPCy5+8zRX1UnunkQ9jg5uXaqTA6JZvei/YgSbD91W40KqEJ4ha9QeKJJQc5EZtJcy87gjzsScspID1Hi14CW5XSOOpnaGuvcqmev5dCvYGXVh7n3wvJ2FkoWffyIyU2oZSGJEmsOhzLe5vOoy000MzDjh/HdSjTkN18nZ73/z7Pr0fiKDTc/rPXsr49Pz3XsVxHlGVrC1m65wrf7L5CoUHC1kLJW4815ckO3g/8QP779A3e3nCGjFwd5ko5v7zYiXa+juWWx9I4FZfJU98dMvYvszZX0NzLnsMxRV9wPh/VmqFtvKokb7ekZWt55KOdaAsNrJnQqdRNQAaDRMhHO0jSaMvU4bis8nV65vx5ln/OJJp8uQIY2b4+cwYFYfOQwX5tIgKfMqqJgY9ObyBfd3tEiQToCg3o9BIFhQbMlfIqm79BkiQ+3nqJb3cXzc/xSEA9Fgxv+UBV3nc+LG0tir7F9GriSnADp3KdH0Wdp2Ph1kusPHwNSSoaQTS8bX1e7hGAbz0rDl5J4/t90ey6lGI8RiaDetYqOvg58lTw7ZFGyw/EELbpPJIEoU1debKDD43cbKjvaPXQQdBb68+w+nAsvZu48sO4DvdNH52SzWNf7rtvk6JSLmPNxJByfXBGJWfxz5lEsrWFZGkLiUrO5khMeoUEsRGxGbz48zFSswtwt7Pgx3EdaOb54L+7cem5vLzqOGfjiyYvbexmS7/m7vRr7k4Td9sK6yN1MVHDm+tOc+rm/Dmv9Qlk6l06JqvzdFy4oeFcgoaDUansuFkbGeRpx6KRrWns/nBB5MM6fT2T7/bFcCAqlfQ7hr5P692IGX0CqzBnt/3f+jOsOhxrHHpemkU5w6+kMfq7Q9iqlBx7J7RS52eCor/xJ+My2Xs5hSsp2Qxp7VWpteA1hQh8yqgmBD5x6bnsvJjM2Xg1ZxM0RCZlmXxLLcnYEF/mDQ6q1I6uBYUG3vz9NOsjitaHeTU0kGm9G5YqD3kFep794TDHrmUY91mZKxjS2pOwwQ8/N8WfJ+N5b9MFUrOL5nIa2tqTN/o1KbFpKDYtl5TsfNzsLHC1tbjrtTefucEra05SUHg74DBXyglpUI/PR7UuU+1BSpaWzgt2UlBo4LeJIXT0f7DAYV9kCrsvpeBoZYajtTn1rM1RyOXk3AxItp9PYu/lFDzsLdg0tYvJrLyHotPYF5nCxO4Bd52SvyRn49WM+l+4sQbglopstoxLz+W55UeJSs7G2lzBqhc70drb4b7H7bqUzCu/nkSdp8PRyowvnmxDt8Dym+X3fvQGiW92RfHp9suolHJ2vt7D5GdPW6hn0soTxkDnFoVcxuQeAUzp1ahazc9iMEicv6HhQFQqqpszMFeXJpdraTn0/2IfuQV6Rnf04cPHmz/Q36Kz8WrG/niEtJwCRrSrz8Ininc0FqoHEfiUUXUOfNS5Or7cGcnP4VdLnIr+v5RyGeZKuXF+kYndGjCrf5NKCX6y8nW8vPIE+6OKRhjMH9aizJ1sCwoN7L6UzM6Lyey4mExKVlGQ8vqjgUzpdf+hu3ez+nCscRbbABdr3hvanEcCyqfJJyI2g2UHrhKZnM2VlGxjENQvyJ1vn2lb6v+DT7dd4qudUbT2dmD9pEfK7f8wW1vI4K/3E52SQ9dGzix/riMAX+6I5MudkUgSjHvEj3mDgx7ofNczcnn8m4OkZGlpWd+eYH8nbFRmWKsUdPBzotUDBCNlpc7VMXHlMQ5Fp9PBz5HfJobc8z7tj0zl2R8PI0nQytuBb55uWyV9oSRJYtT/DnHkajpDWnvyxc1h0nB7lmCA+o6WNPOwI8jTnj7N3EpVq1WZ8oBby0//w92XrKgK/55PYsKKYxgkeOuxJkzodu8OwIei03jxp2NkaQsJ8rTj5+c73nfJDqHqiMCnjKpj4KPTG1h16Bqf74g0zmfS0c+JTg2cCPKyJ8jTrthcDWZyufGb1q9HYpn1R9ED/l7V6eUlWZPP2GVHuXBDg5W5gsVPt6VnY9dyObfBIPHr0TjeWn8Gc6Wcra90w78Mw5lPX89kxLfhFOgNvNjVnzf6Nqmwb856g8TRq+k8+8NhdHqp1COtcrSFPPLRTtR5Or59uu0DrQZdGpcSsxiyeD/5OgPju/hzMVFj7OcEYGmm4OCsXvetqcrMLWDEknCikrNp4m7Lby+FlKqmqDwkafLp+vEuCgoNrBjf8a5rNGXmFtD3870kabQMbuXJJ0+0rPTmizudjVcz6Ov9SBL8/vIjtPN1ZNfFZJ5bfhTAZPh0dfcgS1ZUpR/3x/DuzXmZvn26Lf2al/z7tP18EpNXnzB2cP9+bHuTKRaE6qc8nt/Vp/60jnvrjzPM23iezFwdjVxt+On5jvz2UggzHm1M3yB36jtaoVIqTLY7q5ef7OjD2wOaAvDp9sv8uP/uU8I/rKjkbB7/5iAXbmhwtjHn1wmdyi3oAZDLZYzu6E3XRs4UFBp4648zlDY+z8gp4OWVJyjQG+jTzI23Hmtaoc0FCrmMTg3qMaNPYwDC/jrHtbTiw7nv5rdjcajzdPjVs+LRCmgmauxuywdDWwBFk/UdiErD0kzBopGtCPK0I0+nZ8Wha/c8h7ZQz4SfjxOVnI27nQXLnutQ6UEPgJudBU8H+wBFC3aW9LMhSRL/t/4sSRotDZyt+Wh4iyoNegCae9nzRLuioejvbTpPkiaf19eeAopq3GpK0FMTPNfZjzEhvkgSvLLmJAeiUoul2RARz0srj1NQaCC0qRs/Pd9RBD11hAh8qoGz8WrjpFzvDgnin+ld6V6GPggvdG3Aq6FFnQzf3XSe8cuPcikxq1zzWjTvxEHiM/Pwd7bmj5c707K+Q7leA4qWOPlgaAsszOSER6fx+4n4u6ZNzykoWl8pKQtJkjAYJF65Of+FXz0rPh3ZqtL6PU3o1oCO/k7kFOh5Zc1JCvX3n8fo4JVUvtgRCRT9H1bU1PPD29VndMeigCHQzYaNUzszrG19Jt6cC2T5wasmHej/a0X4NY5cTcdWpWT58x3wsK+6Bo6XewRgYSYnIjaTPZdTin2+PiKev8/cQCmX8fmTre86gV9le/3RxlibKzgZl8njiw+QllNAUw87ZvV/+DWmhNtkMhlzBjajZ2MX8nUGnv3hMF/8G4n+Zj/JFYeu8epvJ9EbJIa18WLJM23LdX4eoXoTgU81sGDLRaCo0+2YEL8yzzoKMK13Q6b3boRCLmPHxWT6fbGX1347dddJsErj2NV0nvruMJm5Olp7O7DupZBSTUdfWj71rJjeuyiQe//v86Rla00+NxgkfjsaR69PdzNldQR9PttL9092M3bZEfZcTsHCTM63z7Sr1FoJhVzGZ6NaY2uhJCI2ky93Rt01rSRJLDsQw7M/HCEzV0er+vaMaFexk9N9MLQ5f0x6hL+mdKGha9Eooceau1Pf0ZL0nAJjAP5feQV6luwpWvfprQFNaeJetU3ErrYWPNupaNLLz/5T63M9I5e5f54DYHrvRhUSmJeVq50Fk27OVJ6gzsfSTMFXo9uIh24FUCqKfv9HtffGIMFn/15m7I9HWLT9Mu9sOIskwZgQXxY+0eqh/uYKNY/4365iB6JS2ReZiplCxmuPNn7o88lkMl7tE8j2V7vxWAv3ov4EJ67z2Bf7Hir4ScnSMmnVCbSFBno0duGXFztVSgfAF7r608TdlsxcHaO/O8SHmy+w+cwNjl5NZ9TScGb+fprMXB2e9haYK+TEpueyL7KoWvuDoS1o6lH5D2gvB0veH9ocKOpAvPxA8WbHfJ2eN9adJmzjefQGicfbeLFmYkiFPwDlchltfRxNrqNUyHnx5jIN3+2NNn4rvtOqw9dIzdZS39GywoOzBzWxewCWZgpOXVez61Iymnwdf56MZ8LPx8nSFtLO15GXe1S/mW3Hd/HH26motmze4GY0dLW5zxFCWVmYKVgwoiWfPtEKSzMF+6NS+fJm7erkngGEDQ6qNiPShMojOjdXYedmg0FiyOIDnIlXl2pUTWmcjMtk1u+nuZiYRc/GLvw4rkOpm30K9Qae+eEwh6LTaeRqw4bJnR969tzSOBmXyZNLw0ucq8bSTMGMPoGM6+xHQaGBA1Gp7LmcQoCLDc93ebjFCh/WR/9cZMmeormN7lw8MSI2g9fWniI6JQe5DN56rCnju/hX6XpbuQWFdP5oJxm5OhY/1ZYBLT1MPuv28S5SswtYMLwFozr4VFk+/2v+Pxf4355oHKzMyNXqjZNO2qiUbJ7WtUJrJB9GQmYe19JyCQmomessVffOzSWJTMpi0qoTRCZn82a/JtUyKBbuT4zqKqPqEvhsOp3AlNURWJsr2DOzJ84VVIMSmZTFgC+L1nX6cnSbUq8zdesBbm2u4M8pXarkG2p8Zh4Ho1KJiMvkZGwm0anZdA90Yc6goGqxVENJ/jux41uPNSE9R8fSvVcwSOBqq+KzUa3p3LBiZ1R+UJ9tv8wXOyJp7mXH7y8/YuwM/N3eaD7YfAFvJ0t2vtajVJPCVbT0nAK6LthpnE8owMWa/s09eKJ9fXzr1YTHcc2UA9wazpBMzQh8oGj0bFp2QZVN9io8PBH4lFF1CHzydXr6fb6Xq2m5vBoayPTQih1+/sW/kXz272XqWZvz74zuDzzB3tZziUxccRygWE1AVZIkqdqtSF4SSZL4ZOslvrkZ/NzyeBsv5g5qhoNV+S2T8LDScwro/NFO8nR6mrjb8vmTrfFxsqLrgl2k5RTw8fCWjOxQfRZDveVwdBpn4tX0aOxi7LckCELtJAKfMqqqwCevQM+eyylsOXuDHReTycovxNnGnD1v9KzwpqOCQgMDv9rH5aRshrX1YtHI1vc95mpqDoO+2k+WtpDxXfx5Z2CzCs1jbSVJEgu3XWLxris425jzweMtqu1U9HsupzBjzUnScgowV8gJbuDEvshUfJys2PFa92pV2yMIQt0jAp8yquzAJ7egkB/3x/C/PdEmi9C52Kr4eHhLejYpvzlw7uVEbAbDvz2IJMFPz3e855D5fJ3eOFdPe19HfpnQSTz0HoIkFU3z7+1kVSVz35RGSpaWWb+fNllCobQTMgqCIFSEaj+B4d69exk0aBCenp7IZDI2bNhg8rkkScyZMwcPDw8sLS0JDQ0lMjLyvuddvHgxfn5+WFhYEBwczJEjRyqoBA9Hpzew8tA1un+ym4XbLpOlLcTLwZIXuviz7qUQDs/uXWlBD0BbH0fGhvgBMO2XCCJiM+6adt5f57hwQ0M9a3O+fqqtCHoekkwmI8jTvtoHPVAUkH8/tj0fPt4CK3MFLevbM6yKV94Wqpd8YMDNLb+K8yIIpVWhT7OcnBxatWrF4sWLS/z8448/5ssvv2TJkiUcPnwYa2tr+vbtS37+3X+V1qxZw4wZM5g7dy4nTpygVatW9O3bl+Tk5LseUxXScwp46rtDvL3hLClZWrydLPniydbsm9mTtwc2o72fU5UMo5zZrzFtfRxQ5+l4+vvDHLxSfEbTdcev8+vROGQy+OLJNqIjYB0kk8l4KtiHk3MeZe1LIWKeE8GEHth8c7v7lJeCUD1VWlOXTCZj/fr1DB06FCiq7fH09OS1117j9ddfB0CtVuPm5sby5ct58sknSzxPcHAwHTp04OuvvwbAYDDg7e3N1KlTmTVr1gPlpaKbumJSc3hu2RGupuViq1Lyet/GjO7oU21WWM7RFjJxxXH2R6VirpTzzVNt6RroTGRSNqeuZ/LepvPk6wzM6BPItApe80sQhJqnJg5nF2qH8nh+V9k87jExMSQmJhIaGmrcZ29vT3BwMOHh4SUGPgUFBRw/fpzZs2cb98nlckJDQwkPD7/rtbRaLVrt7Vl/NRpNOZWiuCMx6UxYcYzMXB1eDpYsf64Djdyq10gTa5WS78e2Z9ovEWy7uZKxXCaj8I6J67oFujDl5gyzgiAIglBbVFkVRGJiIgBubqYL87m5uRk/+6/U1FT0en2pjgGYP38+9vb2xs3bu2I6af55Mp5nvi9a0qGVtwMbJneudkHPLRZmCr55ui3D2nhhkKDQIGFvaUZIg3pM6hHAV6PbiBlNBUEQhFqneqzcV8Fmz57NjBkzjO81Gk2FBD8n4zIp0BvoF+TOZ6NaY2levdffUSrkfDqyFc+G+OJqZ4GnvUWNmBtHEARBEMqqygIfd/eieUySkpLw8Lg9KV5SUhKtW7cu8RhnZ2cUCgVJSUkm+5OSkoznK4lKpUKlqvh1pd4e0IymHnaMaFu/xtSWyGQy2vg4VnU2BEEQBKFSVFng4+/vj7u7Ozt27DAGOhqNhsOHD/Pyyy+XeIy5uTnt2rVjx44dxk7SBoOBHTt2MGXKlAe+9q3+3BXR16dfoD3Z2Vnlfl5BEITqIueO1xrEyC6h8tx6bj/UuCypAmVlZUkRERFSRESEBEiLFi2SIiIipGvXrkmSJEkfffSR5ODgIP3555/S6dOnpSFDhkj+/v5SXl6e8Ry9evWSvvrqK+P7X3/9VVKpVNLy5cul8+fPSxMmTJAcHBykxMTEB85XXFycBIhNbGITm9jEJrYauMXFxZU5NqnQGp9jx47Rs2dP4/tb/WzGjh3L8uXLmTlzJjk5OUyYMIHMzEy6dOnCli1bsLC4PW/MlStXSE29PdfMqFGjSElJYc6cOSQmJtK6dWu2bNlSrMPzvXh6ehIXF4etra3o08LtPk9xcXFVumhrbSfuc+UQ97lyiPtcOcR9NiVJEllZWXh6lm6x7TvVySUrBFPVYdHWukDc58oh7nPlEPe5coj7XP6qx4x6giAIgiAIlUAEPoIgCIIg1Bki8BFQqVTMnTu3Uob812XiPlcOcZ8rh7jPlUPc5/In+vgIgiAIglBniBofQRAEQRDqjEoJfBYvXoyfnx8WFhYEBwdz5MiRe6Zfu3YtTZo0wcLCghYtWrB582aTz8eNG4dMJjPZ+vXrV5FFEARBEAShFqjwmZvXrFnDjBkzWLJkCcHBwXz++ef07duXS5cu4erqWiz9wYMHGT16NPPnz2fgwIGsXr2aoUOHcuLECZo3b25M169fP5YtW2Z8X5r2T4PBQEJCgpjHRxAEQRBqkDvn8ZHLy1h3U+apDx9Qx44dpcmTJxvf6/V6ydPTU5o/f36J6UeOHCkNGDDAZF9wcLA0ceJE4/uxY8dKQ4YMKXOexMzNYhOb2MQmNrHV3K3aztxcUFDA8ePHmT17tnGfXC4nNDSU8PDwEo8JDw83WUkdoG/fvmzYsMFk3+7du3F1dcXR0ZFevXrx/vvvU69evQfKl62tLYCYCVMQaplcoOPN10cAqyrMiyAI5e/WTNa3nuNlUaGBT2pqKnq9vthyEm5ubly8eLHEYxITE0tMn5iYaHzfr18/hg0bhr+/P1euXOGtt96if//+hIeHo1Aoip1Tq9Wi1WqN77OyihYRtbOzE4GPINQiCiDu5mtbwLoK8yIIQsV5mG4qVbY6+8N48sknja9btGhBy5YtCQgIYPfu3fTu3btY+vnz5xMWFlaZWRQEQRAEoRqq0FFdzs7OKBQKkpKSTPYnJSXh7u5e4jHu7u6lSg/QoEEDnJ2diYqKKvHz2bNno1arjVtcXFyJ6QRBEAShMslkMubNm1fV2SgXfn5+DBw4sKqzcV8VGviYm5vTrl07duzYYdxnMBjYsWMHISEhJR4TEhJikh5g+/btd00PcP36ddLS0vDw8Cjxc5VKZWzWEs1bgiAIglA258+fZ968eVy9erWqs1JmFd7UNWPGDMaOHUv79u3p2LEjn3/+OTk5OTz33HMAjBkzBi8vL+bPnw/A9OnT6d69O59++ikDBgzg119/5dixYyxduhSA7OxswsLCGD58OO7u7ly5coWZM2fSsGFD+vbtW9HFEQRBEIRyk5eXh1JZc3qdnD9/nrCwMHr06IGfn19VZ6dMKvxujxo1ipSUFObMmUNiYiKtW7dmy5Ytxg7MsbGxJmPxH3nkEVavXs3bb7/NW2+9RaNGjdiwYYNxDh+FQsHp06f56aefyMzMxNPTk0cffZT33ntPrGUiCIIgVHsGg4GCggIsLCywsLCo6uzUKIWFhQ9/kjIPhK/B1Gq1BEhqtbqqsyIIQjnKkSSp2c0tp4rzItRuc+fOlQDpwoUL0hNPPCHZ2tpKTk5O0rRp06S8vDyTtIA0efJkaeXKlVKzZs0kpVIprV+/3vjZ3LlzTdJfv35dev755yUPDw/J3Nxc8vPzk1566SVJq9Ua02RkZEjTp0+X6tevL5mbm0sBAQHSRx99JOn1+jKX6cSJE1K/fv0kW1tbydraWurVq5cUHh5u/HzZsmUlzqmza9cuSZIkydfXVxowYIC0b98+qUOHDpJKpZL8/f2ln376qdi1HiT/MTExEiB98skn0meffSY1aNBAksvlD/38rjn1a4IgCPdhBZyr6kwIdcrIkSPx8/Nj/vz5HDp0iC+//JKMjAx+/vlnk3Q7d+7kt99+Y8qUKTg7O9+1mSghIYGOHTuSmZnJhAkTaNKkCfHx8axbt47c3FzMzc3Jzc2le/fuxMfHM3HiRHx8fDh48CCzZ8/mxo0bfP7556Uux7lz5+jatSt2dnbMnDkTMzMz/ve//9GjRw/27NlDcHAw3bp1Y9q0aXz55Ze89dZbNG3aFMD4L0BUVBQjRoxg/PjxjB07lh9//JFx48bRrl07goKCAEqd/2XLlpGfn8+ECROQJMlkbsAyKXPIVIOJGh9BEAThYdyq8Rk8eLDJ/kmTJkmAdOrUKeM+QJLL5dK5c+eKnYf/1PiMGTNGksvl0tGjR4ulNRgMkiRJ0nvvvSdZW1tLly9fNvl81qxZkkKhkGJjY0tdnqFDh0rm5ubSlStXjPsSEhIkW1tbqVu3bsZ9a9euNanluZOvr68ESHv37jXuS05OllQqlfTaa68Z9z1o/m/V+NjZ2UnJycmSJJXP81uszi4IgiAIZTR58mST91OnTgUotrh29+7dadas2T3PZTAY2LBhA4MGDaJ9+/bFPr81ad/atWvp2rUrjo6OpKamGrfQ0FD0ej179+4tVRn0ej3btm1j6NChNGjQwLjfw8ODp556iv3796PRaB7oXM2aNaNr167G9y4uLjRu3Jjo6GjjvtLmf/jw4bi4uJSqTPcimroEQag1coEON18fRSxZIVS8Ro0ambwPCAhALpcXG+7t7+9/33OlpKSg0WhMFuQuSWRkJKdPn75rMJCcnHzfa/33urm5uTRu3LjYZ02bNsVgMBAXF2dsqroXHx+fYvscHR3JyMgwvi9t/h/k3pWGCHwEQag1JOD8Ha8FobLdbSkFS0vLcruGwWCgT58+zJw5s8TPAwMDy+1apVXSslEAknT7N7K0+S/Pewci8BEEQRCEMouMjDSpkYiKisJgMJRpjhsXFxfs7Ow4e/bsPdMFBASQnZ1NaGhoqa9xt+taWVlx6dKlYp9dvHgRuVyOt7c38HBrZN1S3vkvLdHHRxAEQRDKaPHixSbvv/rqKwD69+9f6nPJ5XKGDh3Kxo0bOXbsWLHPb9WajBw5kvDwcLZu3VosTWZmZqnnulEoFDz66KP8+eefJk10SUlJrF69mi5duhhXPLC2tjZep6zKO/+lJWp8BEEQBKGMYmJiGDx4MP369SM8PJyVK1fy1FNP0apVqzKd78MPP2Tbtm10796dCRMm0LRpU27cuMHatWvZv38/Dg4OvPHGG/z1118MHDjQOFQ8JyeHM2fOsG7dOq5evYqzszMA48aN46effiImJuaetVDvv/8+27dvp0uXLkyaNAmlUsn//vc/tFotH3/8sTFd69atUSgULFiwALVajUqlolevXri6uj5wGUuT/4ogAh9BEARBKKM1a9YwZ84cZs2ahVKpZMqUKXzyySdlPp+XlxeHDx/mnXfeYdWqVWg0Gry8vOjfvz9WVkXd9a2srNizZw8ffvgha9eu5eeff8bOzo7AwEDCwsKwt7c3ni87OxtLS0scHBzued2goCD27dvH7NmzmT9/PgaDgeDgYFauXElwcLAxnbu7O0uWLGH+/PmMHz8evV7Prl27ShX4lCb/FUEm3dnjqI7QaDTY29ujVqvFgqWCUIvkADY3X2cD1lWYF6F2mzdvHmFhYaSkpFRo7cTDcnNzY8yYMQ8VjFUn5fH8FjU+giDUGjLA947XglCXnTt3jry8PN58882qzkq1IgIfQRBqDSvgalVnQhCqiaCgoAeeeLAuEaO6BEEQBEGoMyol8Fm8eDF+fn5YWFgQHBzMkSNH7pl+7dq1NGnSBAsLC1q0aFFs6m9JkpgzZw4eHh5YWloSGhpKZGRkRRZBEARBEIzmzZuHJEnVun+PULIKD3zWrFnDjBkzmDt3LidOnKBVq1b07dv3rlNqHzx4kNGjRzN+/HgiIiIYOnQoQ4cONZnQ6eOPP+bLL79kyZIlHD58GGtra/r27Ut+fn5FF0cQhGosj6IlKzrcfC0IgvBfFT6qKzg4mA4dOvD1118DRVNVe3t7M3XqVGbNmlUs/ahRo8jJyWHTpk3GfZ06daJ169YsWbIESZLw9PTktdde4/XXXwdArVbj5ubG8uXLefLJJ++bJzGqSxBqJzGqSxBqt/J4fldojU9BQQHHjx83mZZaLpcTGhpKeHh4iceEh4cXm8a6b9++xvQxMTEkJiaapLG3tyc4OPiu5xQEQRAEQYAKHtWVmpqKXq/Hzc3NZL+bmxsXL14s8ZjExMQS0ycmJho/v7Xvbmn+S6vVotVqje9FL3dBEARBqJvqxKiu+fPnY29vb9xuLbYmCIIgCELdUqGBj7OzMwqFgqSkJJP9SUlJuLu7l3iMu7v7PdPf+rc055w9ezZqtdq4xcXFlak8giAIgiDUbBUa+Jibm9OuXTt27Nhh3GcwGNixYwchISElHhMSEmKSHmD79u3G9P7+/ri7u5uk0Wg0HD58+K7nVKlU2NnZmWyCIAiCINQ9FT5z84wZMxg7dizt27enY8eOfP755+Tk5PDcc88BMGbMGLy8vJg/fz4A06dPp3v37nz66acMGDCAX3/9lWPHjrF06VIAZDIZr7zyCu+//z6NGjXC39+fd955B09PT4YOHVrRxREEoZoTs6oIgnAvFR74jBo1ipSUFObMmUNiYiKtW7dmy5Ytxs7JsbGxyOW3K54eeeQRVq9ezdtvv81bb71Fo0aN2LBhA82bNzemmTlzJjk5OUyYMIHMzEy6dOnCli1bsLCwqOjiCIJQjVkDKVWdCUEQqjWxOrto9hIEQRCEGqHaz+MjCIIgCIJQnYjARxCEWiMP6HFzE0tWCIJQkgrv4yMIglBZDMCeO14LgiD8l6jxEQRBEAShzhCBjyAIgiAIdYYIfARBEARBqDNE4CMIgiAIQp0hAh9BEARBEOoMMapLEIRaxaqqMyAIQrUmAh9BEGoNayCnqjMhCEK1Jpq6BEEQBEGoM0TgIwiCIAhCnSECH0EQao18YMDNLb+K8yIIQvVUYYFPeno6Tz/9NHZ2djg4ODB+/Hiys7PveUx+fj6TJ0+mXr162NjYMHz4cJKSkkzSyGSyYtuvv/5aUcUQBKEG0QObb276Ks6LIAjVU4UFPk8//TTnzp1j+/btbNq0ib179zJhwoR7HvPqq6+yceNG1q5dy549e0hISGDYsGHF0i1btowbN24Yt6FDh1ZQKQRBEARBqE1kkiRJ5X3SCxcu0KxZM44ePUr79u0B2LJlC4899hjXr1/H09Oz2DFqtRoXFxdWr17NiBEjALh48SJNmzYlPDycTp06FWVYJmP9+vUPFexoNBrs7e1Rq9XY2dmV+TyCIFQvOYDNzdfZFI3yEgSh9iiP53eF1PiEh4fj4OBgDHoAQkNDkcvlHD58uMRjjh8/jk6nIzQ01LivSZMm+Pj4EB4ebpJ28uTJODs707FjR3788UcqIHYTBEEQBKEWqpB5fBITE3F1dTW9kFKJk5MTiYmJdz3G3NwcBwcHk/1ubm4mx7z77rv06tULKysrtm3bxqRJk8jOzmbatGl3zY9Wq0Wr1RrfazSaMpRKEARBEISarlSBz6xZs1iwYME901y4cOGhMnQ/77zzjvF1mzZtyMnJ4ZNPPrln4DN//nzCwsIqNF+CIAiCIFR/pQp8XnvtNcaNG3fPNA0aNMDd3Z3k5GST/YWFhaSnp+Pu7l7ice7u7hQUFJCZmWlS65OUlHTXYwCCg4N577330Gq1qFSqEtPMnj2bGTNmGN+r1Wp8fHxEzY8g1DJ3ztqsQYzsEoTa5tZz+2G6uJQq8HFxccHFxeW+6UJCQsjMzOT48eO0a9cOgJ07d2IwGAgODi7xmHbt2mFmZsaOHTsYPnw4AJcuXSI2NpaQkJC7XuvkyZM4OjreNegBUKlUJp/funHe3t73LYsgCDVT8SEUgiDUFllZWdjb25fp2AoZ1QXQv39/kpKSWLJkCTqdjueee4727duzevVqAOLj4+nduzc///wzHTt2BODll19m8+bNLF++HDs7O6ZOnQrAwYMHAdi4cSNJSUl06tQJCwsLtm/fzuuvv87rr79eqqYsg8FAQkICtra2yGSyci23RqPB29ubuLi4WjdirDaX7ZbaXsbaXj6o/WWs7eWD2l/G2l4+qLgySpJEVlYWnp6eyOVlG59VYYuUrlq1iilTptC7d2/kcjnDhw/nyy+/NH6u0+m4dOkSubm5xn2fffaZMa1Wq6Vv37588803xs/NzMxYvHgxr776KpIk0bBhQxYtWsSLL75YqrzJ5XLq16//8IW8Bzs7u1r7A12by3ZLbS9jbS8f1P4y1vbyQe0vY20vH1RMGcta03NLhQU+Tk5Oxtqdkvj5+RVro7OwsGDx4sUsXry4xGP69etHv379yjWfgiAIgiDUHWKtLkEQBEEQ6gwR+JQzlUrF3Llz79nZuqaqzWW7pbaXsbaXD2p/GWt7+aD2l7G2lw+qdxkrrHOzIAiCIAhCdSNqfARBEARBqDNE4CMIgiAIQp1RYaO6qrOKnMdHEARBEISKUa3n8anOEhISxKzNgiAIglBDxcXFlXk+vjoR+MyfP58//viDixcvYmlpSfv27QFq9ayZgiAIFSUX6Hjz9RHAqgrzIlQ/Wfk6jl/LoEdj13I/960ZoRs2bIiFhQWZmZmlPkedCHz27NnD5MmT6dChA4WFhbzxxhtA3Zg1UxAEobwpgLibr20B6yrMi1D9LNp9juUHr/JcZy1zBwVVyDVeeOEFVq5cWaZj60Tgs2XLFpP3S5YsISAgoIpyIwiCIAi106m4TH4KvwpAryblX+NzS1BQ2QOqOjmqS61WV3UWBEEQBKFWKdQbmP3HGSQJhrb2pGsjl6rOUonqXOBjMBiYNWtWVWdDEARBqEIymYx58+ZVdTZYvnw5MpmMq1evVutzPtB1D17l/A0N9pZmvD2wmclnV69eRSaTsXz58tKft5zLU+cCn8mTJ3PhwoWqzoYgCIIg1EibN28uFjRez8jl022XAZjdvwnONtVvqYpb6lTgM2XKFDZt2sSmTZuqOiuCIAhCFcrLy+Ptt9+u6mzw7LPPkpeXh6+vb1Vn5YFt3ryZsLAw43tJkpj75znydHo6+Dkysn3x6WJ8fX3Jy8vj2WefrcyslqhOdG6WJImpU6eyfv16du/ejZubW1VnSRAEocaSAc3ueF1TGAwGCgoKsLCwwMLCoqqzA4BCoUChUFR1NsokJUvL1nOJ/H36BuHRaZgpZHz4eAvk8ts/FYWFhRgMBszNzavNPa8TNT6TJ09m5cqVrF69GltbW5KSkqo6S4IgCDWWFXDu5lbZc/jMmzcPmUzGxYsXGTlyJHZ2dtSrV4/p06eTn59vklYmkzFlyhRWrVpFUFAQKpXKOMq3pD4+8fHxjB8/Hk9PT1QqFf7+/rz88ssUFBQY02RmZvLKK6/g7e2NSqWiYcOGLFiwAIPBUKbylNR/xc/Pj4EDB7J//346duyIhYUFDRo04Oeffy52/Llz5+jVqxeWlpbUr1+f999/v8S83K1Pk5+fH+PGjTO+1+l0hIWF0ahRIywsLKhXrx5dunRh+/btADw7ZiyLFy8GwNXOgmdD/Ph1YggA41pYE+hux8KFC/n8888JCAhApVJx/vz5Evv4nD59mnHjxtGgQQMsLCxwd3fn+eefJy0t7b73LS4uDr1ez8mTJzl58iTZ2dn3PeaWOlHj8+233wLQo0ePqs2IIAiCUC5GjhyJn58f8+fP59ChQ3z55ZdkZGQUCw527tzJb7/9xpQpU3B2dsbPz6/E8yUkJNCxY0cyMzOZMGECTZo0IT4+nnXr1pGbm4u5uTm5ubl0796d+Ph4Jk6ciI+PDwcPHmT27NncuHGDzz//vNzKFxUVxYgRIxg/fjxjx47lxx9/ZNy4cbRr1844lDsxMZGePXtSWFjIrFmzsLa2ZunSpVhaWpbpmtnaQj4Mm8tHH33ECy+8QMeOHdFoNBw7doxNOw+wO9udg8rWWPidIf9qBPUGvoaPkyWt6jsQNqMnhqxk3gaWLVtGfn4+EyZMQKVS4eTkVGIwtn37dqKjo3nuuedwd3fn3LlzLF26lHPnznHo0KF7Lik1f/58ANq0aQPArl27HvwZL9VBarVaAiS1Wl3VWREEQRBKYe7cuRIgDR482GT/pEmTJEA6deqUcR8gyeVy6dy5c8XOA0hz5841vh8zZowkl8ulo0ePFktrMBgkSZKk9957T7K2tpYuX75s8vmsWbMkhUIhxcbGlro8y5YtkwApJibGuM/X11cCpL179xr3JScnSyqVSnrttdeM+1555RUJkA4fPmySzt7evtg5/1veO681duxY6ZMtFyXfNzdJ1h4BUsN23aQdFxKlyKQs6ct/L0s9P9kl+b65ybi5hwyRACk2LcfkXDExMRIg2dnZScnJySV+tmzZMuO+3NzcYvn55ZdfipX9zntUHs/vOtHUJQiCIJSfXCDo5pZbRXmYPHmyyfupU6cCRR1v79S9e3eaNTMdWv1fBoOBDRs2MGjQIOOSRne6VfOwdu1aunbtiqOjI6mpqcYtNDQUvV7P3r17H6ZIJpo1a0bXrl2N711cXGjcuDHR0dHGfZs3b6ZTp0507NjRJN3TTz9dqmudiM3g611RABQqLYmJusSzi/4kdNEePt1+mejUHCzM5Axt7cnqF4IZ1sYLAG+nkhs6hw8fjovL/efwubNmKj8/n9TUVDp16lSUpxMnSlWG0qgTTV2CIAhC+ZGA83e8rgqNGjUyeR8QEIBcLi8214u/v/99z5WSkoJGo6F58+b3TBcZGcnp06fv+lBPTk6+77UelI+PT7F9jo6OZGRkGN9fu3aN4ODgYukaN278QNcoKDSQnlNAakoOLjJ4d3AQqUHv8dbLY0j4biJmLr40bNOZ8c+NY+LjPbFRFYUMq+/RBAUPds8B0tPTCQsL49dffy127ypyomER+AiCIAg13t36g5S1v0tJDAYDffr0YebMmSV+HhgYWG7XuttIr6JWq4enLdTz4s/HydXqsJLB16PbMqClB4T4MXbAVTZs2MDWbdvYtnUzb+5Yg/2SJbzwwgsPdO4HvecjR47k4MGDvPHGG7Ru3RobGxsMBgP9+vUrc2fxB1HjAh+9Xs+8efNYuXIliYmJeHp6Mm7cON5+++17doQSBEEQao/IyEiTmoWoqCgMBsNdOy/fi4uLC3Z2dpw9e/ae6QICAsjOziY0NLTU16gIvr6+REZGFtt/6dKlYvscHR1NVjJfEX6NPRcS0Gen07mhc1HQc5OTkxPPP/88zz//PNnZ2XTr1o158+YZA5/yeNZmZGSwY8cOwsLCmDNnjnF/SeUpbzWuj8+CBQv49ttv+frrr7lw4QILFizg448/5quvvqrqrAmCIAiV5NaQ6ltuPQP69+9f6nPJ5XKGDh3Kxo0bOXbsWLHPb9WyjBw5kvDwcLZu3VosTWZmJoWFhaW+9sN47LHHOHToEEeOHDHuS0lJYdWqVcXSBgQEGPsgFeoNLDtwlayTW0Ay4GZ3e36d/w4lt7GxoWHDhmi1WuM+a2trAJNAqrRu1Wj9twarPEfG3U2Nq/E5ePAgQ4YMYcCAAUDRHAS//PKLyX+8IAiCULvFxMQwePBg+vXrR3h4OCtXruSpp56iVatWZTrfhx9+yLZt2+jevTsTJkygadOm3Lhxg7Vr17J//34cHBx44403+Ouvvxg4cKBxaHlOTg5nzpxh3bp1XL16FWdnZwDGjRvHTz/9RExMTJlqoR7EzJkzWbFiBf369WP69OnG4ey+vr6cPn3aJO0LL7zASy+9xPDhw3Ft2oHTf++l4FqEMb+3NGvWjB49etCuXTucnJw4duwY69atY8qUKcY07dq1A2DatGn07dsXhULBk08+Waq829nZ0a1bNz7++GN0Oh1eXl5s27aNmJiYMt6NB1fjAp9HHnmEpUuXcvnyZQIDAzl16hT79+9n0aJFVZ01QRAEoZKsWbOGOXPmMGvWLJRKJVOmTOGTTz4p8/m8vLw4fPgw77zzDqtWrUKj0eDl5UX//v2xsioavWRlZcWePXv48MMPWbt2LT///DN2dnYEBgYSFhaGvb298XzZ2dlYWlri4ODwsEW9Kw8PD3bt2sXUqVP56KOPqFevHi+99BKenp6MHz/eJO2LL75ITEwMP/zwAxmbNmPm1Yw3v1jJz++Ypps2bRp//fUX27ZtQ6vV4uvry/vvv88bb7xhTDNs2DCmTp3Kr7/+ysqVK5EkqdSBD8Dq1auZOnUqixcvRpIkHn30Uf755x88PT3LdkMekEwqr55SlcRgMPDWW2/x8ccfo1Ao0Ov1fPDBB8yePfuux2i1WpNqOo1Gg7e3N2q1Gjs7u8rItiAIQq2Ry+0lK85TubM3z5s3j7CwMFJSUorVVlQnbm5ujBkz5qGCsYoQfiWN0d8dwsJMzsFZvXGyNq/qLJWKRqPB3t7+oZ7fNa6Pz2+//caqVatYvXo1J06c4KeffmLhwoX89NNPdz1m/vz52NvbGzdv7+ILqAmCIAgPxgq4enOr7CUraoJz586Rl5fHm2++WdVZKeb7fUXzAI1oV7/GBT3lpcYFPm+88QazZs3iySefpEWLFjz77LO8+uqrxumrSzJ79mzUarVxi4uLq8QcC4IgCHVJUFAQGo2mTDVSx69l0OvT3ey+VH5zAt0SlZzFjovJyGQwvkuDcj9/TVHj+vjk5uYil5vGawqF4p5j/lUqFSqVqqKzJgiCIAgPZfnBq0Sn5LBw2yV6NHYtt/MaDBJL9hTV9vRp6oa/s3W5nbumqXGBz6BBg/jggw/w8fEhKCiIiIgIFi1axPPPP1/VWRMEQagT8oBuN1/vBcpvisD7mzdvXomrjNcGkiQRfiUVgLPxGs4naGjmWfZ+qJIkcTExiz9PJrDxVALxmXkATOhWd2t7oAYGPl999RXvvPMOkyZNIjk5GU9PTyZOnGgyAZIgCIJQcQzAsTteC+XjUlIWqdkFxvdrj8cx1zOoTOfK1+mZsvoE/1643WRmo1LyfBd/2vk6PnRea7IaN6qrPJRHr3Ch6hgMErsuJdPM0w4P+8r8rikIAkAOYHPzdTZQdxtNytf3+6J5/+8LOFqZkZGrw9HKjMNvhWKuLF13XG2hnpdWHGfXpRTMFDJ6N3FjSGtPejZxxcKs5KUwaoryeH7XuBofQfhiRyRf7IjE0kzBtN6NGN/Fv9R/GARBEKqbg1eKZk2e0C2AZQdiSM7SsuNCEv1beNznyNsKCg1MXhXBrkspWJjJWTauIyEB9SoqyzWSeFoINcrlpCy+2R0FQJ5Oz4ItF3nsy32EX0m7z5GCIAjVl05v4HB00d+xboHODG9XH4Dfjj34KORCvYHpv0bw74UkzJVyvh/TQQQ9JRCBj1BhkjX5XM/ILbfVhA0GiVm/n0anlwht6sqnT7SinrU5UcnZjP7uEKGL9vD+pvPsi0xBW6gvl2sKQlW7cEPD//ZcIbegcteBEirXqbhMcgr0OFmb09TdjiduBj57LqeQpMk3pruhzuPglVRytLd/HnR6A78di+PRz/byz9lEzBVy/vdsO7o0qr4TPFYl0dQlVIhTcZk8ufQQeTo9LrYq2vo40NbHkY7+TrTwskepKH3MvfLwNU7EZmKjUvLe0OZ42FsS2tSNhdsusfpILFHJ2UQlZ/P9/hgautrw64ROONuIaQyEmutiooaRS8LJ0hZyKTGLRaNal+r45Kx8dl9M4bGWHtioxJ/76uxAVFFtT0iDesjlMhq42NDe15Fj1zL4/cR1XuzagO/2RfPFv5FoCw2YKWR08HOiZX0HkxFb9pZmLBrZip7lOBS+thGdm0Xn5nJ3Q53HkK8PkJylLfFzG5WSjv5OdGrghIutCpVSgYWZHJlMhiZPhya/EE2eDitzBd0DXWjgYsMNdR59Fu0lW1vIu0OCGBPiZ3JOda6O/VGp7LmczPbzSWTk6mhV355fJnTCylz8wRdqnhvqPIZ9c5Ab6tvf9r94sjVDWnsZ3xsMEn+eisfHybrYSJ2sfB1Dvj5AdGoOzb3s+HFcB1xtLSgPOYDfzddXEZ2by8PI/4VzJCadDx9vwVPBPgD8djSOmb+fxsvBEntLM87f0AAYOz/fydlGxQtd/Xmmk2+tDnLL4/ktAh8R+JSr3IJCnlgSzrkEDY3dbFn5QjBX03I4fi2D49cyOBydhia/dFX2/s7WqJRyLiZm0dbHgXUvPYJcLrtr+isp2Yz49iAZuTp6NnZh6Zj2mD1ADVOyJp+Vh64Rn5mPnaUSOwsz7C3NaOZpRwc/JxT3uKZQdxgMElnaouDc0lxRIbWKmnwdI5eEczExi4auNnQPdOGH/THYWij5Z3pX6jtaoS3U89pvp9h0+gZKuYyvRrcxdoKVJIlJq07wz9lE4zm9nSz5+fngOj1xXVUxGCQuJGpI0uSTrNGSnKWlmYcdoc3cgKK/m63CtqHTS+x5owe+9Yr+j7K1hXR4/1/ydEVN9w5WZrwzoBnD2noRk5rDnsspnIrLpI2PI6M6eNf4EVsPQozqEqoVg0HilV9Pci5BQz1rc74f2x4XWxUutio6+DkBoDdIXLihIfxKGidiM8jWFqLVGdAW6ik0SMZgw85SSUJmPodj0ohJzQHATCFjwfCW9wx6AAJcbPhhXAee+u4Quy6l8H/rz7BgeEtkMhl6g0RuQSFmCjkqZVEt05WUbJbuiWZ9RDwF+pJnJalnbU5oUzf6NXenU4N6WJqX/g9Mod5QpiY+oWz0Bont5xP55Ugc6Tm350axUSnp2cSFfkEe+NQzXWkqX6c3/lzc6Wy8mqV7o9lzOQVNvo47vy628nagX5A7/Zq7Y61ScPxqBseuZXDmuhq5HOwszLCzNMPJ2pwegS50utmUURJtoZ6DUWl8vSuKi4lZuNiqWP5cB9ztLDgRm0FEbCYz1pxi6Zh2vLTyOIei0wEoNEhM+SWCLyUY0NKDH/bH8M/ZRMwUMhY+0YpPt10mNj2X4d8e5MdxHWjt7XDPe2cwSBy7lsGJ2Ay6NHSmuZf9PdMLd5esyWfCiuOcjMss9tkroY2Y3rsRR2LS0eklvBws8XG6/TNpo1LybIgvS/dGM7iVJ3MGNTMG2g1cbGjgYlPsnML9iRofUeNTLs4lqPlm9xX+Pn0Dc4WcXyYE087X6aHPm5WvY39kKvujUnkkwJkBLR98WOe/55OYsOIYBgnc7SzIKSgkW1to8tAyV8opKLwd7LT3daRnE1eyb36jT8suIDw6DXXe7WplpVxGkKcd7XydCHSzISVLS1xGLnHpeVirFLwSGmjyoNAbJGPbfHADp6JO2aLv0X1pC/Xsu5zK5rM3OHEtg47+Tkzt1Qhvp3svi5mjLWTd8ev8eCCGa2m590zb1MOOQDcbrmfkEZueS0qWFgcrM9p4F/VJ86lnxbrj19kXmVrsWAszOdpCA6X9C+phb8GQ1l50C3RGqzOgztOhztNx/FoGOy8mk32z06q1uYI1E0OMP0vX0nJ47It95BTosbc0Q52nw0al5Jun27IhIp4/IuJRyGVM6NaApXuj0RskY7NwSpaW55Yf4Wy8BkszBX9MeoSmHsX/9l1OyuL3E9fZeDKBhJtNbDIZDGtTnzf6NsbdvnyayuqKs/FqXvjpGImafCzNFDRwscbNzgIzhYyt55IAmNi9AQaDxHf7YhjV3psFI1qanMNgkFDn6XCsowuK/pdo6iqjigp8rqXlsPNiMkNbe1X4D6neIBGTms25BA3nEjTEpuXS1MOOzg3r0crbATOFHEmSSFDncyU5GwcrM1rWdyh2nnydnvAradhbmdHQ1QY7CzPjZznaQmLTc7FRKanvaFnsW3BmbgG7L6Xwc/hVTsRmGvd/NqoVj7epX1FFL5VfjsQy+48z903Xp5kbE7s1oL1f8WBNpzdwJCadLWcT+fdCkkmfi5LIZfB8Z39e7RNIoiaf19eeIuKO++Nhb8HXT7Wt87On/pckScSm5xJ+JY2DV9LYdTGZLK1ps6hSLmNkB2+m9GyIp0PxyStztIUM/Gq/sZbQ3tKMZzr50N7XCW7++Mal57L1XCKHotPRGx7sz59cBgNbejL2ET98nKyws1SiUipIzspn+/kktpxNJPxKGnpJorGbLe39HGnr44iZQo4mvyiwuZaayz9nb9y3qdfNTkXfIHeeDvalsbutyWfrjl/n9bWnAIy1QUGe9ugNEjPXneb3E9eNaYe29uSzUa2Nv7fZ2kJe/OkY4dFpNHG35c8pnVEpb9dc/nPmBpNXn+DWLbFVKQnysjPWKlmaKZjYvQFTejZEp5DT/9ZxVO6SFTXF5jM3mPHbSfJ1BgJcrPlhbAf87mhm/GF/DO9tOg+AuUJOgd5QrA+XUJwIfMqoogKfBVsu8u3uK5gr5PQJcmNke2+6NHQuVd+QW2ur7L6UwvWMXGzvaPpJyy4wjlyKTs0mX1dys4y1uQKfetZcS8sht+D2sO6O/k680rsRIQH1yNYWsuLQNX7cH2MyRbqrrQoPewsS1Pmk3NE52c5CSZCnPY3dbUlU53M2Qc31jDzj50q5jH7N3Xmuc/WbDj02LZeUbC32lkX30tZCiU5vQFtYtKmU8gfupyFJEtcz8jh+LYNj19K5lpaLu50F3k5W1He0ZNelFDaeSgCKapkycgvQFhqwVSmZ0qsha47FEZ2Sg1Iu4/8GNGVsiF+xZo98nZ6jV9MxV8hp6+t4z/5JkiShyS9EJsMkaK0pNPk6DkSmsutSMgei0owjU25xs1PRv7kHHfyc+PVorLHmxVwp58exHYoN1701862zjTnTejdiRLv6d+3cnpFTwI6LyaRma/F2tMLHyQpPBwviM4v+f0/EZhKZlEWwvxMvdG1w35qmotpECdt7/D/k6/TsupjMHxHxRCZlYWtR9Lttb2mGj5M1jwa50bq+w12bwiRJ4pOtlziboOGDoc1N8qS/Od3D2uPXaexmy/rJjxQre0qWln6f7yUtp4CJ3Rswu39ToKimZ+jiA+QW6OnayJmng33o0bholt+TcZm8v+k8x65lAPBsJ1/eHBKE7a2ACtG5+U6SJLF4VxQLt10GoHugC1891abE38/Vh2P5vw1njLWGR/8vFBdbURt8LyLwKaOKCnzWR1znh/0xnI3XGPe521nQq6kr3QNd6NzQucTe9vGZeRy7ms6h6DR2XUwhUXPvGoVbLM0UNPWwJcjTHm8nS05dV3MwKtWkt79SLsO3nhVx6XnG/iut6tsTk5pj/ObpZlf0i5akKT4Ky8HKjBxtITp9yT8m/s7WDGvjxaiO3uU2YqSm23UpmXc2nDUGht0CXfhoWAs8HSzJ1hby5rrT/H3mBlA0OqO9nxMd/ZwwV8rZfSmZg1fS0N5sfrO1UNI90IXugS4YJIm49DyuZ+QSn5lHkkZLkiYfbaEBuQw6N3RmSGsv+ga53fPhW1XyCvRcSckmMjmLy0nZnLjZ4b3wjloXpVxGGx8HQhrUo1ugC219HE2CgCMx6SzYcpHj1zJo6GrDluldjf2mtIV6un+8m0RNPvOHtWB0R59KL2NVMxgkjlxNp7mX/V1H9mw7l8iEFceRyeDXFzvRxMOOIV/v52paLo8E1OPn5zsW64smSRJrj1/nzd9PI0nw1qBmTOzsD9TswOdaWg7xmXl08r97v6vS0OkNvPXHGdYeL6p5e76zP2891uSeffvWR1zn9bWnaefjyG8vhTx0Hmo7EfiUUUX38TmXoGbtseusj4g36RtippDh6WBp7MBrYSbnXIKmWNOJhZmcRwKcCfK0I1tbiDpPhyZPh51lUXNUI1dbGrra4ONkVaw2yWCQOH9DQ6I6Hz9na3zrWWGmkHNDnceS3Vf45WicsU9LgIs1k3o0ZHBrT2OV/JXkbJI02qJOdvWssLc0o6DQwOWkLM4naLiclIWrnYrmnvYEedljb1n9HrDVQW5BIT+HX8PdzoIhrT1NmgklSWL5wass3HqJnIKSJ1r0sLdAW2gw6ZT7oMyVcvo0deOJ9vXp2sil2M+IOlfHibgMIq5lcDw2g+sZeTR0saG5lz0tvOxp6W1/1yC2UG9AIZcVa/b8L0mSOBKTTkRc5s3mWDUxqTkl9odp4GJNj0BXujd2oYOf432nH9Dk6+j+8S4ycnV8PKIlI9t7A7DmaCxv/n4GNzsVe2f2NGnGEUy9ue40a47F4eVgSUNXG/ZcTsHLwZKNU7vgdI9m+iV7rvDRPxeRy+DGuA7kN3a9b+BzMi6TRdsvc+Z6Jp+Nak2Ph5xfRp2rI1GTX6wZsLSytYX0+GQXqdkFNHazZWrvhjzW3KPMAZA6T8ekVcc5EJWGXAZhQ5rzbCffBzo2UV00klRMvXF/IvApo8rq3Hyr/8yeyynsvpTM1bt0tFTc7Czb3teJ7o1dCPZ3qrBhiYnqfDadTqC+oyV9mrmLIdpVSKc3cDZezZGYdI5eTSdfZ6BLI2d6NnYl0M0GgwSnrmey80Iy4dFpWKuUeDtaGpvV3O0scLOzwMVWRZImn79OJrDhZDxXUnKM1/Cwt2B42/rYWCg5E6/mbLz6vh1+AbwcLGnn60hbHwcKDZIxeLmSkoOthZLGbrY09bCjmacdIQ3qGZtcJEli+/kkvtgRybkETbHzOlqZ0cjNlkauNjTxsKNbI2fj0N3SuNWk5Wlvwc7Xe2CmkBO6aA8xqTn832NNebFbg1Kfsy7J1hbS/4u9xKUX1UqqlHJ+f/mR+47ekiSJN38/zW/HrmNQKUl8+RFu1LNCn6dDk6/DIIGFUoHKTE6iOp8vd0Sy4+Lt1cGtzRWsfekRmnmW/u+uJElsOn2DOX+eJSNXx8s9Anjj0cZlDlQW74rik62XTPY1crXhm6fb0sitdEFVfGYe4348QmRyNlbmChY/1ZaeTcQEghVBBD5lVFWjuuLSc0nS5KPJ16HJKxph1MDFmtbeDiLSF8qFJBUFKeuOX2fDyXgy/zPJ2S3+zta0uTmbtr+zNZeTsoyBUVRyNg/Y59fIy8GSkIB6nE/QGCdZszJX0KOxC0Ge9gR5FgVJ5dUcmq/T03Phbm6o83l7QFPc7S2YsjoCe0szDszqVasncCsvR6+mM/J/4UgSLBrZimFtH2xAQkGhgad/OMzRmPQHSi+XwbC29bmekcuh6HTc7SxYP/kRPOwfvEt0WraWd/48y+YziSb7B7Tw4NORrUr9RTErX0eXBbtQ5+l4d0gQadkF/Hgghqz8QroFuvDz8x0f+FxRydk8+8NhbqjzcbNT8eO4og7nQsWos4FPfHw8b775Jv/88w+5ubk0bNiQZcuW0b59+wc6XgxnF+oCbaGef88ns/FUAnI5xqas5p729xx1mJWv41ScmhOxGZyKy0SpkBmDlyYedmTkFHAxMYuLNzRExGVyKi7TpJ+OtbmCcZ39GN+lwT2bTR7WraYtRyszXG0tuJSUxfTejXi1T2CFXbO2OXgllbwCPb2bupXquPicAjp8dwjzxCygKLixtTBDIZeRr9OTr9Mjl8kY0NKD6b0b0cDFBnWejuHfHiQqOZumHnasfSkEGUVrkV24oTEZwSdJGL8gavJ0hEenkZ5TgFIuY3LPhng5WvJ/68+g00u08XHguzHtSxygkFtQSERsJh1u9qG75asdkXy6/TIBLtZse7U7CrmMqORsQhftQSmXceztUBys7v+ze/p6JuOWHSU9p4AAF2tWjA8ucbShUH7qZOCTkZFBmzZt6NmzJy+//DIuLi5ERkYSEBBAQEDAA51DBD6CUH5ytIUcu5bBoeg0bC2UPNXR54EeGg+rUG/g0c/3En2zac/KXMGBN3uJ+U4qQQ7gapCQa/KJslDiqlIW6/dlMEjFmqHi0nN5/JsDpGYX4GBVNBfRgz6BmrjbsvCJVsbmuEPRaUxccRx1ng53Ows+eLy5SQB3Nl7NtF8iiE7NIaRBPb4b2x4blRJNvo4uH+1Ek19YbPh4v8/3cjExi4+Ht2RkB+9iecjX6Y0B2eWkLN5Ye4qcAj0t69uz/LmOFRroC0XqZOAza9YsDhw4wL59+8p8DhH4CELtsPnMDSatOgHA+C7+vDOwWRXnSLifU3GZjFoabpyOw81ORZCnPc42t4MGGTJsLG4tG6PE1c6C3k1di3VYv5KSzQs/HTPO2zSwpQdzBjVj46kbLPjnoslM7LeCkxXh1/js38s0dLVh6yvdTPo5fr0zkoXbLhdr7pIkiQkrjrP9fFKx8twZVAkVr04GPs2aNaNv375cv36dPXv24OXlxaRJk3jxxRfveoxWq0WrvT1UW6PR4O3tLQIfQajhJElizI9HuJiYxcYpXcTMwjXElZRsEjLzaOph99BrneUV6Pl8x2W+3xeD3iBhppAZp9/oG+TGM518mf7rSWNzVEqWFk1+IV+NbsOgVp4m54pOyabXp8Wbuw5EpfL094cBjHNm2Vkq6drIhTkDm9WJNbKqizoZ+FhYFP1hmzFjBk888QRHjx5l+vTpLFmyhLFjx5Z4zLx58wgLCyu2XwQ+glDzSZKEJFEu87AINdfZeDVv/n6acwkazJVy3hnYjGeCfZDJZCYdkAEC3WzYMr1biT8zJTV3Pf39IQ5EpfFsJ1/CBgeJn7UqVCcDH3Nzc9q3b8/BgweN+6ZNm8bRo0cJDw8v8RhR4yMIglB+8oHhN1//DlSXerZCvYFt55No4m5bbAHP+Mw8nv3+MDFpOfzvmXY8GuRe4jludXzuHujCT8935FRcJkMWH0Apl7H7jR7Ud7z3DN5CxaqTq7N7eHjQrJlpO37Tpk35/fff73qMSqVCpRLTgAuCIJQHPbD5jtfVhVIh57EWJS9k7OVgyebpXbmhzsff+e5zRz3W0oNPt1/mQFQqmbkFfLM7CoDBrT1F0FNL3H0e7Wqqc+fOXLpkOunU5cuX8fV9sBkyBUEQhLrJwkxxz6AHIMDFhibuthQaJL7ZfcW4ivrL3R9s1LBQ/dW4wOfVV1/l0KFDfPjhh0RFRbF69WqWLl3K5MmTqzprgiAIQi0w4Gat0dK90QA82syt1LM5C9VXjQt8OnTowPr16/nll19o3rw57733Hp9//jlPP/10VWdNEARBqAUea2naXDapZ8MqyolQEWpcHx+AgQMHMnDgwKrOhiAIglAL3WruupiYxSMB9Wjt7VDVWRLKUY2r8REEQRCEija9dyOauNvy1mNNqzorQjmrkTU+D+vWCH6Npvjq0YIgCMK95dzxWkP1GtlVXjr7WtP5+daAeFZUJ7f+Lx5mJp46GfhkZRUtrOftXXwtFkEQBOHBed4/iSCUu6ysLOzt7ct0bI2bwLA8GAwGEhISsLW1LbawXl10a0LHuLg4MaFjBRL3uXKI+1w5xH2uHOI+m5IkiaysLDw9PZHLy9Zbp07W+MjlcurXr1/V2ah27OzsxC9WJRD3uXKI+1w5xH2uHOI+31bWmp5bROdmQRAEQRDqDBH4CIIgCIJQZ4jAR0ClUjF37lyxnlkFE/e5coj7XDnEfa4c4j6XvzrZuVkQBEEQhLpJ1PgIgiAIglBniMBHEARBEIQ6QwQ+giAIgiDUGSLwEQRBEAShzhCBTy0wf/58OnTogK2tLa6urgwdOpRLly6ZpMnPz2fy5MnUq1cPGxsbhg8fTlJSkkmaadOm0a5dO1QqFa1bt77nNaOiorC1tcXBwaGcS1N9VeZ9liSJhQsXEhgYiEqlwsvLiw8++KCiilatVOZ93rp1K506dcLW1hYXFxeGDx/O1atXK6hk1U953OtTp04xevRovL29sbS0pGnTpnzxxRfFrrV7927atm2LSqWiYcOGLF++vKKLV21U1n3+448/6NOnDy4uLtjZ2RESEsLWrVsrpYw1iQh8aoE9e/YwefJkDh06xPbt29HpdDz66KPk5NxeSvDVV19l48aNrF27lj179pCQkMCwYcOKnev5559n1KhR97yeTqdj9OjRdO3atdzLUp1V5n2ePn0633//PQsXLuTixYv89ddfdOzYsULKVd1U1n2OiYlhyJAh9OrVi5MnT7J161ZSU1NLPE9tVR73+vjx47i6urJy5UrOnTvH//3f/zF79my+/vprY5qYmBgGDBhAz549OXnyJK+88govvPBCnXkoV9Z93rt3L3369GHz5s0cP36cnj17MmjQICIiIiq1vNWeJNQ6ycnJEiDt2bNHkiRJyszMlMzMzKS1a9ca01y4cEECpPDw8GLHz507V2rVqtVdzz9z5kzpmWeekZYtWybZ29uXd/ZrjIq6z+fPn5eUSqV08eLFCst7TVJR93nt2rWSUqmU9Hq9cd9ff/0lyWQyqaCgoPwLUgM87L2+ZdKkSVLPnj2N72fOnCkFBQWZpBk1apTUt2/fci5BzVBR97kkzZo1k8LCwson47WEqPGphdRqNQBOTk5A0TcFnU5HaGioMU2TJk3w8fEhPDy8VOfeuXMna9euZfHixeWX4Rqqou7zxo0badCgAZs2bcLf3x8/Pz9eeOEF0tPTy7cANURF3ed27dohl8tZtmwZer0etVrNihUrCA0NxczMrHwLUUOU171Wq9XGcwCEh4ebnAOgb9++pf77U1tU1H3+L4PBQFZW1j3T1EUi8KllDAYDr7zyCp07d6Z58+YAJCYmYm5uXqw/jpubG4mJiQ987rS0NMaNG8fy5cvr/GJ5FXmfo6OjuXbtGmvXruXnn39m+fLlHD9+nBEjRpRnEWqEirzP/v7+bNu2jbfeeguVSoWDgwPXr1/nt99+K88i1Bjlda8PHjzImjVrmDBhgnFfYmIibm5uxc6h0WjIy8sr34JUcxV5n/9r4cKFZGdnM3LkyHLLf21QJ1dnr80mT57M2bNn2b9/f7mf+8UXX+Spp56iW7du5X7umqYi77PBYECr1fLzzz8TGBgIwA8//EC7du24dOkSjRs3LvdrVlcVeZ8TExN58cUXGTt2LKNHjyYrK4s5c+YwYsQItm/fjkwmK/drVmflca/Pnj3LkCFDmDt3Lo8++mg55q72qKz7vHr1asLCwvjzzz9xdXUt87VqI1HjU4tMmTKFTZs2sWvXLurXr2/c7+7uTkFBAZmZmSbpk5KScHd3f+Dz79y5k4ULF6JUKlEqlYwfPx61Wo1SqeTHH38sr2JUexV9nz08PFAqlcagB6Bp06YAxMbGPlzma5CKvs+LFy/G3t6ejz/+mDZt2tCtWzdWrlzJjh07OHz4cHkVo0Yoj3t9/vx5evfuzYQJE3j77bdNPnN3dy826i4pKQk7OzssLS3LtzDVWEXf51t+/fVXXnjhBX777bdiTYyCCHxqBUmSmDJlCuvXr2fnzp34+/ubfN6uXTvMzMzYsWOHcd+lS5eIjY0lJCTkga8THh7OyZMnjdu7776Lra0tJ0+e5PHHHy+38lRXlXWfO3fuTGFhIVeuXDHuu3z5MgC+vr4PWYrqr7Luc25uLnK56Z9AhUIBFNW61QXlda/PnTtHz549GTt2bInTLoSEhJicA2D79u2l+v+qySrrPgP88ssvPPfcc/zyyy8MGDCgYgpU01Vp12qhXLz88suSvb29tHv3bunGjRvGLTc315jmpZdeknx8fKSdO3dKx44dk0JCQqSQkBCT80RGRkoRERHSxIkTpcDAQCkiIkKKiIiQtFptideta6O6Kus+6/V6qW3btlK3bt2kEydOSMeOHZOCg4OlPn36VGp5q0pl3ecdO3ZIMplMCgsLky5fviwdP35c6tu3r+Tr62tyrdqsPO71mTNnJBcXF+mZZ54xOUdycrIxTXR0tGRlZSW98cYb0oULF6TFixdLCoVC2rJlS6WWt6pU1n1etWqVpFQqpcWLF5ukyczMrNTyVnci8KkFgBK3ZcuWGdPk5eVJkyZNkhwdHSUrKyvp8ccfl27cuGFynu7du5d4npiYmBKvW9cCn8q8z/Hx8dKwYcMkGxsbyc3NTRo3bpyUlpZWSSWtWpV5n3/55RepTZs2krW1teTi4iINHjxYunDhQiWVtOqVx72eO3duiefw9fU1udauXbuk1q1bS+bm5lKDBg1MrlHbVdZ9vtvP/NixYyuvsDWATJIkqXzqjgRBEARBEKo30cdHEARBEIQ6QwQ+giAIgiDUGSLwEQRBEAShzhCBjyAIgiAIdYYIfARBEARBqDNE4CMIgiAIQp0hAh9BEARBEOoMEfgIgiAIglBniMBHEARBEIQ6QwQ+giAIgiDUGSLwEQRBEAShzhCBjyAIgiAIdcb/Azys8JhFUYFUAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/02_scraping/lab_notebook_answers.ipynb b/02_scraping/lab_notebook_answers.ipynb
new file mode 100644
index 00000000..112b9055
--- /dev/null
+++ b/02_scraping/lab_notebook_answers.ipynb
@@ -0,0 +1,1371 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IU2Oe2lgy3XV"
+      },
+      "source": [
+        "# Web Scraping - Anran Zhao"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a8yVtKAxy3XY"
+      },
+      "source": [
+        "## 1. Pick something else on Craigslist: Musical instruments, roommates, antiques, etc. Look at the search page and its source code. Record which fields/data you would like to gather, and what kinds of EDA you'd do with it. I look at used cars below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "bT63yifry3XX"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "\n",
+        "import requests\n",
+        "\n",
+        "header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:124.0) Gecko/20100101 Firefox/124.0'}\n",
+        "url = 'https://charlottesville.craigslist.org/search/rva?purveyor=owner#search=1~gallery~0~0' # I chose RVs\n",
+        "raw = requests.get(url,headers=header)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NiO-E2wuy3XZ"
+      },
+      "source": [
+        "## 2. Get your search results of interest and extract data from them, using code similar to what's below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "lllobrYIy3XY"
+      },
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup as soup\n",
+        "bsObj = soup(raw.content,'html.parser')\n",
+        "listings = bsObj.find_all(class_=\"cl-static-search-result\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "jw-U4BA-y3XY"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import numpy as np\n",
+        "\n",
+        "brands = ['thor', 'airstream', 'forest river', 'winnebago', 'coachman', 'jayco', 'keystone',\n",
+        "          'roadtrek', 'lance', 'gmc', 'catalina', 'pioneer']\n",
+        "\n",
+        "data = []\n",
+        "\n",
+        "for k in range(len(listings)):\n",
+        "    title = listings[k].find('div', class_='title').get_text().lower()\n",
+        "    price = listings[k].find('div', class_='price').get_text()\n",
+        "    link = listings[k].find(href=True)['href']\n",
+        "\n",
+        "    words = title.split()\n",
+        "    hits = [word for word in words if word in brands]\n",
+        "    if len(hits) == 0:\n",
+        "        brand = 'missing'\n",
+        "    else:\n",
+        "        brand = hits[0]\n",
+        "\n",
+        "    regex_search = re.search(r'20[0-9][0-9]|19[0-9][0-9]', title)\n",
+        "    if regex_search is None:\n",
+        "        year = np.nan\n",
+        "    else:\n",
+        "        year = regex_search.group(0)\n",
+        "\n",
+        "    data.append({'title': title, 'price': price, 'year': year, 'link': link, 'brand': brand})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v8TjNu6uy3Xa"
+      },
+      "source": [
+        "## 3. Wrangle your data, do some EDA, and make some plots. Try to find some interesting relationships or stories to tell about your data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "KY1njgHcy3XZ",
+        "outputId": "b9f7fc29-bb1a-47b3-9f12-4b5f41826482",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(11, 6)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                          title  price    year  \\\n",
+              "0                   2016 thor freedom elite 23h  45000  2016.0   \n",
+              "1          2005 airstream 28' international ccd  42500  2005.0   \n",
+              "2  2017 forest river rpod 176 (bunks!) (2017.5)  15000  2017.0   \n",
+              "3        vintage wildwood travel trailer camper   2800     NaN   \n",
+              "4                         2018 kz escape e191bh  13900  2018.0   \n",
+              "\n",
+              "                                                link      brand   age  \n",
+              "0  https://charlottesville.craigslist.org/rvs/d/w...       thor   9.0  \n",
+              "1  https://charlottesville.craigslist.org/rvs/d/s...  airstream  20.0  \n",
+              "2  https://charlottesville.craigslist.org/rvs/d/b...    missing   8.0  \n",
+              "3  https://charlottesville.craigslist.org/rvs/d/b...    missing   NaN  \n",
+              "4  https://charlottesville.craigslist.org/rvs/d/k...    missing   7.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-41e19d28-9567-4743-bffc-6270a0aafc59\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>price</th>\n",
+              "      <th>year</th>\n",
+              "      <th>link</th>\n",
+              "      <th>brand</th>\n",
+              "      <th>age</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2016 thor freedom elite 23h</td>\n",
+              "      <td>45000</td>\n",
+              "      <td>2016.0</td>\n",
+              "      <td>https://charlottesville.craigslist.org/rvs/d/w...</td>\n",
+              "      <td>thor</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2005 airstream 28' international ccd</td>\n",
+              "      <td>42500</td>\n",
+              "      <td>2005.0</td>\n",
+              "      <td>https://charlottesville.craigslist.org/rvs/d/s...</td>\n",
+              "      <td>airstream</td>\n",
+              "      <td>20.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2017 forest river rpod 176 (bunks!) (2017.5)</td>\n",
+              "      <td>15000</td>\n",
+              "      <td>2017.0</td>\n",
+              "      <td>https://charlottesville.craigslist.org/rvs/d/b...</td>\n",
+              "      <td>missing</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>vintage wildwood travel trailer camper</td>\n",
+              "      <td>2800</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>https://charlottesville.craigslist.org/rvs/d/b...</td>\n",
+              "      <td>missing</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2018 kz escape e191bh</td>\n",
+              "      <td>13900</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>https://charlottesville.craigslist.org/rvs/d/k...</td>\n",
+              "      <td>missing</td>\n",
+              "      <td>7.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41e19d28-9567-4743-bffc-6270a0aafc59')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-41e19d28-9567-4743-bffc-6270a0aafc59 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-41e19d28-9567-4743-bffc-6270a0aafc59');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a83efb1e-aa95-4666-b069-6bea4dff665f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a83efb1e-aa95-4666-b069-6bea4dff665f')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a83efb1e-aa95-4666-b069-6bea4dff665f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"rv class b/ camper van\",\n          \"2016 thor freedom elite 23h\",\n          \"off road camper\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25374,\n        \"min\": 2800,\n        \"max\": 89990,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          89990,\n          45000,\n          31000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.80978373727412,\n        \"min\": 1968.0,\n        \"max\": 2018.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2016.0,\n          2005.0,\n          2006.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"link\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"https://charlottesville.craigslist.org/rvs/d/charlottesville-rv-class-camper-van/7783152757.html\",\n          \"https://charlottesville.craigslist.org/rvs/d/waynesboro-2016-thor-freedom-elite-23h/7788165442.html\",\n          \"https://charlottesville.craigslist.org/rvs/d/louisa-off-road-camper/7781545981.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"airstream\",\n          \"roadtrek\",\n          \"thor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.80978373727412,\n        \"min\": 7.0,\n        \"max\": 57.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          9.0,\n          20.0,\n          19.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "df = pd.DataFrame.from_dict(data)\n",
+        "df['price'] = df['price'].str.replace('$','')\n",
+        "df['price'] = df['price'].str.replace(',','')\n",
+        "df['price'] = pd.to_numeric(df['price'],errors='coerce')\n",
+        "df['year'] = pd.to_numeric(df['year'],errors='coerce')\n",
+        "df['age'] = 2025-df['year']\n",
+        "print(df.shape)\n",
+        "df.to_csv('craigslist_cville_RVs.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YBcg4XUoy3XZ"
+      },
+      "source": [
+        "With the data in and wrangled, we can now do EDA:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "qBMwX_rFy3XZ",
+        "outputId": "f5ba84a5-7bcb-4755-970a-01beb1e51379",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "count       11.000000\n",
+            "mean     26608.181818\n",
+            "std      25374.793327\n",
+            "min       2800.000000\n",
+            "25%      11500.000000\n",
+            "50%      15000.000000\n",
+            "75%      36750.000000\n",
+            "max      89990.000000\n",
+            "Name: price, dtype: float64\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "count     7.000000\n",
+            "mean     25.000000\n",
+            "std      21.809784\n",
+            "min       7.000000\n",
+            "25%       8.500000\n",
+            "50%      19.000000\n",
+            "75%      37.500000\n",
+            "max      57.000000\n",
+            "Name: age, dtype: float64\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "print(df['price'].describe())\n",
+        "df['price'].hist(grid=False)\n",
+        "plt.show()\n",
+        "print(df['age'].describe())\n",
+        "df['age'].hist(grid=False)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "xsJPUF2wy3XZ",
+        "outputId": "af40cf8f-8ef4-40ec-ba6d-62440d6653cf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          price                                                              \\\n",
+              "          count      mean           std      min      25%      50%      75%   \n",
+              "brand                                                                         \n",
+              "airstream   1.0  42500.00           NaN  42500.0  42500.0  42500.0  42500.0   \n",
+              "missing     8.0  22523.75  28565.577655   2800.0   9375.0  12950.0  19000.0   \n",
+              "roadtrek    1.0  25000.00           NaN  25000.0  25000.0  25000.0  25000.0   \n",
+              "thor        1.0  45000.00           NaN  45000.0  45000.0  45000.0  45000.0   \n",
+              "\n",
+              "                    \n",
+              "               max  \n",
+              "brand               \n",
+              "airstream  42500.0  \n",
+              "missing    89990.0  \n",
+              "roadtrek   25000.0  \n",
+              "thor       45000.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3f2dd59f-5bed-4b9d-80e5-53be6069074d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"8\" halign=\"left\">price</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>brand</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>airstream</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>42500.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>42500.0</td>\n",
+              "      <td>42500.0</td>\n",
+              "      <td>42500.0</td>\n",
+              "      <td>42500.0</td>\n",
+              "      <td>42500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>missing</th>\n",
+              "      <td>8.0</td>\n",
+              "      <td>22523.75</td>\n",
+              "      <td>28565.577655</td>\n",
+              "      <td>2800.0</td>\n",
+              "      <td>9375.0</td>\n",
+              "      <td>12950.0</td>\n",
+              "      <td>19000.0</td>\n",
+              "      <td>89990.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>roadtrek</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>25000.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>25000.0</td>\n",
+              "      <td>25000.0</td>\n",
+              "      <td>25000.0</td>\n",
+              "      <td>25000.0</td>\n",
+              "      <td>25000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thor</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>45000.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>45000.0</td>\n",
+              "      <td>45000.0</td>\n",
+              "      <td>45000.0</td>\n",
+              "      <td>45000.0</td>\n",
+              "      <td>45000.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f2dd59f-5bed-4b9d-80e5-53be6069074d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3f2dd59f-5bed-4b9d-80e5-53be6069074d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3f2dd59f-5bed-4b9d-80e5-53be6069074d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-83d27325-35d4-4a49-b17d-d4fc43be3a69\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83d27325-35d4-4a49-b17d-d4fc43be3a69')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-83d27325-35d4-4a49-b17d-d4fc43be3a69 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": [\n        \"brand\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"missing\",\n          \"thor\",\n          \"airstream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5,\n        \"min\": 1.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11629.216769941631,\n        \"min\": 22523.75,\n        \"max\": 45000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22523.75,\n          45000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 28565.57765538296,\n        \"max\": 28565.57765538296,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          28565.57765538296\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19498.440108548853,\n        \"min\": 2800.0,\n        \"max\": 45000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"25%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16640.93065055758,\n        \"min\": 9375.0,\n        \"max\": 45000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9375.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"50%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15160.550506715337,\n        \"min\": 12950.0,\n        \"max\": 45000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12950.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"75%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12834.686075890857,\n        \"min\": 19000.0,\n        \"max\": 45000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27712.211959110493,\n        \"min\": 25000.0,\n        \"max\": 89990.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          89990.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "df.loc[:,['price','brand']].groupby('brand').describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "XP4Q0dIdy3Xa",
+        "outputId": "5a6f63a0-4753-40ca-f40d-208fffad0469",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            age                                                 \n",
+              "          count   mean        std   min    25%   50%   75%   max\n",
+              "brand                                                           \n",
+              "airstream   1.0  20.00        NaN  20.0  20.00  20.0  20.0  20.0\n",
+              "missing     4.0  31.75  28.016364   7.0   7.75  31.5  55.5  57.0\n",
+              "roadtrek    1.0  19.00        NaN  19.0  19.00  19.0  19.0  19.0\n",
+              "thor        1.0   9.00        NaN   9.0   9.00   9.0   9.0   9.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-075098b5-ca3a-4f43-974a-77ec9f738aa1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"8\" halign=\"left\">age</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>brand</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>airstream</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>20.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>20.00</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>20.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>missing</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>31.75</td>\n",
+              "      <td>28.016364</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>7.75</td>\n",
+              "      <td>31.5</td>\n",
+              "      <td>55.5</td>\n",
+              "      <td>57.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>roadtrek</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>19.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>19.00</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>19.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thor</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>9.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>9.00</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-075098b5-ca3a-4f43-974a-77ec9f738aa1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-075098b5-ca3a-4f43-974a-77ec9f738aa1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-075098b5-ca3a-4f43-974a-77ec9f738aa1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-62a9ed17-8b7e-4f8e-81ae-8fc38b7bd1b1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62a9ed17-8b7e-4f8e-81ae-8fc38b7bd1b1')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-62a9ed17-8b7e-4f8e-81ae-8fc38b7bd1b1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": [\n        \"brand\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"missing\",\n          \"thor\",\n          \"airstream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.310332521809663,\n        \"min\": 9.0,\n        \"max\": 31.75,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          31.75,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 28.01636426566921,\n        \"max\": 28.01636426566921,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          28.01636426566921\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.701989754294367,\n        \"min\": 7.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"25%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.456182437529678,\n        \"min\": 7.75,\n        \"max\": 20.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"50%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.204844738868042,\n        \"min\": 9.0,\n        \"max\": 31.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          31.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"75%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.36490035985118,\n        \"min\": 9.0,\n        \"max\": 55.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          55.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.093047827819163,\n        \"min\": 9.0,\n        \"max\": 57.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "df.loc[:,['age','brand']].groupby('brand').describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "lHypfI-py3Xa",
+        "outputId": "0552d976-8e30-4dc7-9d6b-3a62cf69a2a1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ax = sns.scatterplot(data=df, x='age', y='price',hue='brand')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "LzFhDn50y3Xa",
+        "outputId": "faabb33e-4b09-47b7-cfe8-4b2626ddf1db",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 554
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "           log_price   log_age\n",
+            "log_price   1.045243 -0.358506\n",
+            "log_age    -0.358506  0.770052\n",
+            "           log_price   log_age\n",
+            "log_price   1.000000 -0.508725\n",
+            "log_age    -0.508725  1.000000\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "df['log_price'] = np.log(df['price'])\n",
+        "df['log_age'] = np.log(df['age'])\n",
+        "\n",
+        "ax = sns.scatterplot(data=df, x='log_age', y='log_price',hue='brand')\n",
+        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
+        "\n",
+        "print(df.loc[:,['log_price','log_age']].cov())\n",
+        "print(df.loc[:,['log_price','log_age']].corr())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "9yERjzXQy3Xa",
+        "outputId": "79fabaf0-28de-4128-aacf-86ad67d6749a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.JointGrid at 0x7f61293ad660>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.jointplot(data=df, x='log_age', y='log_price',kind='hex')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/03_computer_vision/My_Lab_Answers.ipynb b/03_computer_vision/My_Lab_Answers.ipynb
new file mode 100644
index 00000000..044be30f
--- /dev/null
+++ b/03_computer_vision/My_Lab_Answers.ipynb
@@ -0,0 +1,994 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HfZQX0k_YHOK"
+      },
+      "source": [
+        "## Computer Vision\n",
+        "\n",
+        "Let's do some very basic computer vision. We're going to import the MNIST handwritten digits data and $k$NN to predict values (i.e. \"see/read\").\n",
+        "\n",
+        "1. To load the data, run the following code in a chunk:\n",
+        "```\n",
+        "from keras.datasets import mnist\n",
+        "df = mnist.load_data('minst.db')\n",
+        "train,test = df\n",
+        "X_train, y_train = train\n",
+        "X_test, y_test = test\n",
+        "```\n",
+        "The `y_test` and `y_train` vectors, for each index `i`, tell you want number is written in the corresponding index in `X_train[i]` and `X_test[i]`. The value of `X_train[i]` and `X_test[i]`, however, is a 28$\\times$28 array whose entries contain values between 0 and 256. Each element of the matrix is essentially a \"pixel\" and the matrix encodes a representation of a number. To visualize this, run the following code to see the first ten numbers:\n",
+        "```\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "np.set_printoptions(edgeitems=30, linewidth=100000)\n",
+        "for i in range(5):\n",
+        "    print(y_test[i],'\\n') # Print the label\n",
+        "    print(X_test[i],'\\n') # Print the matrix of values\n",
+        "    plt.contourf(np.rot90(X_test[i].transpose())) # Make a contour plot of the matrix values\n",
+        "    plt.show()\n",
+        "```\n",
+        "OK, those are the data: Labels attached to handwritten digits encoded as a matrix."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "fEBKu6bTYHOL"
+      },
+      "outputs": [],
+      "source": [
+        "from keras.datasets import mnist\n",
+        "df = mnist.load_data('minst.db')\n",
+        "train,test = df\n",
+        "X_train, y_train = train\n",
+        "X_test, y_test = test"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "np.set_printoptions(edgeitems=30, linewidth=100000)\n",
+        "for i in range(5):\n",
+        "    print(y_test[i],'\\n')\n",
+        "    print(X_test[i],'\\n')\n",
+        "    plt.contourf(np.rot90(X_test[i].transpose()))\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "2di7Ev1cYO96",
+        "outputId": "165f0eb3-7e85-43a3-d1cb-419f0867cc51"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7 \n",
+            "\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0  84 185 159 151  60  36   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0 222 254 254 254 254 241 198 198 198 198 198 198 198 198 170  52   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0  67 114  72 114 163 227 254 225 254 254 254 250 229 254 254 140   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0  17  66  14  67  67  67  59  21 236 254 106   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  83 253 209  18   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  22 233 255  83   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 129 254 238  44   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  59 249 254  62   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 133 254 187   5   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   9 205 248  58   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 126 254 182   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  75 251 240  57   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  19 221 254 166   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   3 203 254 219  35   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0  38 254 254  77   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0  31 224 254 115   1   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0 133 254 254  52   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0  61 242 254 254  52   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 121 254 254 219  40   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 121 254 207  18   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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WzEb9IV4tkMIuKuaNpbpa/3u7pyBh1oGkaREq8o34AHKm/E3K5MLYKOfUi7oHjClhXnv3OpBytQJkXFZChPUe2UN8ADlmYvfZOH6wm9qWvyjKXyu/cVntCiK/V8CEDRGvuxZ7LTb1e8ql2lSM8Mgm4gMogDAREvcP9aJsy2/a+Ovl/t8nypqZm2b/1TMYytmYiBAexUF8AAXi9w3f5g91IiScIK+X6S3s3aFSK0b8Tj3cCI/8Ij6AAqr0hp/kD3ROxZgTZtM2P9OPSsL+vnLuqQfhkW/EB1BgafshnoUpiInXzPTfL+opF9ui3M8nbd+zCIf4AJA6eX+DSXLKU22xaZTpRyV+djX1O/XI+/dFkbCnMQAkwNQbqXvq4T7lEnS9R9yTEPfUg/AoJuIDABKS9zdUU/dyyfvrVETEBwAkKI1vrHFNP4JOPWYd/FcqXx9ER3wAQMLCvsGaPuViUtSpB9GRb8QHAKSAjTdbvzubSuanH2HXeiCfiA8ASIkgAZLluwD7Od2CfCM+ACBF/LzxeoVHrVMuXlOPuNZ2uE+5RNnXA/lEfABAitTa/yPr4cHUAxKbjAFAZtRaYCplOzxQHMQHAKREtalHmCtbgiwwdQu606mJ8GDqURycdgGAjIkSHnFMPWpdVsvEA25MPgAgBfxOPcKs7xhnKzzCLDBl6lEsTD4AIGF5Dg+mHvBCfABASiUVHn7We5gMD6YexUN8AECCKk090jzx8MLEA0EQHwCQMtV2Lw0SHnExeUktU49iChwfe/bs0dq1a9Xe3q66ujo999xzkz5/8803q66ubtLH6tWrTR0vAOSG19Sj2iW1QcPDxjoPJh4II3B8jIyMaMmSJdq+fXvF56xevVrHjh2b+HjyyScjHSQAFEEawqPaeg/T4cHUo7gCX2q7Zs0arVmzpupzGhoa1NraGvqgACDv3FOPNIRHNUHDA6gmlu+WXbt2ae7cuVq4cKHuvPNOnThxouJzR0dHNTw8POkDAIokj+HB1APVGI+P1atX67HHHtPOnTv1ox/9SLt379aaNWv04Ycfej6/r69PpVJp4qOjo8P0IQFAqlTb18Prfi3j4g4Pr1MucYQHYHyH0xtuuGHiv1922WVavHixFixYoF27dmnFihVTnr9582b19vZO/Hp4eJgAAZBb1U63+Nk2vRJbl9TW4ic8mHog9pN0F110kebMmaNDhw55fr6hoUFNTU2TPgAgj2qt8wjL1l1qAVNiv7fLW2+9pRMnTqitrS3uPwoAUqvaqRYp3EZitqcdYe7Z4sbUA1KI+Dh9+vSkKcbhw4e1f/9+NTc3q7m5WVu3btW6devU2tqqgYEB3X333br44ou1atUqowcOAFlRaz+PMKdb0nCahUtrEVbg+Hjttdd0zTXXTPx6fL3G+vXr9dBDD+nAgQP65S9/qZMnT6q9vV0rV67UD37wAzU0NJg7agDICD8bidWSxC6mQJwCx8fVV18tx3Eqfv73v/99pAMCgLwIuoOpFGyRaZYw9UA5doUBgBjUWuMhVb+sFsiz2BecAkDRhD3V4jX1sHXKxb3HBzePQ5yYfACAQX7DI8rUIw2LTYEomHwAgAGVTrP4DQ/WeqBImHwAQERBwiOIrF/lQnigEuIDACIIGh55mHoE3d8DcOO0CwCEFGd4VJp6mF7v4XUzObcwO5sy9UA1TD4AwKA8hEeQe7p4TT0ID9TC5AMAQvCzj8e4LIdH+dTDz+kWwgN+EB8AEFCUK1uSjg7JOzxM7OtBeMAvTrsAgAFR9vIgPFA0TD4AIIAgp1vcguxgmsbwqHRVC+GBoIgPAIgozOkWm9EhBQ8Pv5fTEh4Ig/gAAJ/Cbp2e9vAIe98WwgNhER8A4IPf0y1pDw9TN4wjPBAF8QEAIbmnHmHCIy2nWSTCA/YQHwBQhd/LavMUHtW2Syc8YALxAQAVmLphXJrCI+y0QyI8YA7xAQAeouxgWutGcVlb3wGYRnwAQJla0RH1dEtWw4OpB0wiPgAgpDyEh59pB+EB04gPAPh/QaceaRB14lEN0YG4cG8XAPAh6OmWLGBxKZJCfACAot2zJS1MTT0ID8SN0y4AUEOtqYeXSjuZmuJ1Sa0JhAdsYPIBoPCiTj1sn3KJa/dSwgO2EB8AUEWYqUecmHggDzjtAqDQqk09/FzdYmvqUS06ok49CA/YxuQDADx4hYffqYfp9R5xTTuApBAfAArL5hUuYTcYCxoeTD2QBZx2AQAXv1OPOE+5+I0O9ykXIAuYfAAoJFN3rHUzccqF0yzIO+IDQOEEDQ+bU48g4cFCU2QVp10AoApbl9ZGiQ4ga5h8AICCnW6pNPUIe8rFRHgw9UCWEB8AUEGQ0y1xb6cumQsPIGnEB4BCSdsN5KJe1cI26sgi4gNA4UW9tNbG1MML4YGsIj4AwIe0nW7hVAuyjPgAABf31CNqeITd3VQyGx5MPZAWxAeAQgu7qViSC0zLucOjEsIDaUJ8ACgMP4tN/U49TAizk6l76uHG6RZkAfEBAAFlbZ0HUw+kDfEBoLBqnXLxmnoQHkB0xAcA/L9aW6lnLTyAtCI+AMCDe+phOjz8rveIEh5MPZBWxAeAQnAvNnWfcrF1A7lqot6lthzhgTQjPgDAJe6phxeT4QGkHfEBIPeyMPUoFzU8mHog7WYkfQAAYFOt8Ihj6lG+vuOm2X+dst7Dz2ZifhAdyAomHwByrXzqETQ84hB0YzEuqUUeER8AcqvajqZ+TrWkYa2HH4QHsobTLgByxys6wmwoljbuqQfRgaxi8gEgV/yEh5/TLWmYevi9aRyQNXxnA8iNMOHhxUZ4AEVGfADIhbDhkdTplqj7enDKBVnGmg8AmVZpUWnY8GDqAcQv8ORjz549Wrt2rdrb21VXV6fnnntu0ucdx9F9992ntrY2zZo1S93d3XrjjTdMHS8ATPAbHibcNPuvxr4WUw8UXeD4GBkZ0ZIlS7R9+3bPzz/wwAP66U9/qocfflivvvqqzj77bK1atUrvvfde5IMFgHFBwiPq1GM8PEwGCFBkgU+7rFmzRmvWrPH8nOM42rZtm+655x596UtfkiQ99thjamlp0XPPPacbbrgh2tECgOIJD7+8dij1y2snU6YeKCKjC04PHz6swcFBdXd3TzxWKpXU1dWlvXv3ev6e0dFRDQ8PT/oAAC/vLrqg4sJSv+FRSa2pR63HajG1hTqQB0bjY3BwUJLU0tIy6fGWlpaJz7n19fWpVCpNfHR0dJg8JAA5EXR9R6XwSGKRaaXwCLObKZAHiV9qu3nzZg0NDU18vPnmm0kfEoCMCBIe53QOpSo8vHDKBUVhND5aW1slScePH5/0+PHjxyc+59bQ0KCmpqZJHwBQrto9WtyCrPFIMjxY64EiMxofnZ2dam1t1c6dOyceGx4e1quvvqply5aZ/KMAFERcV7UkuZ9H0PAA8ibw1S6nT5/WoUOHJn59+PBh7d+/X83NzZo3b542bdqkH/7wh/rEJz6hzs5O3XvvvWpvb9e1115r8rgBFIDNy2mD8HO1i991Hn7Cg6kH8iZwfLz22mu65pprJn7d29srSVq/fr0effRR3X333RoZGdHtt9+ukydP6nOf+5xefPFFfexjHzN31AByL63h4QfhAVRX5ziOk/RBlBseHlapVFJ329c1Y5r5XQoBZIOfe7VI/sIjanS4L62tNvkwGR4S8YHs+GDsjF469gsNDQ3VXL+Z+NUuAODmd4GpjfAwgfAAJuPGcgBSo1p0+Llfi43wCDr1IDyAqZh8AEiFoOHhnnokPfEwGR5A3hEfABKX9fDwEiU8mHog7zjtAiAxtdZ2ZCU8uG8LEAzxASARQXYtHRc0PMqvUgl7J9pyXl/D9OkWph4oAuIDgHV+wsM99YgSHl6/DuLxt6/0/P2EBxAOaz4AWGUjPEzzGy4sMAX8YfIBwBpb4RFlylGJ+5SLe+rBxAPwj8kHgNRI28RjnIn1IpUQHigiJh8ArAhyZUuUnUtNTj0qRYepqQfhgaIiPgAkzlR4mMS0A4gP8QEgdn43Eat1mkWqHh6mph5BtlBnkSkQHPEBIDFBwiMP6zskph6AxIJTADGrNPWodqO4MOFhYupRKzyYegBmEB8AYuM3PMqnHmmdeNQKDz+YegAfIT4AWBVHeESdegQNDy/uqQeAyljzASAWXlOPtE08/Kzv8LOFuhf3KRemHsD/kOoArMhLeHhh6gEEw/9jABjnnnrEGR5hTrlECQ+mHkB0nHYBYJTN8AjDxPqOckw9gOCIDwDGVAuPNOzjETU8mHoAZhAfAIwIGx5Ro+Pxt6+M5S62brX29JAID8Av4gNAZOXhEeTOtKamHeMTjWoREmXqQXgAZnGyEkBo7y66wHd4nNM5FEt4lAu7NTrhAdhFfAAIJa0LSx9/+8opERI2SggPIB7EB4DAaq3vSPqKFul/wRH2dAvhAcSHNR8AAkl6fUcQhAeQTkw+APhmKzxsXL1SCeEBxI/JB4CaktixdPw/w67XqMXPPVvc4eGODonwAMJg8gGgKpvrO7wmHnFMQQgPIFlMPgBUZHN9R7XIuGn2X2ObgHghPIB4MfkA4Ckt4RHkOX74mXrUQngA0TD5ADCJ7f07gkRF1HUgJk63EB5AdEw+AFRUa+JRLu7wiAPhASSD+AAwwT31qMY99QjKdni4px6EB5Ac4gOAL9WmHkERHkCxseYDQGR+T7mYiI6g6z2ihgcA84gPAJ7c6z3KhTnlkvS0Q2IvDyAtiA8AkoKt9wjKVHj4nXqYmnYQHkA8iA8ANYW9yiWJq1k4zQKkHwtOAUxR7ZSLX6bDw8/Uw2R4MPUA4kN8AAjEz3qPrE88CA8gXpx2AVB1vUeQUy5JbRrGqRYgW5h8AEi9aqdcTIcHUw8gfsQHgEnCXmKb9VMtEuEB2EJ8AAglzL1cwqg09eBUC5BdxAeAikxuqW5SHOHB1AOwhwWnQIG5F5qWn3Jxh0f5KRdbC029ph6EB5B9TD6AgjIVHnEhPID8YvIBFJDf8HAvMPUKjzimHoQHkG/EB1Aw5eHhvrKlKOFBdADJIj6Agqg27ZCCh0cc3OER5s60tRAeQPKID6AAwoaHrZvG+Zl2SP7Cg23TgfQjPoCcy8PCUilaeBAdQLoQH0BOBZl2SMHCw9TUI8z6DonwALKO+AByKM7wMCHsrqUS4QHkAfEB5EzY0yySv/CIOvUIe5pFIjyAvDC+ydj999+vurq6SR+XXHKJ6T8GgAf3ZbSmwyOqKOs7CA8gP2KZfFx66aV66aWX/veHzGDAAsTJ1mmWsFMPv9Eh+b+UlhvFAdkVSxXMmDFDra2tcXxpAC5p37/D9GkWictpgayL5d4ub7zxhtrb23XRRRfpa1/7mo4cOVLxuaOjoxoeHp70AcCfWus7TIZHmKkH4QHAi/H46Orq0qOPPqoXX3xRDz30kA4fPqzPf/7zOnXqlOfz+/r6VCqVJj46OjpMHxKQS7UmHiYFDY/H376S8ABQUZ3jOE6cf8DJkyc1f/58/eQnP9Gtt9465fOjo6MaHR2d+PXw8LA6OjrU3fZ1zZgW3w9TIMuirPGQ/E89TK7xkOKLD8IDSN4HY2f00rFfaGhoSE1NTVWfG/tK0HPPPVef/OQndejQIc/PNzQ0qKGhIe7DAHIjanj4ZfqmcXFOPQBkSyxrPsqdPn1aAwMDamtri/uPAnLPHR5h2NjLwzROtwD5Ynzy8e1vf1tr167V/PnzdfToUW3ZskXTp0/XjTfeaPqPAgrFKzxMTz3SFh0A8sl4fLz11lu68cYbdeLECZ1//vn63Oc+p1deeUXnn3++6T8KKIww4eHF1l1qw+IutUAxGI+Pp556yvSXBAotbHgEmXokER5e6z3cWOcB5FPsaz4AhOcnPLykPTy8uKcetcKDqQeQXcQHkFJ+w8PP6RbJzm6mlVTaSj0swgPINuIDSKEo4ZGFqUetUy6cbgHyjfgAUibsqZZqkpx6+FFpbw8vTD2A7CM+gBQJEh5Rpx5pxdQDyD/iA0iJuMKj0tQjrQtNq2HqAeQD8QGkQNTwyBI/l9gCyLfY7+0CoLogW6bnaeLhhQ3FgGJg8gGkUNQrW7IYHgCKg/gAEhTlyhbCA0BWER9AQqKcbiE8AGQZ8QGkiJ/TLYQHgKxjwSmQAJNbpxMdALKGyQeQUn4WmBIeALKI+AAsM3WnWsIDQFYRH4BFYU+3EB4A8oT4ACzxe3UL4QEg71hwCsSsWnS4px5+r2wpR3QAyBomH0CMgoSHH+6pB+EBIIuIDyAmQTYRk4KfbiE8AGQV8QHEoFZ4mDjdAgBZRXwAGVBpkWkW/OHIwqQPAUDKEB+AYVGnHn6k+ZTL429fmfQhAEg54gNImSKdcmkc8PcjaNbBf8V8JABsIj4Ag2xMPbLs9OFS0ocAIAWIDyDlWO8BIG+ID8CQoFMPL1k/5RLHeg9OuQD5Q3wAFvi5f0vWsdAUgF9srw4YEHRDsTwhOgAExeQDiJnfqYfXKZe0r/cwGR6z+89MeYxTLkA+ER9AjMLcv6WWtOzxUSs83ItN3Ve6+L3MFkD+cNoFiCjqPVyyhtMsAKLinx5ATIJMPbJylQvhAcAE4gOIoNLUo1J4BJl6pG29h+3wYL0HkF+cdgFQVZjoqLXeA0CxER8AKoojPNwLTd1XuTDxAPKP+AAwRVzTDsIDgER8ACgTdl0H0w4AQRAfACItJi0PD6YdAPwgPoCCY9oBwDbiAygoU9MOifAAEAzxARQQ0w4ASWKTMaBgCA8ASWPyARQE0QEgLZh8AAVAeABIEyYfQM6ZCA+iA4BJxAeQU0w7AKQV8QHkUBzhQXQAMIX4ACKYdfBfenfRBVMen91/Rm8vrJ/yeOPANJ1aMDbl8dOHSzqnc2jSY+MhsHJe/6THH3/7St00+69THouCaQcAm4gPIKIwATKuPETK3/DLQ8QrQkzc+K2SauFBdAAwgfgADBh/E3ZHyPibtVeESLVDxCtCpKnTEPfnw2DaAcAW4gMwKOgUpNz4m32QaUgUXjeBcx/LuPLwIDoAREV8AIZFCRAp2DSkkmph4ffPlph2AIgH8QHEoNZpmHFRpyEmMe0AYAvxAcSo0hRknN8YqTQN8csdFtUw7QAQN+IDiFmtAClX/sbvJ0RMIzwA2EB8ABZUOg1TTdBTNFEQHQBsii0+tm/frh//+McaHBzUkiVL9OCDD2rp0qVx/XFAJpS/qQcJEWlqIMSF8AAQt1ji41e/+pV6e3v18MMPq6urS9u2bdOqVavU39+vuXPnxvFHAplT7U0+aJiYQHQAsKXOcRzH9Bft6urSFVdcoZ/97GeSpLGxMXV0dGjjxo363ve+V/X3Dg8Pq1Qqqbvt65oxLb4xM5BlleKEgACQlA/GzuilY7/Q0NCQmpqaqj7X+OTjzJkz2rdvnzZv3jzx2LRp09Td3a29e/dOef7o6KhGR0cnfj009NEeBh+M2RkxA1k088Bhz8c/sHwcADBu/H3bz0zDeHz897//1YcffqiWlpZJj7e0tOj111+f8vy+vj5t3bp1yuO7jj9i+tAAAEDMTp06pVKp+n5EiV/tsnnzZvX29k78+uTJk5o/f76OHDlS8+AR3fDwsDo6OvTmm2/WHJMhOl5vu3i97eL1tittr7fjODp16pTa29trPtd4fMyZM0fTp0/X8ePHJz1+/Phxtba2Tnl+Q0ODGhoapjxeKpVS8WIWRVNTE6+3RbzedvF628XrbVeaXm+/QwPjuxXV19fr8ssv186dOyceGxsb086dO7Vs2TLTfxwAAMiYWE679Pb2av369frMZz6jpUuXatu2bRoZGdEtt9wSxx8HAAAyJJb4uP766/Wf//xH9913nwYHB/XpT39aL7744pRFqF4aGhq0ZcsWz1MxMI/X2y5eb7t4ve3i9bYry693LPt8AAAAVBLfHaoAAAA8EB8AAMAq4gMAAFhFfAAAAKtSFx/bt2/Xxz/+cX3sYx9TV1eX/va3vyV9SLl0//33q66ubtLHJZdckvRh5caePXu0du1atbe3q66uTs8999ykzzuOo/vuu09tbW2aNWuWuru79cYbbyRzsDlQ6/W++eabp3y/r169OpmDzbi+vj5dccUVamxs1Ny5c3Xttdeqv79/0nPee+899fT06LzzztM555yjdevWTdl4Ev74eb2vvvrqKd/fd9xxR0JH7E+q4uNXv/qVent7tWXLFv3973/XkiVLtGrVKv373/9O+tBy6dJLL9WxY8cmPl5++eWkDyk3RkZGtGTJEm3fvt3z8w888IB++tOf6uGHH9arr76qs88+W6tWrdJ7771n+UjzodbrLUmrV6+e9P3+5JNPWjzC/Ni9e7d6enr0yiuv6I9//KPef/99rVy5UiMjIxPPueuuu/Sb3/xGTz/9tHbv3q2jR4/quuuuS/Cos8vP6y1Jt91226Tv7wceeCChI/bJSZGlS5c6PT09E7/+8MMPnfb2dqevry/Bo8qnLVu2OEuWLEn6MApBkvPss89O/HpsbMxpbW11fvzjH088dvLkSaehocF58sknEzjCfHG/3o7jOOvXr3e+9KUvJXI8effvf//bkeTs3r3bcZyPvpdnzpzpPP300xPP+ec//+lIcvbu3ZvUYeaG+/V2HMf5whe+4Hzzm99M7qBCSM3k48yZM9q3b5+6u7snHps2bZq6u7u1d+/eBI8sv9544w21t7froosu0te+9jUdOXIk6UMqhMOHD2twcHDS93qpVFJXVxff6zHatWuX5s6dq4ULF+rOO+/UiRMnkj6kXBgaGpIkNTc3S5L27dun999/f9L39yWXXKJ58+bx/W2A+/Ue98QTT2jOnDlatGiRNm/erHfeeSeJw/Mt8bvajvvvf/+rDz/8cMouqC0tLXr99dcTOqr86urq0qOPPqqFCxfq2LFj2rp1qz7/+c/r4MGDamxsTPrwcm1wcFCSPL/Xxz8Hs1avXq3rrrtOnZ2dGhgY0Pe//32tWbNGe/fu1fTp05M+vMwaGxvTpk2bdNVVV2nRokWSPvr+rq+v17nnnjvpuXx/R+f1ekvSV7/6Vc2fP1/t7e06cOCAvvvd76q/v1/PPPNMgkdbXWriA3atWbNm4r8vXrxYXV1dmj9/vn7961/r1ltvTfDIAPNuuOGGif9+2WWXafHixVqwYIF27dqlFStWJHhk2dbT06ODBw+yXsySSq/37bffPvHfL7vsMrW1tWnFihUaGBjQggULbB+mL6k57TJnzhxNnz59yoro48ePq7W1NaGjKo5zzz1Xn/zkJ3Xo0KGkDyX3xr+f+V5PzkUXXaQ5c+bw/R7Bhg0b9MILL+jPf/6zLrzwwonHW1tbdebMGZ08eXLS8/n+jqbS6+2lq6tLklL9/Z2a+Kivr9fll1+unTt3Tjw2NjamnTt3atmyZQkeWTGcPn1aAwMDamtrS/pQcq+zs1Otra2TvteHh4f16quv8r1uyVtvvaUTJ07w/R6C4zjasGGDnn32Wf3pT39SZ2fnpM9ffvnlmjlz5qTv7/7+fh05coTv7xBqvd5e9u/fL0mp/v5O1WmX3t5erV+/Xp/5zGe0dOlSbdu2TSMjI7rllluSPrTc+fa3v621a9dq/vz5Onr0qLZs2aLp06frxhtvTPrQcuH06dOT/tVx+PBh7d+/X83NzZo3b542bdqkH/7wh/rEJz6hzs5O3XvvvWpvb9e1116b3EFnWLXXu7m5WVu3btW6devU2tqqgYEB3X333br44ou1atWqBI86m3p6erRjxw49//zzamxsnFjHUSqVNGvWLJVKJd16663q7e1Vc3OzmpqatHHjRi1btkyf/exnEz767Kn1eg8MDGjHjh364he/qPPOO08HDhzQXXfdpeXLl2vx4sUJH30VSV9u4/bggw868+bNc+rr652lS5c6r7zyStKHlEvXX3+909bW5tTX1zsXXHCBc/311zuHDh1K+rBy489//rMjacrH+vXrHcf56HLbe++912lpaXEaGhqcFStWOP39/ckedIZVe73feecdZ+XKlc7555/vzJw505k/f75z2223OYODg0kfdiZ5vc6SnEceeWTiOe+++67zjW98w5k9e7Zz1llnOV/+8pedY8eOJXfQGVbr9T5y5IizfPlyp7m52WloaHAuvvhi5zvf+Y4zNDSU7IHXUOc4jmMzdgAAQLGlZs0HAAAoBuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGDV/wGHSN1sCTxJNwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2 \n",
+            "\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 116 125 171 255 255 150  93   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0 169 253 253 253 253 253 253 218  30   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0 169 253 253 253 213 142 176 253 253 122   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  52 250 253 210  32  12   0   6 206 253 140   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  77 251 210  25   0   0   0 122 248 253  65   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  31  18   0   0   0   0 209 253 253  65   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 117 247 253 198  10   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0  76 247 253 231  63   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0 128 253 253 144   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0 176 246 253 159  12   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0  25 234 253 233  35   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 198 253 253 141   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  78 248 253 189  12   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  19 200 253 253 141   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0 134 253 253 173  12   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0 248 253 253  25   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0 248 253 253  43  20  20  20  20   5   0   5  20  20  37 150 150 150 147  10   0]\n",
+            " [  0   0   0   0   0   0   0   0 248 253 253 253 253 253 253 253 168 143 166 253 253 253 253 253 253 253 123   0]\n",
+            " [  0   0   0   0   0   0   0   0 174 253 253 253 253 253 253 253 253 253 253 253 249 247 247 169 117 117  57   0]\n",
+            " [  0   0   0   0   0   0   0   0   0 118 123 123 123 166 253 253 253 155 123 123  41   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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qa2ur7+Nob2/X7Nmz1d7ervvvv1/9/f3q6OjQnDlz9NBDD2nVqlWc6QJXYQJEmhohzVMQyX0pxiSmHekKe98WJh5A8UWKjx/+8IeSpDVr1kz6/J49e3TvvfdKkv75n/9Z06ZN09atWzU+Pq4NGzboX//1X40cLMop7DKMFBwhzVOQKKJcm4HwyAbhAZRD5GWXILNmzdLu3bu1e/fu2AeFago7BZHCbUiVPo4QUxd9IjqSC3NqbZz7ttSw5ALkH/d2Qa5EDRApeCnGhDC3QSc8popzQbk8XssDgFnEB3Kn8U3c1H4QN0nuMEp4mBPmSqYstwDlQnwg16KEiNepuSYRHckEneWSZLlFYskFKAriA4URJkTcTs1Nwu9S2IRHckFTDyYeQDkRHyikoBBpjhCT99MgOswI2mRKeADlRXyg8PxO1TV9Ey/CI7zmPw+/aZSJ8GDJBSgO4gOlEXWjatDXQHr8lluYeADlR3yglIiIfIky9YiDqQdQLOZPBwBQeY2Tp+bwCHNqbRR5Co+ol+BvRjSjKogPAEb5LXmZ3mRqOzya75Js8v5BhAeqhPgAYIzfJtMyTzySIjxQNez5AJCKqBcUy/u9W4KmHnGXXAgPVBHxASCxoNNqw9w4Loy8hIcphAeqivgAkEjQac2m7t2SRXjEjY4wUw/CA1XGng8AsbmFR5z7twTJW3gELbkEITxQdUw+AETmNe1I47TavIVHUoQHQHwAiChJeES9a23Wp9K6MbXRFKgyll0AhGY6PPzkMTwAmMHkA0CgsNERldfUo6jhETT1YMkFuIz4AOArangknXrYDI+o0ZF0oymAy1h2AeDJRHh4yfrutbaXWZh6AB9j8gHAVZoTj6yXW+KEBxtNAXOIDwBT2A6PPC+1SMnDg6kHMBnxAWCStMIj62lHXEw8APOIDwB1ScMjr8ssNaY3mHIZdSAe4gOAJLvhUYSrljLxANJDfAAIvDlcszDhkZdpRxymwoOpB+COU22BivMLD7epRxHDI8rUg/AA0sfkA6gwW+FRhGmHxFILYAvxAVSU6fDI27SjJuzUw2R4MPUA/BEfQMUE7e+oWng0R4dEeABpIz6ACrEVHllHh5RNeAAIh/gAKqIq4RF3mUVKHh5MPYBwiA+g5MKcRkt4BIdHEMIDCI/4ADBF2AuINSpzeLDUApjFdT6AEosz9XC7V0szrw2mWcly4iEx9QCiYvIBoNDi3KW2pjk83HBmC2Aekw+gwrzu29IozJJLVkxeuVTilFrAFuIDKKmo92uRwi25FJHbckuzKMsthAeQDMsuQEWFmXqEVYS71DaKu9xCdABmMPkAKijMqbVStJvG2ZT2cosbwgMwh/gAUFpxl1s4tRZIF/EBlFDUm8YVicnTat2w3AKkj/gAEHrJJWumwyPONT0AJMeGU6BCij71CBJmmaUm7HILUw/APLIfKJmop9hGOb02y82mQVMPv/BIejExAGYRH0BFRJl65HHJxU/U8Ai73MLUA0gH8QFUWFEuKuY39TARHiy3AHax5wMoCa/lFq+Jh1d4eE09vJZc0r7AmMnwAJAPxAdQAm7hETU6pHKHh9dSC1MPwD7iAygwU9EhRbuaqY3LqbPUApQX8QEUUJQlljjRIRUvPLyWWQgPIH+ID6Bg0l5ikfK5zCIRHkBZEB9AQdiIDimf4RFnYynhAeQX8QEUQHN4lCk6pGz2d0iEB5AV4gPIuTDhkSQ6pHxOOyQ2lgJlRXwAORVmmSVpdEiEBwD7iA8gh4KmHWlNOmryGh7s7wDKIfLl1Q8ePKjNmzeru7tbLS0tev755yf9/r333quWlpZJvzZu3GjqeIHSixseV/WOhNrX4Rce98x9NdXweOa92wL3dxAeQPlFnnyMjY1pxYoV+qu/+ivdcccdro/ZuHGj9uzZU/+4tbU1/hECFRF3mcXEpEPK77RDYpkFKJvI8bFp0yZt2rTJ9zGtra3q7OyMfVBA1cSZdsQ9e8UN4QHAplT2fOzfv18LFizQ3Llz9aUvfUmPP/645s2b5/rY8fFxjY+P1z8eHR1N45CA3IoaHlWJDonwAMoq8p6PIBs3btSPfvQj7du3T//4j/+oAwcOaNOmTfroo49cHz8wMKD29vb6r56eHtOHBFQG4XEZ4QHkm/HJx1133VX/55tuuknLly/XkiVLtH//fq1du3bK43fs2KH+/v76x6OjowQIKiPJWS3N8hQeQQgPoNpSP9X2+uuv1/z583XixAnX+GhtbWVDKhBS1JvAuck6PII0hwfRAZSP8WWXZm+99ZbeffdddXV1pf2tgEqKEh62mLpcOuEBlFPkyceFCxd04sSJ+scnT57U0aNH1dHRoY6ODu3atUtbt25VZ2enhoaG9Mgjj+iGG27Qhg0bjB44gOjhkeepB+EBVEfk+Pjd736nL37xi/WPa/s1tm3bph/+8Ic6duyY/vM//1Pnzp1Td3e31q9fr3/4h39gaQUIEPUslyKGR9BejxrCAyi3yPGxZs0aOY7j+fv/8z//k+iAgKpwu6hYWHkOj6CzXNw0Tj0ID6D8Ut/zASC5sDeJc5OHiYcU/X4tNYQHUD7EB5BDUU6xLbKgfR6EB1BOxAeQA837Pfzk+bRaryUXt6kH4QFUV+rX+QAwVZT9HnGXXPISHm6ihgfRAZQLkw8gZ0wsueQpPILOcCE8gOph8gEUSJgllyKf2UJ4ANXA5APImN9+j6hLLnkLj+aph9+ZLYQHUB1MPgCL/G4kl/SiYkULD7fredQQHkC5ER+AJXHDowxLLc3CnNkCoLyID8CCtMIjTxtLG0VZbgFQPez5AFJWlvCIi9NqATRj8gGkqDE8otw4Lo/hEXfqAQDNiA8gJWHDI+ndam0wtdzCXg8AEvEBGOe3zCIVLzzCSnJ2Sw1LLkA1sOcDMMhGeORxk2mcpRamHkB1MfkADEnzGh5ZiRsecaYeAKqDnwiAAbbCI2/X8zC5uZQlF6A6mHwACcU9o0XK78QjDK/wCDP1YMkFqDbiA0jAZnjkZerhN+3gYmIAwiA+gBiSbCyV8j3xMBkeYaceLLkA1cKeDyChoIlHszjhkYermTLxAGAKkw8gouaphx8TezzysNxiYuIhMfUAcBnxASQQdeoRRR6v59EsyVKLRHgAVUV8ABHYmnrkKTzCntUisccDQDjs+QBCirLJNAnCA0DZER9AjhAeAKqAZRcghKhTjzhLLnnYWBqE8ABgAvEBBIiyzyOOPE07asLcr0WaGh5sLAUQBvEB+HALD5NTj6KEh5sw4UF0AHBDfAAeyjTxCLvMEvd+LYQHgCiID6CJX3REnXp4ITwAVBlnuwANTE873JZc8hgeXggPAGlg8gH8f0HhYeK6HrbCI2p0hNlgSngAMIX4QOWFmXY0h4cbE/dxMSFKeMRdanFDeAAIi2UXIIY4Z7jYmHrYCo/mqQfhASAK4gOVFmfqEXeTaVl4XcsDAMIiPgAfccKjyMstcTD1ABAV8YHKSrrBNE/hYUqcvR4AEBU/WQAXpsPD9pVM/ZicegBAHMQHKsn0hcSKPvGQ3O/d0oz9HgBMID6ABmUPjyhTD5ZcAKSFny6oHK+pR9nDAwDygvgAlG545GW/h9/UI8ySixvOdAEQB/GBSnGbesS5bHrZJx5hLiwGAHERH6iMuDeNy8tl0/0EXeMjjakHAMRFfKDSoi635DE8AKBoiA9UQpzlFhPhkYf9Hkw9AOQN8YFKYuLhjf0eANI2I+sDANLWPPWoysRDSnfqwZkuAOIiPlBqScMjDtvh4bXZNGp4eF1UzG3qQXgASIL4QGlFDQ83UaceZQoPr6UWwgNAUsQHSilOeCSZeuQlOqT0woPoAGAKG05ROnGu51GkK5gSHgCKjvhAqZg4pVYiPBoRHgBMY9kFpRD3ZnFx5Sk6pOThwf4OADYRHyi8JOERZ+qRt/DwEzc8iA4AaWLZBYVmOjyC5DE8vKYehAeAvIocHwcPHtTmzZvV3d2tlpYWPf/885N+33EcPfbYY+rq6tLs2bO1bt06vfHGG6aOF6jz2t+RJDy8ph73zH3Vang8895thAeA0oocH2NjY1qxYoV2797t+vtPPPGEfvCDH+ipp57Sa6+9pk984hPasGGDPvzww8QHC9SE2VgaVV4uoR42OggPAEUVec/Hpk2btGnTJtffcxxHTz75pL7zne/oK1/5iiTpRz/6kRYuXKjnn39ed911V7KjBRQtPEwst9ieeASJsrmUM1oA5JHRDacnT57U8PCw1q1bV/9ce3u7Vq5cqUOHDrnGx/j4uMbHx+sfj46OmjwklEjY/R2S91ktcZZbbEgaHVK88CA6AGTB6IbT4eFhSdLChQsnfX7hwoX132s2MDCg9vb2+q+enh6Th4SSi7LUQngQHgDyIfOzXXbs2KGRkZH6rzfffDPrQ0LBmdpgagPhAaCKjC67dHZ2SpLOnj2rrq6u+ufPnj2rz372s67/Tmtrq1pbW00eBkooyQbTvIZHUmwsBVBURicfvb296uzs1L59++qfGx0d1WuvvaZVq1aZ/FaAq7CXTi9KeARNPRoRHgCKIvLk48KFCzpx4kT945MnT+ro0aPq6OjQokWLtH37dj3++OP61Kc+pd7eXj366KPq7u7Wli1bTB43MEWSe7bkLTqCNE49OKMFQNFEjo/f/e53+uIXv1j/uL+/X5K0bds2Pf3003rkkUc0Njamr3/96zp37py+8IUv6KWXXtKsWbPMHTUqL2jJpQzhEeY6HuzvAFBEkeNjzZo1chzH8/dbWlr0ve99T9/73vcSHRgQRdAN4wgPAMgPbiyH0mmcehQtOvz4hQfLLACKJPNTbYEgzWe6+N27pUjh4XWardvUg/AAUCZMPlApeQmPKNxOqa1hmQVAETH5QKFFmXoUITyCTq1tnHoQHgCKiskHCiXOnWuLEB1evJZbCA8ARcbkA4UVZupRpPBonnoQHgDKivhArjVuNm2cehAelxEeAIqI+AByJuhaHjWEB4CiIj4AAIBVxAcAALCKs12AHDu/ZMJ36QWAv+aLFJZNUZdfiQ8AQOmUPTpqav+dRYsQ/koFACiND5ZdW5nwaFS0/2YmHwCAwivam28aijQFIT6Agvpg2bWF+CEDpM0rPOJcEbmImq//U4SfDSy7AAAKyWuJ5b2lMysTHpJ7ZOV9EsTkAwBQKFWfdLip/bc3TkHyvAzD5APImcbLxQP4mN9m0iqHR6OiTEGYfAAF8t7SmVPWd4Gy83vz9IqOxvs/lV3ztYDcfk7kbQrC5AMosDz+jQYwyW/SQXhcdn7JxJT/Zq/nJy8/M5h8AAXX/MMkL3+zAeKKM+mQqhcdzdyuiJzXKQjxAWTknrmv6pn3bpMkrV80KEn65amlki7v+6jd3bb2A7X2QyVo6SUvf7PJE4LMXxFeM0w5wgkbIFK2p+QSH0CG7pn7qiRNipDGAJE0KUIaA0Saen4/3IV9cy1TpBQhKIIkmXJUaeN27WdETREChPgAcqB5ClILEGnqFKTxh0qcHf4Ei7c4b9g2fnCXPSTCIjjcNf6MqPEKECkfFyUjPoCciLsMExWnJJoNsCKGQZFeAwRHOGEDRPLeB2IzQIgPIEeiLsM0ixskVeP15lumqVCRAsONX3QQHO6KFCDEB5BDYZdhmrH5zluYMPN7w85rmCSJjCK9XgiOcLwCRMrX9UCIDyCnwi7DIJykkyIb+2vSnlYUKTak+MFR+/+lChr/YlLj9fMhTxtRiQ8gx8Isw8CfX6SlvXSV5dJHlNAo8mupSqHhpvkvJjV5DxDiAygAv2UY+Gt+Yw2aGBVxL03UiUZRY6PqoeHH7edCngOE+AAKwm8ZBuFFjRHJ/83dVpgkXTIpWnAQGtGZCBDJ/VRcyew+EOIDKBC3ZRiE57U+3ijqXpqwUeAWKWnuwQgTG7x+yidpgEh2piDEB1BAjVMQhNf4Zus1NUoaI17S3uxJbHwc51Xi9nPAK0CkcFdDldIPEOIDKKgq/qCNy+sHdCPbMZKUqdjgdVRsXn8R8doblvRaIJKZACE+AJRe4xus18Qo7D6aoDd9U3GSZI8G0VEtUQPEje0AIT5QGHMHL9Y3RLUNTauPsS+cbK//oP7lqaWlHy0jmaAlq6Sbeb2iwStKTG8EDXr9Ex3l4/V6dnsNe70OvTZOp3VxPeIDuTb7+Nux753xzHu38YMWrpo37roxfUpz2mebMO3Ipyz2Znm9bqOGhx+WXQAfBAj8pD0FsYVphx1F2ORtMjzSvKVAvq+cA4TU+D9W8/98z7x3WyF+aCAbYd6Y87yUR3ikryg/Q7yWWdzCo21oWuzw4GwXVFrjvo8wmILAS9hlmEZZTkPCxhCv9+SKGh2S2WUW04gPlJLXxlMCBH6iXD8lbACEjRST0xVe48mlFR02ojVpdKQ99ZCIDxRM4xkvzRrPevFDgMCP6Qu42V6y4bWdnKk//yymY2mGh0nEByqp9sOFH9RwE2YZJo94PScT9887LxuSvfZ2hBUUHtzbBfj//PZ9hLnmB1MQ+CnKZex5DScX9s85L6HRyMbeDtN3tiU+UCrNSy8ECJLKe4Dw2k0uzJ+vyeiwcYl+kxOPNBAfKD0CBEnldRmG12wytqYdNu8HFHXaESY8TE89JOIDBdS86bR56cVt42nYAJH4gQ5vvDbKI+1pRxY3IMz7tKMR8YHcS3KJ9Ua1HyRMQYDqSmvakeXdjtOYdtSkMfWQiA9UEMswQDWZnnbEDY4sL/KV9cSjhvhAITRPP8IsvUjeN/MKMwVhGQbwlrf9L3GVPTak+MGR1tRDIj5QIGECRFJqEYLJiLL4eE1lJ2xsFDU0apJMONKMjhriA4VS+5+iFiG1/8H8piBS+AiR8n0TsTzxegMlSvwRHh/L0zUzih4bNUmXVWyEh0R8oKCiLsPUBEWIFH5jKtyxX8ZdGaMjT/EQRZLNoXmLDcncPg5b4SERHygwtwCRPp6CuC3D1BAh6WK/zGRFDY8s4yLLs0ck85GRl42ejWzGRjPiA4XWvAwjhdsLUhMlQsoujchiCmI+PPL+esw6GqKqQmQ0yjI4GhEfKIWgZRgpXIRI/iFSZn5vaknCpKoBEjU68h4VbooUGiYjI++B0SgvsdGM+EBpBC3D1PhFiBRuGlI1ScOkasswfuFRxMioMREbedwz0axIcdEsr7HRjPhAqXgtw0hESFqinClUhSlIWcIjTmgQFvYVJTaaGY+P7373u9q1a9ekzy1dulSvv/666W8FeHK7JHvSCCkzU4EV9ropZQyQoGWWNMIj69emidgoUwwUNQSykMrk4zOf+Yx+9atfffxNZjBggX1uUxApfoSUWfObWNIYCbqEfdmWYdKedmQdGY2KdPOyOAgIO1KpghkzZqizszONLw1ERoREZyJGqnIPHZPhkafIaBQmOEyEBm/81ZFKfLzxxhvq7u7WrFmztGrVKg0MDGjRokWujx0fH9f4+Hj949HR0TQOCSBCEogbI2Vehkm6zJLX0JDSiw3iAjUtjuM4Jr/gL37xC124cEFLly7VmTNntGvXLr399ts6fvy42trapjzebY+IJK3r+mvNmDZzyucBE5oDpFFzhCRRlXAJGyNBk5AiREiS6KhCcBAY1fXHiYv61Zl/08jIiObMmeP7WOPx0ezcuXNavHixvv/97+v++++f8vtuk4+enh7iA1Z4RYjJACkiE9EUJkjKcPVYWxOOvN+GnehAlPhIfSfo1VdfrU9/+tM6ceKE6++3traqtbU17cMAXLmdFSO5X6SsStze6KIGSZilmqLe0C+t4MjbqapEB9KSenxcuHBBQ0NDuueee9L+VkAsfgEiMQWp8XpjDBslQTFSpGtgNIsaG3mLjGZEB9JmPD6+9a1vafPmzVq8eLFOnz6tnTt3avr06br77rtNfyvAGK/NqBJTkCBxpySmT++1LWxw5D00GhEdsMV4fLz11lu6++679e677+qaa67RF77wBR0+fFjXXHON6W8FGBc0BakCE6EVJ0jyvBkzijRjI8vXIdEBk4zHx3/913+Z/pKAVV4BUhVBb3Bx4yTpsk0emQqNPMct0YE0cOlRwIXfMkzVmY4TE5tbbYkbG3mOCy9EB9JEfAA+qj4FicNEnBRpn0SjIkZGM6IDNhAfQIC4P4yJFndpLevYUobAcEN0wCbiA0hJFX6YpxFYZX1zb1SF1wbgh/gAEJuJN9EiT4iICCAe4gNApngDB6qnmLu6AABAYREfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYlVp87N69W5/85Cc1a9YsrVy5Ur/97W/T+lYAAKBAUomPn/70p+rv79fOnTv1+9//XitWrNCGDRv0zjvvpPHtAABAgaQSH9///vf1wAMP6L777tOf/umf6qmnntKVV16p//iP/0jj2wEAgAKZYfoLXrx4UUeOHNGOHTvqn5s2bZrWrVunQ4cOTXn8+Pi4xsfH6x+PjIxIkv44cdH0oQEAgJTU3rcdxwl8rPH4+MMf/qCPPvpICxcunPT5hQsX6vXXX5/y+IGBAe3atWvK5/ef3WP60AAAQMrOnz+v9vZ238cYj4+oduzYof7+/vrH586d0+LFi3Xq1KnAg0dyo6Oj6unp0Ztvvqk5c+ZkfTilx/NtF8+3XTzfduXt+XYcR+fPn1d3d3fgY43Hx/z58zV9+nSdPXt20ufPnj2rzs7OKY9vbW1Va2vrlM+3t7fn4smsijlz5vB8W8TzbRfPt10833bl6fkOOzQwvuF05syZuvnmm7Vv37765yYmJrRv3z6tWrXK9LcDAAAFk8qyS39/v7Zt26bPfe5zuvXWW/Xkk09qbGxM9913XxrfDgAAFEgq8XHnnXfq//7v//TYY49peHhYn/3sZ/XSSy9N2YTqprW1VTt37nRdioF5PN928XzbxfNtF8+3XUV+vlucMOfEAAAAGMK9XQAAgFXEBwAAsIr4AAAAVhEfAADAqtzFx+7du/XJT35Ss2bN0sqVK/Xb3/4260Mqpe9+97tqaWmZ9OvGG2/M+rBK4+DBg9q8ebO6u7vV0tKi559/ftLvO46jxx57TF1dXZo9e7bWrVunN954I5uDLYGg5/vee++d8nrfuHFjNgdbcAMDA7rlllvU1tamBQsWaMuWLRocHJz0mA8//FB9fX2aN2+errrqKm3dunXKhScRTpjne82aNVNe39/4xjcyOuJwchUfP/3pT9Xf36+dO3fq97//vVasWKENGzbonXfeyfrQSukzn/mMzpw5U//1yiuvZH1IpTE2NqYVK1Zo9+7drr//xBNP6Ac/+IGeeuopvfbaa/rEJz6hDRs26MMPP7R8pOUQ9HxL0saNGye93n/yk59YPMLyOHDggPr6+nT48GG9/PLLunTpktavX6+xsbH6Yx5++GH9/Oc/17PPPqsDBw7o9OnTuuOOOzI86uIK83xL0gMPPDDp9f3EE09kdMQhOTly6623On19ffWPP/roI6e7u9sZGBjI8KjKaefOnc6KFSuyPoxKkOQ899xz9Y8nJiaczs5O55/+6Z/qnzt37pzT2trq/OQnP8ngCMul+fl2HMfZtm2b85WvfCWT4ym7d955x5HkHDhwwHGcy6/lK664wnn22Wfrj/nf//1fR5Jz6NChrA6zNJqfb8dxnD//8z93/vZv/za7g4ohN5OPixcv6siRI1q3bl39c9OmTdO6det06NChDI+svN544w11d3fr+uuv11/8xV/o1KlTWR9SJZw8eVLDw8OTXuvt7e1auXIlr/UU7d+/XwsWLNDSpUv1zW9+U++++27Wh1QKIyMjkqSOjg5J0pEjR3Tp0qVJr+8bb7xRixYt4vVtQPPzXfPjH/9Y8+fP17Jly7Rjxw69//77WRxeaJnf1bbmD3/4gz766KMpV0FduHChXn/99YyOqrxWrlypp59+WkuXLtWZM2e0a9cu/dmf/ZmOHz+utra2rA+v1IaHhyXJ9bVe+z2YtXHjRt1xxx3q7e3V0NCQ/v7v/16bNm3SoUOHNH369KwPr7AmJia0fft23X777Vq2bJmky6/vmTNn6uqrr570WF7fybk935L0ta99TYsXL1Z3d7eOHTumb3/72xocHNTPfvazDI/WX27iA3Zt2rSp/s/Lly/XypUrtXjxYv33f/+37r///gyPDDDvrrvuqv/zTTfdpOXLl2vJkiXav3+/1q5dm+GRFVtfX5+OHz/OfjFLvJ7vr3/96/V/vummm9TV1aW1a9dqaGhIS5YssX2YoeRm2WX+/PmaPn36lB3RZ8+eVWdnZ0ZHVR1XX321Pv3pT+vEiRNZH0rp1V7PvNazc/3112v+/Pm83hN48MEH9eKLL+o3v/mNrrvuuvrnOzs7dfHiRZ07d27S43l9J+P1fLtZuXKlJOX69Z2b+Jg5c6Zuvvlm7du3r/65iYkJ7du3T6tWrcrwyKrhwoULGhoaUldXV9aHUnq9vb3q7Oyc9FofHR3Va6+9xmvdkrfeekvvvvsur/cYHMfRgw8+qOeee06//vWv1dvbO+n3b775Zl1xxRWTXt+Dg4M6deoUr+8Ygp5vN0ePHpWkXL++c7Xs0t/fr23btulzn/ucbr31Vj355JMaGxvTfffdl/Whlc63vvUtbd68WYsXL9bp06e1c+dOTZ8+XXfffXfWh1YKFy5cmPS3jpMnT+ro0aPq6OjQokWLtH37dj3++OP61Kc+pd7eXj366KPq7u7Wli1bsjvoAvN7vjs6OrRr1y5t3bpVnZ2dGhoa0iOPPKIbbrhBGzZsyPCoi6mvr0979+7VCy+8oLa2tvo+jvb2ds2ePVvt7e26//771d/fr46ODs2ZM0cPPfSQVq1apc9//vMZH33xBD3fQ0ND2rt3r7785S9r3rx5OnbsmB5++GGtXr1ay5cvz/jofWR9uk2zf/mXf3EWLVrkzJw507n11ludw4cPZ31IpXTnnXc6XV1dzsyZM51rr73WufPOO50TJ05kfVil8Zvf/MaRNOXXtm3bHMe5fLrto48+6ixcuNBpbW111q5d6wwODmZ70AXm93y///77zvr1651rrrnGueKKK5zFixc7DzzwgDM8PJz1YReS2/MsydmzZ0/9MR988IHzN3/zN87cuXOdK6+80vnqV7/qnDlzJruDLrCg5/vUqVPO6tWrnY6ODqe1tdW54YYbnL/7u79zRkZGsj3wAC2O4zg2YwcAAFRbbvZ8AACAaiA+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABW/T87gjt3i8FAMgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1 \n",
+            "\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  38 254 109   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  87 252  82   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 135 241   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 244 150   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  84 254  63   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 202 223  11   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  32 254 216   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  95 254 195   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 140 254  77   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  57 237 205   8   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 124 255 165   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 171 254  81   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0  24 232 215   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0 120 254 159   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0 151 254 142   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0 228 254  66   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0  61 251 254  66   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0 141 254 205   3   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0  10 215 254 121   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   5 198 176  10   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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LlqmmpkYlJSXavXt32uOrVq1SSUlJ2m3JkiVurRdAxGQbNrXTcvF6i62VE0ytDJgOoOUC5GY7fPT09Gj27NnaunVr1ucsWbJEZ86cSd1ef/31ghYJoDh4eaJptmFTBkwB/42w+w1Lly7V0qVLcz4nkUioqqrK8aIAICiFBg+qHkB+nsx87Nu3TxMnTtT06dP12GOP6ezZs1mf29vbq+7u7rQbgHgza7lkq3p42XJhwBQIhuvhY8mSJXrllVe0d+9e/e53v1NLS4uWLl2qy5cvmz6/ublZyWQydautrXV7SQACUujhYrmCR6HDpl4ED6oegDW22y753Hfffak/33DDDZo1a5amTZumffv2aeHChUOev2HDBjU1NaW+7u7uJoAAMWa16pEteFgJHa9+d0vaQWN2LybnBMEDsM7zrbZTp07V+PHjdeLECdPHE4mEysvL024Aos+s6uFH8LDC7aoHwQOwx/Pw8eWXX+rs2bOqrq72+qUAxIBbwcOPaodE8ACcsN12uXDhQloVo729XUeOHFFFRYUqKiq0adMmrVixQlVVVWpra9OTTz6pa665RosXL3Z14QDCy2nVw+2Kh1kAsVv1yIXgAThjO3x89tlnuuOOO1JfD8xrrFy5Utu2bdPRo0f117/+VefOnVNNTY0WLVqk3/72t0okEu6tGkDkWT3Tw81TTJ0wa7kQOoDC2A4fCxYskGEYWR//xz/+UdCCAESb1apHJrOqR9DBwwzBAygc13YB4Cmn7RYvgkehg6YED8AdhA8ArrFyroeVdgsVDyDeCB8AXOFWu8Wv4MFppkBwCB8ACmb1JNN87RYvg4eVK9dmQ9UDcJfrJ5wCKB65Qkdm1cPqttogUPUA/EXlA4AjdoKHFX5WPTJbLrlQ9QDcR/gAYJvdC8YF2W6xi6oH4D3CBwBX5Wu3BK2QE00BuIPfOgC22K16ZPJ71qOQQVMA3iB8AHCNk6qHWcvlobGfuLYmAOFD+ABgmZ9VDzcCiN2qByeaAv4gfABwhVuzHl5WPZj3AMKB3zwAlhRa9TAT1kPFAHiL8AEgr3zBw0rVI0yHigEIFuEDQEGcHChmldMWDFUPINwIHwBysnuSadBVj2zBw86ppgC8RfgAEIigTzVl2BQIDr99ALJyo+phR6E7XQppt3CsOuAfwgcA2+zMeRTacuHAMSB+CB8ATGWremQLHnaqHl60XHJVPZzMe3DAGOAdwgcAz/g1aGq33cK8BxAsfgMBxJqVqgfzHoC/CB8AChb09lo7qHoAweO3EIBlbhwo5va8R6GzHmZVD+Y9AG8RPgAUpNDttV4xCx6ZVQ/aLUAwCB8AXBfGQVPaLUB48NsIYAgvrmAr5W65ZDvP49XvbrH9OplVD7Pgka3qQcsF8B7hA4AlYbyOi1O0W4BgET4ARJLVC8jRbgHCh99KAK7JVfXw40JyhbRbJFougF8IHwAcCXKXi9MLyNFuAcKB8AEgLyvnewQ960G7BYgOfjsBeM7rlkuh7RaJlgvgJ8IHANui1nKh3QKEC+EDQBonZ3x4NWhq5YwP2i1A9PBbCiCnQq7nYid4WAkaXlQ9Rh/7ipYL4DPCBwBbMlsu2aoeYdhay5wHEE6EDwCucxo8nByl7hTBAwgO4QOAqwqteAwEkMwgktlycVr1oM0CBI/wASAlc9g0c94jX8vFrVaLVxUQQgcQDoQPALFjVvUgeADhMSLoBQCIBy8HTO22XAYjdADhQ+UDgCVWd7kEjQPFgPAjfAAwZed8Dz+21Q6g6gFEH+EDgCR7J5v6WfWwc7AYVQ8gGggfAPIK8louuVD1AKKJ8AFgiKi0XAaj6gFEB+EDgC1hablQ9QCii/ABIG3eI9/BYoP5WfXIhaoHEC2ED6DI5Ro0DdP22lwtFwDRQvgAili+49SD5LTlAiD8+A0GilS+rbV+XccFQPEhfABFyCx4uFH1eGjsJwX/DCn/ceoAoo3wARQZK8HDyayHW8GjUOx0AcKP8AEUESunmFo5UCxMF5FjpwsQPYQPoEhkCx752i12d7gUUgGxc5Q6gOgifABFwGrwCPIYdYIHUDxsh4/9+/dr2bJlqqmpUUlJiXbv3p32uGEYevbZZ1VdXa3Ro0eroaFBx48fd2u9AGwqJHiYVT28aLlkCx4MmgLxZDt89PT0aPbs2dq6davp45s3b9ZLL72kl19+WYcOHdKVV16pxYsX6+LFiwUvFoA9TlstdmW2WrwaPuV8DyAeRtj9hqVLl2rp0qWmjxmGoS1btujpp5/WXXfdJUl65ZVXVFlZqd27d+u+++4rbLUALLMTPKxWPbxAuwUoPq7+b0R7e7s6OjrU0NCQui+ZTKq+vl4HDhww/Z7e3l51d3en3QAUxquKh9stl1zBw0rLhZ0uQDS5Gj46OjokSZWVlWn3V1ZWph7L1NzcrGQymbrV1ta6uSSg6NgNHkENmVLxAIpX4A3UDRs2qKurK3U7depU0EsCil6QF5DLhnkPID5c/W2uqqqSJHV2dqbd39nZmXosUyKRUHl5edoNQHFjlwsQb66Gj7q6OlVVVWnv3r2p+7q7u3Xo0CHNmzfPzZcC4IIgz/UAULxs73a5cOGCTpw4kfq6vb1dR44cUUVFhSZPnqx169bphRde0LXXXqu6ujo988wzqqmp0fLly91cNwAb3N5aCwCFsB0+PvvsM91xxx2pr5uamiRJK1eu1I4dO/Tkk0+qp6dHjzzyiM6dO6fbbrtNe/bs0ahRo9xbNQDPZJv38PJ6LgCKi+3wsWDBAhmGkfXxkpISPf/883r++ecLWhgA+6xcOM5tTg4UY6cLUNwYHweKVFjnPQoZNh197CsXVwLAK4QPAClh3GILIH4IH0DMxWHY1OyMD043BaKL8AEUobC2XAAUB8IHgLzY6QLATYQPAJLCMe/ByaZAcSB8AAAAXxE+gBgzGzY1m/fIVfVwu+XCGR8ACB9AkSskeDg5YMwudroA8UP4AGLC7dNN/R4yNZv3MAse2XDAGBAdhA+giGS2XAq5josfVQ8zVD2A6CN8AEjjZcUj27yH1apHtuBB1QOIFsIHEFOZw6ZWqh5Wg4fXVQ/mPIB4I3wAkBRc8OBsD6D4ED6AIpRZ9XC71fLqd7c4/l67VQ9aLkD0ED6AIpDrWi52gkchVQ+zeQ8rVQ/aLUD8ED6AGIrqlWztbK2VqHoAUUX4AIrM4JaLX1UPM1Q9gOJF+ABiINcBY7laLmGSWfXIFzyoegDRRfgAikhQVQ+u5wJgMMIHEDNRmPfIbLnYrXoAiLYRQS8AgHcKbbkEdYR6PrRcgGij8gFEnNULyuW6eq2ZsAYPANFH+ABixGrLJd+8B8EDgJcIH0BMOW25EDwAeI3wAUSY1ZaLVQQPAH5g4BSIiVwtl3zzHoQOAH6i8gEUOYIHAL8RPoCIcnqqqdtXsAUAuwgfQAxE4WAxABjAzAcQMXaHTO2e7wEAXqPyAUSIleARlQvJAShehA8gIrIFj0JaLgybAggCbRcg5Nw8y4NhUwBhQOUDCDG7wSOz5cK8B4AwInwAERaHXS5lbel/DY1t7cv5fK5oC0Qf4QMIqXxVj8zgEZaqx6vf3ZL29T9PTk/7+kJ70vHPJngA8UD4AIpE1Oc9CB5AfBA+gBCKatUjn8yqh9WWC8EDiBd2uwARE+Y5j3wtF7sIHUA8ET6AkMlV9TALHmGoemSGDicyqx4EDyC+aLsAEWb1NFMv5z2sBo98LRcAxYPffiBE7FY9CuHG6aa5gkchLReqHkC80XYBIsBKu0Xyr+VSaJvF7tkeAOKFygcQEl5cu0Vyv+XiJHjYOduDqgcQf4QPIIKCqnpYDR52Wi5UPYDiQ9sFCAE7VQ+rQ6ZuKqTNwqApgEz8LQCElJ3g4WXVw27wKKTqQcsFKA6EDyDicgWPQuc93Di/YzCqHgAkwgcQOLOWixu7W3IFDyvbbN0IHgyaAjBD+ABiKKiLyOVqubC9FsAAwgcQUdmqHmEJHnaqHgCKC+EDiACr128JS/DIxKwHgMH4GwEIGaeHilkNHm7Pe5gFj3xVD1ouQHEjfAAhF4ar1mZjJXhYqXowbAoUF8IHEKBcF5KzI4h2i9MLx1H1AED4AEIkX8vFrOphJ3i4cSXbXDjNFIAV/M0AhFi+o9S9qHhYmfdwq90CoDi5/rfDc889p5KSkrTbjBkz3H4ZoOhkVj3CtLPF6rZaWi4AJI8uLHf99dfrgw8++N+LjOD6dUA+mS0Xty8g50bLxeqch52qB8OmQPHxJBWMGDFCVVVVXvxoIDbsDJuGoeqRLXhwmBgAuzxpyh4/flw1NTWaOnWqHnzwQZ08eTLrc3t7e9Xd3Z12A4qd21UPq7LNe9gJHtmqHrRcAAxwPXzU19drx44d2rNnj7Zt26b29nbdfvvtOn/+vOnzm5ublUwmU7fa2lq3lwTAA1Q8ADhVYhiG4eULnDt3TlOmTNGLL76o1atXD3m8t7dXvb29qa+7u7tVW1urhupfaMQwZyc9AlGQ2XbJNfPhdtvFbP7DTtUjW/Awq3pkq3gw6wHEy3/7+/TBmT+rq6tL5eXlOZ/r+STomDFjdN111+nEiROmjycSCSUSCa+XAWCQwUHjobGfEDwA+MrzjfgXLlxQW1ubqqurvX4pAA7YnfMwQ/AAYIfr4eOJJ55QS0uL/v3vf+uTTz7R3XffreHDh+v+++93+6UAeKTQAVOCB4BcXG+7fPnll7r//vt19uxZTZgwQbfddpsOHjyoCRMmuP1SQNG40J4M/IJyVoIHoQOAFa6HjzfeeMPtHwnE0uhjX7l2YTk3WZ3zsHqQGMEDQCYuvgAgpZDgYVb1IHgAMEP4ACLI6eXs3fiZBA8AhSJ8ALA8YErwAOAGrvgGFBmrFQ4rJ5hmBg9CBwArCB9ASIxt7RtyyqlThbZlCtnZAgD5ED6AiPJi7kNyHjyoegCwipkPACkEDwB+IHwAAcr1j3bmP/peX0WW4AHAL7RdgIANPmxs4B/3gdmPgX/8B65w6+ZJp7nCjJVdLYQOAE4RPoAQyDztNHP4tKxtWFoAkZQWQtysilDtAOA1wgcQEgP/oOeqggwEEMn9NgzVDgB+IXwAIZOrCpLZhilUtuuzUO0A4CXCBxBCZlWQzDaMV6h2APAau12AEBv8D//Y1j7PD/YieADwA+EDCLnMAOB2ABkINQQPAH6h7QJEQLZhVC9fCwC8QuUDiBCvgwHBA4AfCB9AxHgREEYf+4rgAcA3tF2ACCIoAIgyKh8AAMBXhA8AAOArwgcAAPAV4QMAAPiK8AEAAHxF+AAAAL4ifAAAAF8RPgAAgK8IHwAAwFeEDwAA4CvCBwAA8BXhAwAA+IrwAQAAfEX4AAAAviJ8AAAAXxE+AACArwgfAADAV4QPAADgK8IHAADwFeEDAAD4ivABAAB8RfgAAAC+InwAAABfET4AAICvCB8AAMBXhA8AAOArwgcAAPAV4QMAAPiK8AEAAHxF+AAAAL4ifAAAAF8RPgAAgK8IHwAAwFeEDwAA4CvCBwAA8BXhAwAA+IrwAQAAfEX4AAAAvvIsfGzdulVXX321Ro0apfr6en366adevRQAAIgQT8LHm2++qaamJm3cuFGff/65Zs+ercWLF+vrr7/24uUAAECEeBI+XnzxRa1Zs0YPP/ywfvKTn+jll1/WFVdcob/85S9evBwAAIiQEW7/wL6+Ph0+fFgbNmxI3Tds2DA1NDTowIEDQ57f29ur3t7e1NddXV2SpP/297m9NAAA4JGBf7cNw8j7XNfDx7fffqvLly+rsrIy7f7Kykp98cUXQ57f3NysTZs2Dbl/X+d2t5cGAAA8dv78eSWTyZzPcT182LVhwwY1NTWlvj537pymTJmikydP5l08Ctfd3a3a2lqdOnVK5eXlQS8n9ni//cX77S/eb3+F7f02DEPnz59XTU1N3ue6Hj7Gjx+v4cOHq7OzM+3+zs5OVVVVDXl+IpFQIpEYcn8ymQzFm1ksysvLeb99xPvtL95vf/F++ytM77fVooHrA6elpaWaM2eO9u7dm7qvv79fe/fu1bx589x+OQAAEDGetF2ampq0cuVK3XjjjZo7d662bNminp4ePfzww168HAAAiBBPwse9996rb775Rs8++6w6Ojr005/+VHv27BkyhGomkUho48aNpq0YuI/321+83/7i/fYX77e/ovx+lxhW9sQAAAC4hGu7AAAAXxE+AACArwgfAADAV4QPAADgq9CFj61bt+rqq6/WqFGjVF9fr08//TToJcXSc889p5KSkrTbjBkzgl5WbOzfv1/Lli1TTU2NSkpKtHv37rTHDcPQs88+q+rqao0ePVoNDQ06fvx4MIuNgXzv96pVq4Z83pcsWRLMYiOuublZN910k8rKyjRx4kQtX75cra2tac+5ePGiGhsbNW7cOF111VVasWLFkIMnYY2V93vBggVDPt+PPvpoQCu2JlTh480331RTU5M2btyozz//XLNnz9bixYv19ddfB720WLr++ut15syZ1O3jjz8Oekmx0dPTo9mzZ2vr1q2mj2/evFkvvfSSXn75ZR06dEhXXnmlFi9erIsXL/q80njI935L0pIlS9I+76+//rqPK4yPlpYWNTY26uDBg3r//fd16dIlLVq0SD09PannrF+/Xu+++67eeusttbS06PTp07rnnnsCXHV0WXm/JWnNmjVpn+/NmzcHtGKLjBCZO3eu0djYmPr68uXLRk1NjdHc3BzgquJp48aNxuzZs4NeRlGQZOzatSv1dX9/v1FVVWX8/ve/T9137tw5I5FIGK+//noAK4yXzPfbMAxj5cqVxl133RXIeuLu66+/NiQZLS0thmH88FkeOXKk8dZbb6We869//cuQZBw4cCCoZcZG5vttGIbxs5/9zPjVr34V3KIcCE3lo6+vT4cPH1ZDQ0PqvmHDhqmhoUEHDhwIcGXxdfz4cdXU1Gjq1Kl68MEHdfLkyaCXVBTa29vV0dGR9llPJpOqr6/ns+6hffv2aeLEiZo+fboee+wxnT17NuglxUJXV5ckqaKiQpJ0+PBhXbp0Ke3zPWPGDE2ePJnPtwsy3+8Br732msaPH6+ZM2dqw4YN+v7774NYnmWBX9V2wLfffqvLly8POQW1srJSX3zxRUCriq/6+nrt2LFD06dP15kzZ7Rp0ybdfvvtOnbsmMrKyoJeXqx1dHRIkulnfeAxuGvJkiW65557VFdXp7a2Nv3mN7/R0qVLdeDAAQ0fPjzo5UVWf3+/1q1bp1tvvVUzZ86U9MPnu7S0VGPGjEl7Lp/vwpm935L0wAMPaMqUKaqpqdHRo0f11FNPqbW1VW+//XaAq80tNOED/lq6dGnqz7NmzVJ9fb2mTJmiv/3tb1q9enWAKwPcd99996X+fMMNN2jWrFmaNm2a9u3bp4ULFwa4smhrbGzUsWPHmBfzSbb3+5FHHkn9+YYbblB1dbUWLlyotrY2TZs2ze9lWhKatsv48eM1fPjwIRPRnZ2dqqqqCmhVxWPMmDG67rrrdOLEiaCXEnsDn2c+68GZOnWqxo8fz+e9AGvXrtV7772njz76SJMmTUrdX1VVpb6+Pp07dy7t+Xy+C5Pt/TZTX18vSaH+fIcmfJSWlmrOnDnau3dv6r7+/n7t3btX8+bNC3BlxeHChQtqa2tTdXV10EuJvbq6OlVVVaV91ru7u3Xo0CE+6z758ssvdfbsWT7vDhiGobVr12rXrl368MMPVVdXl/b4nDlzNHLkyLTPd2trq06ePMnn24F877eZI0eOSFKoP9+hars0NTVp5cqVuvHGGzV37lxt2bJFPT09evjhh4NeWuw88cQTWrZsmaZMmaLTp09r48aNGj58uO6///6glxYLFy5cSPu/jvb2dh05ckQVFRWaPHmy1q1bpxdeeEHXXnut6urq9Mwzz6impkbLly8PbtERluv9rqio0KZNm7RixQpVVVWpra1NTz75pK655hotXrw4wFVHU2Njo3bu3Kl33nlHZWVlqTmOZDKp0aNHK5lMavXq1WpqalJFRYXKy8v1+OOPa968ebr55psDXn305Hu/29ratHPnTt15550aN26cjh49qvXr12v+/PmaNWtWwKvPIejtNpn++Mc/GpMnTzZKS0uNuXPnGgcPHgx6SbF07733GtXV1UZpaanxox/9yLj33nuNEydOBL2s2Pjoo48MSUNuK1euNAzjh+22zzzzjFFZWWkkEglj4cKFRmtra7CLjrBc7/f3339vLFq0yJgwYYIxcuRIY8qUKcaaNWuMjo6OoJcdSWbvsyRj+/btqef85z//MX75y18aY8eONa644grj7rvvNs6cORPcoiMs3/t98uRJY/78+UZFRYWRSCSMa665xvj1r39tdHV1BbvwPEoMwzD8DDsAAKC4hWbmAwAAFAfCBwAA8BXhAwAA+IrwAQAAfEX4AAAAviJ8AAAAXxE+AACArwgfAADAV4QPAADgK8IHAADwFeEDAAD4ivABAAB89X+KvJTiYw8uqwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0 \n",
+            "\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
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+            " [  0   0   0   0   0   0   0   0   0   0   0   0  11 150 253 202  31   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0  37 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0  21 197 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 110 190 251 251 251 253 169 109  62   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 253 251 251 251 251 253 251 251 220  51   0   0   0   0   0   0   0   0]\n",
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+            " [  0   0   0   0   0   0   0   0  63 221 253 251 251 251 147  77  62 128 251 251 105   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  32 231 251 253 251 220 137  10   0   0  31 230 251 243 113   5   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  37 251 251 253 188  20   0   0   0   0   0 109 251 253 251  35   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  37 251 251 201  30   0   0   0   0   0   0  31 200 253 251  35   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  37 253 253   0   0   0   0   0   0   0   0  32 202 255 253 164   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 140 251 251   0   0   0   0   0   0   0   0 109 251 253 251  35   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0   0  21  63 231 251 253 230  30   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0   0 144 251 251 251 221  61   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0 182 221 251 251 251 180   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 218 253 253  73  73 228 253 253 255 253 253 253 253   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 113 251 251 253 251 251 251 251 253 251 251 251 147   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  31 230 251 253 251 251 251 251 253 230 189  35  10   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  62 142 253 251 251 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0  72 174 251 173  71  72  30   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "4 \n",
+            "\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0  50 224   0   0   0   0   0   0   0  70  29   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0 121 231   0   0   0   0   0   0   0 148 168   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   4 195 231   0   0   0   0   0   0   0  96 210  11   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  69 252 134   0   0   0   0   0   0   0 114 252  21   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  45 236 217  12   0   0   0   0   0   0   0 192 252  21   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0 168 247  53   0   0   0   0   0   0   0  18 255 253  21   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  84 242 211   0   0   0   0   0   0   0   0 141 253 189   5   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 169 252 106   0   0   0   0   0   0   0  32 232 250  66   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0  15 225 252   0   0   0   0   0   0   0   0 134 252 211   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0  22 252 164   0   0   0   0   0   0   0   0 169 252 167   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   9 204 209  18   0   0   0   0   0   0  22 253 253 107   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0 169 252 199  85  85  85  85 129 164 195 252 252 106   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0  41 170 245 252 252 252 252 232 231 251 252 252   9   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  49  84  84  84  84   0   0 161 252 252   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 127 252 252  45   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 128 253 253   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 127 252 252   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 135 252 244   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 232 236 111   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 179  66   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SexfCHUgYHOM"
+      },
+      "source": [
+        "2. What is the shape of `X_train` and `X_test`? What is the shape of `X_train[i]` and `X_test[i]` for each index `i`? What is the shape of `y_train` and `y_test`?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mv2nyfzUYHOM",
+        "outputId": "b81d955c-2318-4ed1-f37e-a1f391a0e924"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(60000, 28, 28)\n",
+            "(10000, 28, 28)\n",
+            "(28, 28)\n",
+            "(28, 28)\n",
+            "(60000,)\n",
+            "(10000,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X_train.shape)\n",
+        "print(X_test.shape)\n",
+        "print(X_train[0].shape)\n",
+        "print(X_test[0].shape)\n",
+        "print(y_train.shape)\n",
+        "print(y_test.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K8fhwQ6cYHON"
+      },
+      "source": [
+        "> The shape of `X_train` is 60,000 samples that are 28 x 28 matrices. The shape of `X_test` is 10,000 samples that are also 28 x 28 matrices. The shape of `X_train[i]` and `X_test` is a 28 x 28 matrix. The shape of `y_train` is 60,000 numerical assignments, one for each value in `X_train`. The shape of `y_test` is 10,000 numerical assignments for each element in `X_test`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YW88xpkwYHON"
+      },
+      "source": [
+        "3. Use Numpy's `.reshape()` method to covert the training and testing data from a matrix into an vector of features. So, `X_test[index].reshape((1,784))` will convert the $index$-th element of `X_test` into a $28\\times 28=784$-length row vector of values, rather than a matrix. Turn `X_train` into an $N \\times 784$ matrix $X$ that is suitable for scikit-learn's kNN classifier where $N$ is the number of observations and $784=28*28$ (you could use, for example, a `for` loop)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#import pandas as pd\n",
+        "#import numpy as np\n",
+        "#reload = 0\n",
+        "\n",
+        "#if reload == 1:\n",
+        "#    Z_train = []\n",
+        "#    for i in range(60000):\n",
+        "#        row = X_train[i].reshape((1,784))\n",
+        "#        Z_train.append(row[0])\n",
+        "#    Z_train = pd.DataFrame(Z_train)\n",
+        "#    Z_train.to_parquet('Z_train.parquet')\n",
+        "\n",
+        "#    Z_test = []\n",
+        "#    for i in range(len(y_test)):\n",
+        "#        row = X_test[i].reshape((1,784))\n",
+        "#        Z_test.append(row[0])\n",
+        "#    Z_test = pd.DataFrame(Z_test)\n",
+        "#    Z_test.to_parquet('Z_test.parquet')\n",
+        "#else:\n",
+        "#    Z_train = pd.read_parquet('Z_train.parquet')\n",
+        "#    Z_test = pd.read_parquet('Z_test.parquet')\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "Z_train = []\n",
+        "for i in range(60000):\n",
+        "    row = X_train[i].reshape((1,784))\n",
+        "    Z_train.append(row[0])\n",
+        "\n",
+        "Z_train = pd.DataFrame(Z_train)\n",
+        "\n",
+        "Z_test = []\n",
+        "for i in range(len(y_test)):\n",
+        "    row = X_test[i].reshape((1,784))\n",
+        "    Z_test.append(row[0])\n",
+        "\n",
+        "Z_test = pd.DataFrame(Z_test)"
+      ],
+      "metadata": {
+        "id": "J1XolvA-ZXwt"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JOVXTUcEYHON"
+      },
+      "source": [
+        "4. Use the reshaped `X_test` and `y_test` data to create a $k$-nearest neighbor classifier of digit. What is the optimal number of neighbors $k$? If you can't determine this, play around with different values of $k$ for your classifier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "53uPUNt6YHON",
+        "outputId": "901f665b-46e6-4582-b546-11686469b88b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[4]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "\n",
+        "k_bar = 50\n",
+        "k_grid = np.arange(2,k_bar)\n",
+        "accuracy = np.zeros(k_bar)\n",
+        "\n",
+        "for k in range(k_bar):\n",
+        "    knn = KNeighborsClassifier(n_neighbors=k+1)\n",
+        "    predictor = knn.fit(Z_train.values,y_train)\n",
+        "    accuracy[k] = knn.score(Z_test.values,y_test)\n",
+        "\n",
+        "accuracy_max = np.max(accuracy)\n",
+        "max_index = np.where(accuracy==accuracy_max)\n",
+        "k_star = k_grid[max_index]\n",
+        "print(k_star)\n",
+        "\n",
+        "plt.plot(np.arange(0,k_bar),accuracy)\n",
+        "plt.xlabel(\"k\")\n",
+        "plt.title(\"optimal k:\"+str(k_star))\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X45rv7V5YHON"
+      },
+      "source": [
+        "5. For the optimal number of neighbors, how well does your predictor perform on the test set?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 411
+        },
+        "id": "COEWNQDxYHON",
+        "outputId": "f33574be-d814-4d87-9591-72cd67b183fc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy:  0.9691\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "col_0    0     1    2    3    4    5    6    7    8    9\n",
+              "row_0                                                   \n",
+              "0      973     1    1    0    0    1    3    1    0    0\n",
+              "1        0  1129    3    0    1    1    1    0    0    0\n",
+              "2        7     6  992    5    1    0    2   16    3    0\n",
+              "3        0     1    2  970    1   19    0    7    7    3\n",
+              "4        0     7    0    0  944    0    3    5    1   22\n",
+              "5        1     1    0   12    2  860    5    1    6    4\n",
+              "6        4     2    0    0    3    5  944    0    0    0\n",
+              "7        0    14    6    2    4    0    0  992    0   10\n",
+              "8        6     1    3   14    5   13    3    4  920    5\n",
+              "9        2     5    1    6   10    5    1   11    1  967"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8cb27863-0906-452d-979b-eea86074359a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>col_0</th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>row_0</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>973</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1129</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>992</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>16</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>970</td>\n",
+              "      <td>1</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>944</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>22</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2</td>\n",
+              "      <td>860</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>5</td>\n",
+              "      <td>944</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0</td>\n",
+              "      <td>14</td>\n",
+              "      <td>6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>992</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>14</td>\n",
+              "      <td>5</td>\n",
+              "      <td>13</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>920</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>10</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>967</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cb27863-0906-452d-979b-eea86074359a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8cb27863-0906-452d-979b-eea86074359a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8cb27863-0906-452d-979b-eea86074359a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f3c70f67-e90c-4dde-8826-f1c58cb7a352\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3c70f67-e90c-4dde-8826-f1c58cb7a352')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f3c70f67-e90c-4dde-8826-f1c58cb7a352 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"row_0\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 0,\n        \"max\": 973,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          973,\n          0,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 355,\n        \"min\": 1,\n        \"max\": 1129,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          1129,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313,\n        \"min\": 0,\n        \"max\": 992,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          3,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 305,\n        \"min\": 0,\n        \"max\": 970,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          5,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 297,\n        \"min\": 0,\n        \"max\": 944,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 270,\n        \"min\": 0,\n        \"max\": 860,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          0,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 297,\n        \"min\": 0,\n        \"max\": 944,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          1,\n          944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 312,\n        \"min\": 0,\n        \"max\": 992,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0,\n          992,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 290,\n        \"min\": 0,\n        \"max\": 920,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          3,\n          920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 304,\n        \"min\": 0,\n        \"max\": 967,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "knn = KNeighborsClassifier(n_neighbors=1)\n",
+        "predictor = knn.fit(Z_train.values,y_train)\n",
+        "y_hat = predictor.predict(Z_test.values)\n",
+        "accuracy = knn.score(Z_test.values,y_test)\n",
+        "print('Accuracy: ', accuracy)\n",
+        "\n",
+        "pd.crosstab(y_test, y_hat)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nef3oZ-nYHON"
+      },
+      "source": [
+        "> With k=5, the rule is 97% accurate on the test set."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "12po0IXuYHON"
+      },
+      "source": [
+        "6. For your confusion matrix, which mistakes are most likely? Do you find any interesting patterns?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "46AaC_EmYHOO"
+      },
+      "source": [
+        "> The biggest mistakes are mistaking a 4 for 9 (22), a 2 for a 7 (16), a 7 for a 1 (14), an 8 for a 3 (14), and a 8 for a 5 (45), a 8 for a 5 (13). The pattern makes sense because I could see how 2's and 7's look similar, and I understand 7's and 1's because of the top part."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uqpMDfCNYHOO"
+      },
+      "source": [
+        "7. So, this is how computers \"see.\" They convert an image into a matrix of values, that matrix becomes a vector in a dataset, and then we deploy ML tools on it as if it was any other kind of tabular data. To make sure you follow this, invent a way to represent a color photo in matrix form, and then describe how you could convert it into tabular data. (Hint: RGB color codes provide a method of encoding a numeric value that represents a color.)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qxpYSm01YHOO"
+      },
+      "source": [
+        "> To add color, we can use three 28x28 matrices for Red, Green, and Blue intensities. By reshaping these matrices and placing them side by side, we could create a single row of tabular data.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/04_hedonic_pricing/04_hedonic_pricing (1).ipynb b/04_hedonic_pricing/04_hedonic_pricing (1).ipynb
new file mode 100644
index 00000000..9cff916d
--- /dev/null
+++ b/04_hedonic_pricing/04_hedonic_pricing (1).ipynb	
@@ -0,0 +1,1449 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> I chose the data set, cars_hw.csv"
+      ],
+      "metadata": {
+        "id": "I_KrTQGYcqW_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 1. Clean the data and perform some EDA and visualization to get to know the data set."
+      ],
+      "metadata": {
+        "id": "0LQVx77Sa7Q1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "TaJr4vzUdZkb",
+        "outputId": "af3b1fa2-2b4a-4008-dfb4-3012cc12116d"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c8d5c73e-087b-48dc-ba06-7961ca1c712d\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c8d5c73e-087b-48dc-ba06-7961ca1c712d\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving cars_hw.csv to cars_hw.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "import io"
+      ],
+      "metadata": {
+        "id": "NdlkwbFym7h_"
+      },
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(io.BytesIO(uploaded['cars_hw.csv']), low_memory=False)\n",
+        "print(df.shape)\n",
+        "df.head()\n",
+        "\n",
+        "df.columns = df.columns.str.strip().str.lower()\n",
+        "df['price'] = pd.to_numeric(df['price'], errors='coerce')\n",
+        "df['mileage_run'] = pd.to_numeric(df['mileage_run'], errors='coerce')\n",
+        "missing_values = df.isnull().sum()\n",
+        "print(\"Missing values per column:\\n\", missing_values)\n",
+        "df.dropna(inplace=True)\n",
+        "categorical_columns = ['make', 'color', 'body_type', 'fuel_type', 'transmission', 'transmission_type']\n",
+        "for col in categorical_columns:\n",
+        "    df[col] = df[col].astype('category')\n",
+        "\n",
+        "print(df.info())\n",
+        "print(df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jfpxOZ8dnOHv",
+        "outputId": "3c7408b3-df88-4860-836a-65c980aec95b"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(976, 12)\n",
+            "Missing values per column:\n",
+            " unnamed: 0           0\n",
+            "make                 0\n",
+            "make_year            0\n",
+            "color                0\n",
+            "body_type            0\n",
+            "mileage_run          0\n",
+            "no_of_owners         0\n",
+            "seating_capacity     0\n",
+            "fuel_type            0\n",
+            "transmission         0\n",
+            "transmission_type    0\n",
+            "price                0\n",
+            "dtype: int64\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 976 entries, 0 to 975\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column             Non-Null Count  Dtype   \n",
+            "---  ------             --------------  -----   \n",
+            " 0   unnamed: 0         976 non-null    int64   \n",
+            " 1   make               976 non-null    category\n",
+            " 2   make_year          976 non-null    int64   \n",
+            " 3   color              976 non-null    category\n",
+            " 4   body_type          976 non-null    category\n",
+            " 5   mileage_run        976 non-null    int64   \n",
+            " 6   no_of_owners       976 non-null    object  \n",
+            " 7   seating_capacity   976 non-null    int64   \n",
+            " 8   fuel_type          976 non-null    category\n",
+            " 9   transmission       976 non-null    category\n",
+            " 10  transmission_type  976 non-null    category\n",
+            " 11  price              976 non-null    int64   \n",
+            "dtypes: category(6), int64(5), object(1)\n",
+            "memory usage: 53.6+ KB\n",
+            "None\n",
+            "   unnamed: 0        make  make_year   color  body_type  mileage_run  \\\n",
+            "0           1  Volkswagen       2017  silver      sedan        44611   \n",
+            "1           2     Hyundai       2016     red  crossover        20305   \n",
+            "2           3       Honda       2019   white        suv        29540   \n",
+            "3           4     Renault       2017  bronze  hatchback        35680   \n",
+            "4           5     Hyundai       2017  orange  hatchback        25126   \n",
+            "\n",
+            "  no_of_owners  seating_capacity fuel_type transmission transmission_type  \\\n",
+            "0          1st                 5    diesel      7-Speed         Automatic   \n",
+            "1          1st                 5    petrol      5-Speed            Manual   \n",
+            "2          2nd                 5    petrol      5-Speed            Manual   \n",
+            "3          1st                 5    petrol      5-Speed            Manual   \n",
+            "4          1st                 5    petrol      5-Speed            Manual   \n",
+            "\n",
+            "    price  \n",
+            "0  657000  \n",
+            "1  682000  \n",
+            "2  793000  \n",
+            "3  414000  \n",
+            "4  515000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(df['price'], bins=30, kde=True, color='skyblue')\n",
+        "plt.title('Distribution of Car Prices')\n",
+        "plt.xlabel('Price')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "plt.show()\n",
+        "\n",
+        "sns.scatterplot(x='mileage_run', y='price', data=df, hue='make', palette='viridis', alpha=0.7)\n",
+        "plt.title('Mileage Run vs Price')\n",
+        "plt.xlabel('Mileage Run')\n",
+        "plt.ylabel('Price')\n",
+        "plt.legend(title='Make', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "sns.boxplot(x='make_year', y='price', data=df, palette='coolwarm')\n",
+        "plt.title('Car Make Year vs Price')\n",
+        "plt.xlabel('Make Year')\n",
+        "plt.ylabel('Price')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "6vS2htlw-NOL",
+        "outputId": "a07aba81-7950-45a7-957b-305b4eeb649d"
+      },
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-75-760c968fc4a7>:16: FutureWarning: \n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "  sns.boxplot(x='make_year', y='price', data=df, palette='coolwarm')\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 2. Transform your variables --- particularly categorical ones --- for use in your regression analysis."
+      ],
+      "metadata": {
+        "id": "y0pLml94cFkD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option('future.no_silent_downcasting', True)\n",
+        "df['No_of_Owners'] = df['No_of_Owners'].replace({'1st': 1, '2nd': 2, '3rd': 3})\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "5o9oIlU7nU3k",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "465685c9-dfe1-4709-f997-304db32cdac8"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0        Make  Make_Year   Color  Body_Type  Mileage_Run  \\\n",
+              "0           1  Volkswagen       2017  silver      sedan        44611   \n",
+              "1           2     Hyundai       2016     red  crossover        20305   \n",
+              "2           3       Honda       2019   white        suv        29540   \n",
+              "3           4     Renault       2017  bronze  hatchback        35680   \n",
+              "4           5     Hyundai       2017  orange  hatchback        25126   \n",
+              "\n",
+              "  No_of_Owners  Seating_Capacity Fuel_Type Transmission Transmission_Type  \\\n",
+              "0            1                 5    diesel      7-Speed         Automatic   \n",
+              "1            1                 5    petrol      5-Speed            Manual   \n",
+              "2            2                 5    petrol      5-Speed            Manual   \n",
+              "3            1                 5    petrol      5-Speed            Manual   \n",
+              "4            1                 5    petrol      5-Speed            Manual   \n",
+              "\n",
+              "    Price  \n",
+              "0  657000  \n",
+              "1  682000  \n",
+              "2  793000  \n",
+              "3  414000  \n",
+              "4  515000  "
+            ],
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+              "      <th>Make</th>\n",
+              "      <th>Make_Year</th>\n",
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+              "      <td>Volkswagen</td>\n",
+              "      <td>2017</td>\n",
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+              "      <td>sedan</td>\n",
+              "      <td>44611</td>\n",
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+              "      <td>657000</td>\n",
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+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
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+              "      <td>2016</td>\n",
+              "      <td>red</td>\n",
+              "      <td>crossover</td>\n",
+              "      <td>20305</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>petrol</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>682000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Honda</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>white</td>\n",
+              "      <td>suv</td>\n",
+              "      <td>29540</td>\n",
+              "      <td>2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>petrol</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>793000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Renault</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>bronze</td>\n",
+              "      <td>hatchback</td>\n",
+              "      <td>35680</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>petrol</td>\n",
+              "      <td>5-Speed</td>\n",
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+              "      <td>414000</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>orange</td>\n",
+              "      <td>hatchback</td>\n",
+              "      <td>25126</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>petrol</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>Manual</td>\n",
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+              "  .colab-df-spinner {\n",
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+              "      spin 1s steps(1) infinite;\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    80% {\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "        const charts = await google.colab.kernel.invokeFunction(\n",
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+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 976,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 281,\n        \"min\": 1,\n        \"max\": 976,\n        \"num_unique_values\": 976,\n        \"samples\": [\n          200,\n          542,\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Make\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Volkswagen\",\n          \"Hyundai\",\n          \"Chevrolet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Make_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2011,\n        \"max\": 2022,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2011,\n          2014,\n          2017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"brown\",\n          \"black\",\n          \"silver\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"crossover\",\n          \"muv\",\n          \"suv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage_Run\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24390,\n        \"min\": 1117,\n        \"max\": 99495,\n        \"num_unique_values\": 878,\n        \"samples\": [\n          16240,\n          23724,\n          43273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"No_of_Owners\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seating_Capacity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7,\n          4,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"diesel\",\n          \"petrol\",\n          \"petrol+cng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"5-Speed\",\n          \"4-Speed\",\n          \"6-Speed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Manual\",\n          \"Automatic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 367323,\n        \"min\": 188000,\n        \"max\": 2941000,\n        \"num_unique_values\": 585,\n        \"samples\": [\n          423000,\n          290000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cat_vars = ['Make', 'Color', 'Body_Type', 'No_of_Owners', 'Fuel_Type', 'Transmission_Type']\n",
+        "encoded_df = pd.get_dummies(df, columns=cat_vars, drop_first=True, dtype='int')\n",
+        "encoded_df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "yOMk6mVYE6i_",
+        "outputId": "8b66bdbb-2853-43a4-f994-cec8a652c01b"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0  Make_Year  Mileage_Run  Seating_Capacity Transmission   Price  \\\n",
+              "0           1       2017        44611                 5      7-Speed  657000   \n",
+              "1           2       2016        20305                 5      5-Speed  682000   \n",
+              "2           3       2019        29540                 5      5-Speed  793000   \n",
+              "3           4       2017        35680                 5      5-Speed  414000   \n",
+              "4           5       2017        25126                 5      5-Speed  515000   \n",
+              "\n",
+              "   Make_Datsun  Make_Ford  Make_Honda  Make_Hyundai  ...  Color_yellow  \\\n",
+              "0            0          0           0             0  ...             0   \n",
+              "1            0          0           0             1  ...             0   \n",
+              "2            0          0           1             0  ...             0   \n",
+              "3            0          0           0             0  ...             0   \n",
+              "4            0          0           0             1  ...             0   \n",
+              "\n",
+              "   Body_Type_hatchback  Body_Type_muv  Body_Type_sedan  Body_Type_suv  \\\n",
+              "0                    0              0                1              0   \n",
+              "1                    0              0                0              0   \n",
+              "2                    0              0                0              1   \n",
+              "3                    1              0                0              0   \n",
+              "4                    1              0                0              0   \n",
+              "\n",
+              "   No_of_Owners_2  No_of_Owners_3  Fuel_Type_petrol  Fuel_Type_petrol+cng  \\\n",
+              "0               0               0                 0                     0   \n",
+              "1               0               0                 1                     0   \n",
+              "2               1               0                 1                     0   \n",
+              "3               0               0                 1                     0   \n",
+              "4               0               0                 1                     0   \n",
+              "\n",
+              "   Transmission_Type_Manual  \n",
+              "0                         0  \n",
+              "1                         1  \n",
+              "2                         1  \n",
+              "3                         1  \n",
+              "4                         1  \n",
+              "\n",
+              "[5 rows x 44 columns]"
+            ],
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+              "  <div id=\"df-98fb34d1-94c6-4953-8e0c-3d9eff1ed885\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>Make_Year</th>\n",
+              "      <th>Mileage_Run</th>\n",
+              "      <th>Seating_Capacity</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Make_Datsun</th>\n",
+              "      <th>Make_Ford</th>\n",
+              "      <th>Make_Honda</th>\n",
+              "      <th>Make_Hyundai</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Color_yellow</th>\n",
+              "      <th>Body_Type_hatchback</th>\n",
+              "      <th>Body_Type_muv</th>\n",
+              "      <th>Body_Type_sedan</th>\n",
+              "      <th>Body_Type_suv</th>\n",
+              "      <th>No_of_Owners_2</th>\n",
+              "      <th>No_of_Owners_3</th>\n",
+              "      <th>Fuel_Type_petrol</th>\n",
+              "      <th>Fuel_Type_petrol+cng</th>\n",
+              "      <th>Transmission_Type_Manual</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>44611</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7-Speed</td>\n",
+              "      <td>657000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2016</td>\n",
+              "      <td>20305</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>682000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>29540</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>793000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>35680</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>414000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>25126</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5-Speed</td>\n",
+              "      <td>515000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 44 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98fb34d1-94c6-4953-8e0c-3d9eff1ed885')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-98fb34d1-94c6-4953-8e0c-3d9eff1ed885 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-98fb34d1-94c6-4953-8e0c-3d9eff1ed885');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-bcfabb6d-150c-4540-97ed-59697a0bbd26\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bcfabb6d-150c-4540-97ed-59697a0bbd26')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-bcfabb6d-150c-4540-97ed-59697a0bbd26 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "encoded_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 3. Implement an 80/20 train-test split. Put the test data aside."
+      ],
+      "metadata": {
+        "id": "OI9SA8pUcIaw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "y = encoded_df['Price']\n",
+        "X = encoded_df.drop(columns=['Transmission', 'Price'])\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y,\n",
+        "    test_size=0.2,\n",
+        "    random_state=100\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "ME1Nrg0knlhV"
+      },
+      "execution_count": 76,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 4. Build some simple linear models that include no transformations or interactions. Fit them, and determine their RMSE and $R^2$ on the both the training and test sets. Which of your models does the best?"
+      ],
+      "metadata": {
+        "id": "O4d4qepqcK_l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "simple_model = LinearRegression()\n",
+        "simple_model.fit(X_train, y_train)\n",
+        "y_train_pred_simple = simple_model.predict(X_train)\n",
+        "y_test_pred_simple = simple_model.predict(X_test)\n",
+        "\n",
+        "simple_model_results = {\n",
+        "    'Train RMSE': np.sqrt(mean_squared_error(y_train, y_train_pred_simple)),\n",
+        "    'Test RMSE': np.sqrt(mean_squared_error(y_test, y_test_pred_simple)),\n",
+        "    'Train R^2': r2_score(y_train, y_train_pred_simple),\n",
+        "    'Test R^2': r2_score(y_test, y_test_pred_simple)\n",
+        "}\n",
+        "\n",
+        "print(simple_model_results)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7H4vFX99npMz",
+        "outputId": "952f63b2-c722-46d7-8041-82a6e5a02bf5"
+      },
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'Train RMSE': 152519.95541401798, 'Test RMSE': 138005.4862230456, 'Train R^2': 0.8265740183937349, 'Test R^2': 0.8612947063233363}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This model was the best"
+      ],
+      "metadata": {
+        "id": "8-INxLa5M_Gr"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 5. Include transformations and interactions, and build a more complex model that reflects your ideas about how the features of the asset determine its value. Determine its RMSE and $R^2$ on the training and test sets. How does the more complex model your build compare to the simpler ones?"
+      ],
+      "metadata": {
+        "id": "yUjGL-6xcRu2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "expander = PolynomialFeatures(degree=2, include_bias=False)\n",
+        "X_train_poly =  expander.fit_transform(X_train)\n",
+        "X_test_poly = expander.transform(X_test)\n",
+        "complex_model = LinearRegression()\n",
+        "complex_model.fit(X_train_poly, y_train)\n",
+        "y_train_pred_poly = complex_model.predict(X_train_poly)\n",
+        "y_test_pred_poly = complex_model.predict(X_test_poly)\n",
+        "\n",
+        "complex_model_results = {\n",
+        "    'Train RMSE': np.sqrt(mean_squared_error(y_train, y_train_pred_poly)),\n",
+        "    'Test RMSE': np.sqrt(mean_squared_error(y_test, y_test_pred_poly)),\n",
+        "    'Train R^2': r2_score(y_train, y_train_pred_poly),\n",
+        "    'Test R^2': r2_score(y_test, y_test_pred_poly)\n",
+        "}\n",
+        "print(complex_model_results)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g6R1GXpGnqBX",
+        "outputId": "9e7e1e0f-9ea2-4d1e-bdcd-8e068132df65"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'Train RMSE': 79436.08511430337, 'Test RMSE': 1748876.7672425678, 'Train R^2': 0.9529568399615526, 'Test R^2': -21.27505320456273}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The complex model has a higher train $R^2$ but lower train $R^2$. The RMSE for the complex model was lower for the train but higher for the test."
+      ],
+      "metadata": {
+        "id": "ZJUlqwz0NBXi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 6. Summarize your results from 1 to 5. Have you learned anything about overfitting and underfitting, or model selection?"
+      ],
+      "metadata": {
+        "id": "KjJlrkhCcX9E"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The dataset was cleaned by standardizing column names, handling missing values, and converting data types where necessary. Visualizations such as histograms and scatter plots were used to understand the distribution of variables like Price and Mileage_Run. Categorical variables were transformed into dummy variables to prepare them for regression analysis. This included variables like Make, Color, and Body_Type. An 80/20 train-test split was implemented to separate the data into training and testing sets, ensuring that models could be evaluated on unseen data. Without regularization, a complex model tends to overfit by capturing noise instead of patterns. While omplexity can enhance training performance, it reduces generalization without proper controls. Although no models in this scenario exhibit underfitting, simple models with poor training performance would suggest a lack of complexity. However, a straightforward approach with moderate regularization and a solid baseline proves successful."
+      ],
+      "metadata": {
+        "id": "Dyqs1dt7FQ9y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> 7. If you have time, use the sklearn.linear_model.Lasso to regularize your model and select the most predictive features. Which does it select? What are the RMSE and $R^2$? We'll cover the Lasso later in detail in class."
+      ],
+      "metadata": {
+        "id": "-NUED8MKcbOb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Was a little busy this week so did not do this part"
+      ],
+      "metadata": {
+        "id": "7j2oBaYYe_P-"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/04_hedonic_pricing/04_hedonic_pricing.ipynb b/04_hedonic_pricing/04_hedonic_pricing.ipynb
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/04_hedonic_pricing/04_hedonic_pricing.ipynb
@@ -0,0 +1 @@
+
diff --git a/05_PCA/myanswers.ipynb b/05_PCA/myanswers.ipynb
new file mode 100644
index 00000000..cb05056b
--- /dev/null
+++ b/05_PCA/myanswers.ipynb
@@ -0,0 +1,1293 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XbcdBvGPnmee"
+      },
+      "source": [
+        "## PCA and Text Analysis\n",
+        "\n",
+        "This assignment involves processing real e-mails, some of which are scams. Some of these scam e-mails have some offensive content. I don't think anything is worse than R-rated, but I just want to warn you that if you start reading the e-mail text, you might read something offensive. If that's a problem, feel free to e-mail me and we can talk about it more or you can skip the assignment.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gYRr-Y8Vnmef"
+      },
+      "source": [
+        "### Q1.\n",
+        "\n",
+        "Open the `Phishing_Email.parquet` data. It is available at `https://data434.s3.us-east-2.amazonaws.com/Phishing_Email.parquet`, and you can download it directly using Pandas by providing that URL: `df = pd.read_parquet('https://data434.s3.us-east-2.amazonaws.com/Phishing_Email.parquet')`.\n",
+        "\n",
+        "We just want to look at the first step of cleaning text data, so you can get an idea of how it works. The `Email Text` variable contains the actual text of the email and the `Email Type` takes the value `Phishing Email` or `Safe Email`. We want to predict which emails are phishing emails from their contents.\n",
+        "\n",
+        "Use the `str.split()` method to break the `Phishing Email` values into **tokens**: The individual words or symbols that create text data like emails. Natural Language Processing is primarily about analyzing the frequency and co-occurrence of tokens. Print the results of your split and examine it.\n",
+        "\n",
+        "In words, how would you clean the tokens and use them to predict whether the email is a phishing scam or not? A short summary of the kinds of tasks you would do and how you would run a predictive algorithm is fine."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import pickle\n",
+        "from collections import Counter\n",
+        "from multiprocessing.pool import Pool\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import r2_score\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "jCd4EWiTnu6-"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_parquet('https://data434.s3.us-east-2.amazonaws.com/Phishing_Email.parquet')\n",
+        "tokens = df['Email Text'].str.split()\n",
+        "tokens.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "F52XiFNVn3Os",
+        "outputId": "df5091ad-19b7-4928-83c4-505176433651"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    [re, :, 6, ., 1100, ,, disc, :, uniformitarian...\n",
+              "1    [the, other, side, of, *, galicismos, *, *, ga...\n",
+              "2    [re, :, equistar, deal, tickets, are, you, sti...\n",
+              "3    [Hello, I, am, your, hot, lil, horny, toy., I,...\n",
+              "4    [software, at, incredibly, low, prices, (, 86,...\n",
+              "Name: Email Text, dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Email Text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[re, :, 6, ., 1100, ,, disc, :, uniformitarian...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>[the, other, side, of, *, galicismos, *, *, ga...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>[re, :, equistar, deal, tickets, are, you, sti...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>[Hello, I, am, your, hot, lil, horny, toy., I,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>[software, at, incredibly, low, prices, (, 86,...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> I cleaned the data by removing stop words (e.g., punctuation, common words like \"and\" and \"the\") and numbers, then one-hot-encoded the tokens as features. Running the algorithm would then remove any phishing or scam emails."
+      ],
+      "metadata": {
+        "id": "jt5l3wlzn9r0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Pdc5w2dnmef"
+      },
+      "source": [
+        "### Q2.\n",
+        "\n",
+        "I aggregated all the emails into a single vector, and removed the punctuation and very common words (e.g. \"the\"). Run the below code chunk to open it, and use the Counter class to look at the most common words:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "QQF9wlnaDTOh",
+        "outputId": "3db119c6-7f9f-4be2-fb65-b3785c8cc175"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c00382cc-4a25-4473-bf64-3ddcf0731f5d\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c00382cc-4a25-4473-bf64-3ddcf0731f5d\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving all_tokens.pickle to all_tokens.pickle\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "8p-zQqlQnmef"
+      },
+      "outputs": [],
+      "source": [
+        "with open('all_tokens.pickle', 'rb') as file:\n",
+        "    all_tokens = pickle.load(file)\n",
+        "\n",
+        "from collections import Counter\n",
+        "token_count = Counter(all_tokens)\n",
+        "token_freq = token_count.most_common()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iEZes08qnmeg"
+      },
+      "source": [
+        "Plot a histogram of the occurrences of tokens. What do you notice about the frequency of occurrence of different tokens? How does it look?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gdf = pd.DataFrame(token_freq,columns=['token','count'])\n",
+        "gdf['count'].hist(grid=False,bins=100)\n",
+        "gdf['count'].describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 748
+        },
+        "id": "T7mPbdp2D4nl",
+        "outputId": "5ec30a96-b5ef-4c8e-adbd-ce2a4313236c"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    48691.000000\n",
+              "mean         5.687889\n",
+              "std         27.717476\n",
+              "min          1.000000\n",
+              "25%          1.000000\n",
+              "50%          1.000000\n",
+              "75%          2.000000\n",
+              "max       1365.000000\n",
+              "Name: count, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>48691.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>5.687889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>27.717476</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1365.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> Looking at the shape of the histogram, we see that most tokens only appear once (the 25% and 50% are both 1.0). Since there is a high concentration at 1 and lower concentration at the higher values, it will be difficult to make predictions on if an email is a scam or not."
+      ],
+      "metadata": {
+        "id": "y4aiJ8Dt_fSj"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NIT3jqXknmeg"
+      },
+      "source": [
+        "### Q3.\n",
+        "\n",
+        "Load `Phishing_clean.parquet`. This is the text from the e-mails broken into the most common 2,711 tokens and one-hot-encoded as features/covariates. So each row is an e-mail, the `Email Type` takes the value 1 if it's a scam and 0 otherwise, and every other column is a word or symbol that occurs in at least 15 e-mails.\n",
+        "\n",
+        "1. Perform an 80/20 train-test split of the data.\n",
+        "2. Run a regression of $y$ on the one-hot-encoded emails. What is the $R^2$ on the test set? On the training set?\n",
+        "3. What words have the largest coefficients in absolute value and most strongly influence predictions?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "NMjjvr4n8szG",
+        "outputId": "a8911bb9-acbc-441e-b540-a5df83043c2d"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-ff04466e-7a00-4725-9c81-6aa555cfdf02\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-ff04466e-7a00-4725-9c81-6aa555cfdf02\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving Phishing_clean.parquet to Phishing_clean.parquet\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_parquet('Phishing_clean.parquet')\n",
+        "\n",
+        "y = df['Email Type']\n",
+        "X = df.drop('Email Type',axis=1)\n",
+        "\n",
+        "## Part 1\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
+        "                                                    test_size=0.2, random_state=125)\n",
+        "\n",
+        "## Part 2\n",
+        "lm_0 = LinearRegression(fit_intercept=False).fit(X_train,y_train)\n",
+        "y_hat_test_0 = lm_0.predict(X_test)\n",
+        "y_hat_train_0 = lm_0.predict(X_train)\n",
+        "print('train: ', r2_score(y_hat_train_0,y_train) )\n",
+        "print('test: ', r2_score(y_hat_test_0,y_test) )\n",
+        "\n",
+        "## Part 3\n",
+        "rdf = pd.DataFrame({'variable':lm_0.feature_names_in_ , 'value':lm_0.coef_})\n",
+        "rdf['abs'] = np.abs(rdf['value'])\n",
+        "rdf.sort_values('abs',ascending=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "PC4wGeDuD9Fw",
+        "outputId": "fd78a2d7-0df9-451b-e12b-cf2b37785426"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train:  0.6210107280960204\n",
+            "test:  -0.00026838432670373\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        variable         value           abs\n",
+              "710         1997 -1.415784e+13  1.415784e+13\n",
+              "421         1995  1.158467e+13  1.158467e+13\n",
+              "1133         19, -1.025713e+13  1.025713e+13\n",
+              "340         2005 -9.773947e+12  9.773947e+12\n",
+              "1146       -0400 -8.062341e+12  8.062341e+12\n",
+              "...          ...           ...           ...\n",
+              "1399       union  2.822876e-04  2.822876e-04\n",
+              "565         east -2.746582e-04  2.746582e-04\n",
+              "2268     ravages -1.068115e-04  1.068115e-04\n",
+              "457          due -6.484985e-05  6.484985e-05\n",
+              "2709  leadership -6.103516e-05  6.103516e-05\n",
+              "\n",
+              "[2711 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-26d33515-6a16-4d34-97ad-f949039bcc57\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>variable</th>\n",
+              "      <th>value</th>\n",
+              "      <th>abs</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>710</th>\n",
+              "      <td>1997</td>\n",
+              "      <td>-1.415784e+13</td>\n",
+              "      <td>1.415784e+13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>421</th>\n",
+              "      <td>1995</td>\n",
+              "      <td>1.158467e+13</td>\n",
+              "      <td>1.158467e+13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1133</th>\n",
+              "      <td>19,</td>\n",
+              "      <td>-1.025713e+13</td>\n",
+              "      <td>1.025713e+13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>2005</td>\n",
+              "      <td>-9.773947e+12</td>\n",
+              "      <td>9.773947e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1146</th>\n",
+              "      <td>-0400</td>\n",
+              "      <td>-8.062341e+12</td>\n",
+              "      <td>8.062341e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1399</th>\n",
+              "      <td>union</td>\n",
+              "      <td>2.822876e-04</td>\n",
+              "      <td>2.822876e-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>565</th>\n",
+              "      <td>east</td>\n",
+              "      <td>-2.746582e-04</td>\n",
+              "      <td>2.746582e-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2268</th>\n",
+              "      <td>ravages</td>\n",
+              "      <td>-1.068115e-04</td>\n",
+              "      <td>1.068115e-04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>457</th>\n",
+              "      <td>due</td>\n",
+              "      <td>-6.484985e-05</td>\n",
+              "      <td>6.484985e-05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2709</th>\n",
+              "      <td>leadership</td>\n",
+              "      <td>-6.103516e-05</td>\n",
+              "      <td>6.103516e-05</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2711 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26d33515-6a16-4d34-97ad-f949039bcc57')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-26d33515-6a16-4d34-97ad-f949039bcc57 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-26d33515-6a16-4d34-97ad-f949039bcc57');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-065892e0-ef76-4f5f-b796-57001cb4f063\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-065892e0-ef76-4f5f-b796-57001cb4f063')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
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+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-065892e0-ef76-4f5f-b796-57001cb4f063 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"rdf\",\n  \"rows\": 2711,\n  \"fields\": [\n    {\n      \"column\": \"variable\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2711,\n        \"samples\": [\n          \"december\",\n          \"division\",\n          \"video\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 739914491692.5779,\n        \"min\": -14157843842225.965,\n        \"max\": 11584666689061.584,\n        \"num_unique_values\": 2643,\n        \"samples\": [\n          0.1072845458984375,\n          0.08745574951171875,\n          0.0716705322265625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 733947399037.043,\n        \"min\": 6.103515625e-05,\n        \"max\": 14157843842225.965,\n        \"num_unique_values\": 2593,\n        \"samples\": [\n          0.2853240966796875,\n          0.023303985595703125,\n          0.04402923583984375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> The test set had an $R^2$ value of -0.00268 while the training set had an $R^2$ value of 0.621. The words that have the largest coefficients are 1997 and 1995, which aren't too helpful."
+      ],
+      "metadata": {
+        "id": "buUwUR_AAcRF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZKqpaoyqnmeg"
+      },
+      "source": [
+        "### Q4.\n",
+        "\n",
+        "Take the matrix of one-hot-encoded tokens (the data, less the outcome variable, `Email Type`) and perform a principal components analysis decomposition with two components. Plot the first two principal components in a scatter plot, and hue the points by whether they are a phishing scam or not. Do you notice any patterns?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "reduction = PCA(n_components=2).fit(X_train)\n",
+        "Z_train = reduction.transform(X_train)\n",
+        "Z_test = reduction.transform(X_test)\n",
+        "sns.scatterplot(x=Z_test[:,0],y=Z_test[:,1],hue=y_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "9mdoZUV59NSR",
+        "outputId": "3ad76e72-fb20-4e05-996c-618c6091ba1a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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Buiw3/5vcBY2y+o8vNEDNF7f3JCZYW4+jFQRBEITaJ2ZeGrDCchuPfbeDtUcKq45ll1p5ZP4uXh3XlnVPXMnxYhsquZywQDWRgVrkclG5ThAEQWjaxMxLA1Na6eBYfjmb04rIKrVyVetIwg2aGte9svQIdpuVrgnBdIgzEW3SicBFEAShqagshoJDkLEFCg5L39eyNWvWMGrUKGJiYpDJZPz444+1/pwXS8y8NCC5pVae/XkvS/fmVB1rFRnIGxM68dh3O8krq85xKbTYKTeXQogJ5Ir6GK4gCIJQG0oz4af74djv1cdaDIbR/wNTbK09rcVioVOnTkybNo1x48bV2vNcCmLmpYGosDt5c8Uhr8AF4GBuGf/3814eHpLidVwuA7W7Avb+CH+8KHWariypuwELgiAIl15lcc3ABeDoSvj5gVqdgRkxYgQvvPACY8eOrbXnuFRE8NJAFJTbWLA1w+e5YwUWgvQqrwTdq1uHEmreD/t/htWvwId9Yf17UFH7U4uCIAhCLbHk1wxcTjm6UjoviOClobDYXDjdHr/n88psGLVSK4DkCAPP9NFiMBjh0JLqi9a8BsWptT1UQRAEobZYzX/v/GVC5Lw0EAaNEo1Sjs3puzpuu6gA7u4ZTIdIFc3c6USaj0H6RqnD9Ok2fya1FBB5MIIgCI2P1vj3zl8mxMxLAxEeqGFSzwSf51pHBZIYpOT2FCs9AwqIDFDBwV9h5zc1L67IB7erlkcrCIIg1IqAcCk515cWg6XzggheGgqtSsG9A1swqUc8itO2PPdMCuaTW7oTHhIMiVdAQm/Y8z0cXub7gdqNB6W6jkYtCIIgXFK6YGlX0ZkBzKndRrrg+hlXAyOWjRqQCKOWf41sy90DW1BS4SBAoyTMoCZIrwaXE8pzoKIQet0NzQdJCbq5e6ofIKiZFOAIjVfhUcjcAtm7IKIdJPSC4CQQ/aoE4fJhioXrP5OSc61maakoILzWA5fy8nKOHDlS9X1qaio7duwgJCSEhATfKwP1RQQvDUyARkmARkmz0NMO2srhyHJY9Ej1NrnAaLjmNdjyORz/CzpOggGPgimuXsYtXALZu2DudVBRVH1Ma4KbF0Jst/oblyAIdU8XXOezLFu2bGHQoEFV38+YMQOAW2+9ldmzZ9fpWM5FBC+NQf4B+O4272Nl2fD9VLjrT9AYQR8KKtHXqNEqPgELbvcOXACspTD/Vrh1EYQk1svQBEG4PAwcOBCPx/+u14ZEzEU3dLYyWPWK73MuB2z/CgxRInBpDCpLpTLf6RshZ7dURbPqXJFUCtyX0nTpvCAIggCImZeGz14B+fv9n8/eDs5KUBjqbkzChSvJgA0fwuaPwWWXjsV0gbEfQXirmlvez+SoqP0xCoIgNBJi5qWhU+kgpLn/8xHtQClmXRo0p13a1r7hverABSBrO3x1PRSlSst+ypoNOAGQK8EQUTdjFQRBaARE8NLQaY0w8Enf52Ry6D4NFGICrUErTYcN7/s+V3ICCo9IS399HvB9TffbQR9We+MTBEFoZETw0hhEtINR70izMKdog2DSNxDcrN6GJZwnR+XZm6nl7gNtoBSIXvsGGGOk44ZIuPq/cMWDoA+pm7EKguClsSSwNhaX6vUUH9kbA50JOk6EFleBOUuacdEFSwmfW2ZBbFepFkhgZH2PVPBFpQW1Aezlvs+HtZT+a4qFbtOkYlQuByjUEJQgarwIQj1QqaRechUVFeh0unNcLZwvu11aOlco/l4LGxG8NBZuh/QJffsX0PFG+OI67yTOyPYweZ6o89IQGWOlWZV179Y8FxAOEa2rv5fLISSp7sYmCIJPCoWCoKAg8vLyANDr9chksnPcJZyN2+0mPz8fvV6PUvn3wg8RvDQWWdvhmxvhxi9h4d01d5/k7oEVz8HIt0ETUC9DFPxQ6aDnnVCWA3u+g1PTpsGJMGHu2ROyBUGoN1FRUQBVAYzw98nlchISEv52IFirwcuaNWt47bXX2Lp1K9nZ2SxcuJAxY8ac9Z5Vq1YxY8YM9u7dS3x8PP/617+47bbbanOYDZ+lAJb+U9pxUlHof/lh7w9w1b+afvBizgZrCchVUi5IY8gHCYqHYf+F/o9KBQY1gRAQBgHRUJohBTQao7REKAhCgyCTyYiOjiYiIgKHw1Hfw2kS1Go18kuwFF6rwYvFYqFTp05MmzaNcePGnfP61NRUrr32Wu6++26++uorVq5cyfTp04mOjmbYsGG1OdSGzWGFnF0Qmiz1uvDH7Tx3vZDGzFYOaX/C4kfBfLLAW1xPuO49qVZKQ2eIkL5OLRMVH4dfH4Hd30lbqJsPkgKcsFZiB5kgNCAKheJv52gIl1at/oYcMWIEI0aMOO/rZ86cSVJSEm+88QYAbdq0Ye3atbz11luXd/Ail0t1QMyZEN7a/3WBUdIn+qYqdw98M9H7WMYmmDUC7lwlJbc2FqUZMPtaaRv1Kcf+gE+uklo+hKfU39gEQRAauAa1jWH9+vUMGTLE69iwYcNYv36933tsNhtms9nrq8kxREo1QByV0ptdfE/f1w15TmrY2BRVFMGK//NzrhCOrqrL0fx9qWu8A5dTnFZY8xrYLXU/JkEQhEaiQQUvOTk5REZ6b/eNjIzEbDZTWVnp856XXnoJk8lU9RUfH18XQ61bcgV0noy71bXw+wvQ7xHoPKW6IqsxFuvoj/G0vBqaaja8oxKyd/g/f+yP6kTY82WvgOI0OPqHFEyUnACn7e+M8vw47bDvR//nj66UGjIKgiAIPjX6hfWnnnqqqm03gNlsbpoBTGAkJ/q9gqJzDrq83ajaTELV6yEKyqwcKYU526280kJP1KV4LnMW5O2DtL+kInhJV0rbfZXqS/HoF0ehlGaVio75Ph/W8sICt8oS2PE1rHhWqqkC0q6g0e9BynDQ1GKvKLlC2iLtjy5IagkgCIIg+NSgfkNGRUWRm5vrdSw3Nxej0ei3SJBGo0Gj8dMTpon5bq+FT/4sIDa4OZV2OznmI17nHS7333+S4uMwd4x3kKBQw+T50OyK+gtgDJHQ/zH46d6a52Ry6HDDhT1ezm5Y+pT3MUcl/DAd7loLUe0vfqznIldIJf+3f+n7fJ/7RS8jQRCEs2hQy0Z9+vRh5cqVXseWL19Onz596mlEDUvbGCN2l5vUAgs5Zu9dRZFGDSrFWf443W4pj8J1lu1+tjJY8lTN2Q2XHb6dJG3xrU8tr5aKvZ1OqZVq31xIcT6rWcor8cXjgY0zpaWd2hSSBAOfqnk8eSi0uqb6+6JUSF0N6/4H+36W+iA5fC+hCoIgXC5qdealvLycI0eqZwdSU1PZsWMHISEhJCQk8NRTT5GZmckXX3wBwN133817773H448/zrRp0/j999+ZP38+ixcvrs1hNhqd44MJ0qsoqagZgDw8OIVIo48ZKLdLyuXYNR+O/yUVROsxXSqQdubSiKUADv3m+8kdlZC7t357KRnCYfC/ofe9kLMH1AHSFunAKP8dmX1xVkqviT+FR6TE2dqcZdIFQ+97oO11sH+RFFi2vlb6czGcXFIqPArzpkDe/ur7tEEw5TtpZkilr73xCYIgNGC1Grxs2bKFQYMGVX1/Kjfl1ltvZfbs2WRnZ3PiRPWbSFJSEosXL+aRRx7hnXfeIS4ujk8//fTy3iZ9mpggLfPu7MPdX24ltcBCXLCOm3s3o0tCMOEGDRabC4P2jD/S3L0wa3j17pXU1bB1Foz7BNqMlvrunOKyg+csS08VBZf+h7pQuiDp61Q/oIuhMkB0RyhO9X0+rkfdBAZak/QV0abmudJMWPq0d+ACUnG+bybCtGUQllz7YxQEQWiAZJ4m1jLTbDZjMpkoLS3FaDTW93BqRV6ZlbJKB4UWO8/9so/92Wauah3B8PbRdIozoVXJMenUGN0lUg+k3L01H0Spgfs2e8+kmLPh05PNH325ey1EdaiVn6nO5eyGjwbUDNaUGrhnnVQQsD7l7YcP+/jfQXXTAkge4vucIAhCI3Qh798NKudFOD8RgVrsLg+TP9mIw+lm/l19SAoL4KPVR3liwS62Hi9hX1YpLkuR78AFpC3BBYe8jwVGwfCXfV+ffDUExlzaH6Q+hbSAyd+B8bSfKaQ53LoITPW4NHaKo/LsW7/Lcv2fE+pcaYWd44UWUgssFFnqYLu9IFzmGtRuI+H8lFudvLn8IHHBOt6Z1IWJH2+gtLI6D2bbiR0MaRNB8xEJnHXPypk1TWQyaD5QelNf9jQUHJaWNXreBT1uh4DQ2vhx6odaD8mDYfpKqCyWfnZdKARGnvveuqAJlPJbrCW+z/taahLqnNvt4Wh+Of/6cQ8bU4sAaB9r5MWxHWgdFYhaKUrKC0JtEMFLI1Ruc7IxtYg5U3vyzsrDXoHLKSv253Fn/0QiItpKNVvOJJNDZNuax7UmSLkaYjpJPZXkSmmbclPstSOTSTMvxgY4o2SIgv4zYPmzNc8lDmgczSgvAxkllYz/cB1mq7Pq2J5MMzfMXM/iB/uTHFGL9YIE4TImlo0aIaVcRr8WYbg9Hlbu9798sHh3Lu7rPpQClTP1/wcEnGVexhAp5cOYYptm4NLQaQOlnUjDX5a6T4OUj9P1Vhj9P2lXklCvXG4PP23P9ApcTrE53Xy0+iiV9prnBEH4+8S7UiMUFqjh5j7NKK10IEMG+M6NUClkyCNaw52rYdXLkL1dqpQ74HGI6167VWSFvy84UaprkzxE2rqt0EgzLqeCGaFeVdidrDnsv8v7xtQiym0udGrxa1YQLjXxr6qRahMdyHebMxjePoqfd/reHTSqXSh4XNK24HEfS0XolBqx5NCYKDV/b1u4UGvUSjlRRh1Q7PN8mEGDWtlEe40JQj0Ty0aNSUk67JoHP9xN0OZ3uSnFyT1XxBBuqFmgbXzXGOLzV0kdl0GaZTFGi8BFEC4RjVLBtH6Jfs/fO6gFJl099gMThCZMzLw0FoVHYdYIKK/OcdGtepGEsXP49KZeLDtYwvqjBQRqVYzrEkv3UBshs+6B5O2XbgwuJ5TnQHme9H1AuLS9WqG6dM8hCI1I83ADT41ozctLDnjtbJ/aN5EuCUH1Ni5BaOpE8NIYVJbAooe9AhcAPG4Cfp5Oy9vXsF4NN/WMo02onIjirYR+MR1iu4H2EhXqs1vg2Cr48d7q7bsaI4x8G1KGifwZ4bJk0qmY0iuBq9tFsTmtCKfLQ8+kEMIDNZh0IqgXhNoigpfGoKIQUtf4Pue0oc/Zwt0layG3DP7YIPUokithxKuXbpmoOE3qs3P6x0ubGRZMgztXQUyXS/M8gtDIGLQqDFoVSWEB9T0UQbhsiJyXxsB9ju2WLick9oPcPVI35JQRcOcaiGx3aZ7faYP1H/iv+PrnG2CzXJrnqk8ej7QkVpZT+12lBUEQhIsmZl4aA40RghL8d0I2RoNSLzXr8zil6zWBl+757RVSoTuZDGK6Slt1C49IeTggtRlwVICmEX/yNGfD/p9g86dScb42o6HXnaKeiiAIQgMkgpfGwBgNI16BbybVPNd2DKRvhPbja6+0vVqPrd2N5Ax8h+VpDo6WeujTVUH34Apilt8L4W2kcvuNVVk2zLsJMrdUH9vwPuz6Fqb/DiGJ9TY0QRAEoSYRvDQWCX1hygJY/RJk7YDAaOh2q7TbZ/f30PveWntqO0o2hl7HtNnbcbqlpaNvtkK0ScP8yd8Tr3eBuhHPumTv8g5cTqkohHX/g+EvSvVWBEEQhAZBBC+NhS4IYrvCwH+CrVRKyt39PYS2gDEfnr3qqtslzS7YyqQqrQFhF7QLyVWaTbQrl0U3xbIs1cnsrQV8NC6BLqFu5Pk78TiCkMlVYIprfG/ybidsn+v//P4fYcCjDbP/kSAIwmVKBC+NiT4EkvpLCaX2cmg5VOpPdLYlm4piOPALrPg/aSZBJpPKzV/zhtS76GxsZZD6J7rfHqdlaTrIZCQnDea++15HvupFZLvnV1+rNcENX0B8L1DrLsmPWzfkoND6P61QA6JKqiAIQkMidhs1NgoVBMVDRBspmfRcuSZHV8LPD1RX2vV44PBymDtGSlI9m6zt8O0kKE2vulfptqLY9Y134AJgLYVvJlRf21jI5dBjmv/zXW45ewNL4bLkdnvILKlg5f5c5qxLY3NqEblma30PSxAuG2LmpSkry5FmXHwpOgYFB6VkYF8shbDsX9Xfy+TSTM+VT8LXE3zf47RJwVJ4yt8adp0LawkdJsCZAVlYCnS9BRSK+hmX0CB5PB72ZpmZ8tkGzJXVZQxahBuYM60HccGNOHldEBoJMfPSlDkqzj4TkrHV/zm3A+J6SMtAKj1M+EKa6TFngsV/J10Kj130cOtNQDgMexFu/QXajIIWg2HcJ3DLj2CKre/RCQ1MdqmVW2dt8gpcAI7ml/Psj3soszrqaWSCcPkQMy9NmUINaoOUH+OLrxomFcVSDZeNM6V2BEkD4JrXYfEMyNgMQc0gNBkcldB9KoS1kvJo0jdJia/xPWr1R6o1hnDpK76XlODcmLd+C7UqvaiCIovvIoZ/HMqnsNxOoFa0BhCE2iSCl8bMVi7NglQUSVuVA8K8dx15gM5TYNNHNe9V6SGuu/cxayls/gT++G/1sZxdENVBClwAts2Bq/8LHheseR2yXpCCl6Qr4fpZENLikv+Ydaqx7ZYS6py/wAWklDKb01WHoxGEy5MIXhqrslxY9RJs/0KaKQCpv9D1syAkSTr/1TgY+BQUHYEjK6vv1Zpg0jwwnrEkUpbjHbiAlA9yKnBRakAXKi2lfHIVuE7+Evd4pKaNObvg9hW18uMKQkPRPNx/E1KjVolBI2ZdBKG2ieClMXLapAqwW2d5H8/aDl+Oo2LKL7hcbgILDsEPd8KAf0CP6VCUCrpgadbFGAOKM/74Tw9wTrFXgC6YooEvkxPZjzK3ju5rX0bh8vHps6II9i6EfjOkXTyC0ARFBGoYmBLOqkM1c78eGpJCpFHM3glCbRPvMI1ReS5s+tj3uaJjlGQd5d0NpRRc8xl0uw2iO0qNBsNSoLJEyl+xmWve6ysgydtHfspE/pnWkWvmnCC32IzixDr/Y0tdIxXRE4QmKjhAzSvXd+S2voloVdKv0DCDmv+Oac/YLjEoFeLXqiDUNjHz0hjZLVLCrB/K0lSWHXQzrUtnPOlLkX0zCTxu6WR0Z2knjTao5o3Jg2HFv70OeeJ6snhfMb8dKAGg0OqRdueYM30/uSFCquIrCE1YpFHL09e0YXr/JGxON3q1gshALXK5KGgoCHVBfEQ4X7YyMGdJeSEeT/2ORRUASv9VYZ2mZgxICiRox0xku+ZVBy4A2Tvgt8d9L+sYY6HjRK9D+R3u4OMNuVXfz91RRn7ns/RR6nGH2KkjXBbUSjlxwXpahBuINulE4CIIdUgEL+fitEPuPlh4F7zfEz4bChs+lIKY+hIYAd1v930uOJEj9lAmtFaj2znH9zWFR3xX19WHwNUvSEm/0Z3BFI8rNIWC8urlpGMFFja42lDRfor3vTIZXPWM7+3XgiAIgnAJyTye+p5GuLTMZjMmk4nS0lKMxvNvPuhX9i74dHDNfJDmg2Dcx9IySX0wZ8HK/8Cub6tngiLbcWLox0yYn8uXowNJ/n6o//vHfw4dxvs/bymE8lxKc45x14YQNqQWV52SyeDJgVFc0wwiCjeiUSqlejBnbtUWBEEQhPN0Ie/fIuflbCqLYclTvhNZj/0BJcfrPngxZ0HaWtjxDbQajqf3vdgqy8l3aNiar+C/83LIL7NhJVza2uy0+X6cczVlDAgFSx6mZQ/z1LW/MDatGPfJGMnjgZf+yGFukI4f7rmNCJNYJhIEQRDqjghezsZWBsfX+j9/cIlUQr+OeMxZ8NUNyHL3SAeO/Y4MULe4isxO/+XhRamAVGsinyAcnW5BtfWTmg8U2gKrPpqVu7JYe6SQxFA9w9pFEWXSolWd1sdHHwqGCFL2vMXXt/2bfy0+wpG8cuQyGJwSyr+uTiBCVQmI4EUQBEGoOyJ4ORuZXCqx72vmBUAbWGdDcTidsHshqlOBy2nkR3+nZ5eDLH1wIEcLrbjdHuZsSkfXfRpdKkvR7P+uemkpqiP28bMZN/co+7LLqh7j1aUH+eTmbvRrGYZaeTKAMUTADXPQ7fuJ3kfe4Ns+3SgLTEYh8xBy/FcMX8yC3vdA/0dFZVpBEAShzoicl7OxV8Kvj8GOL32fv28ThLf6e89xnsz5WRjnXQcFh3xfkDyYX1q9zAMLDlcdUspl3NMngjEt1QS4zQQaTTg0ofxzaTaL99RMONYo5Sx5uD9WhxuFXEawXk14oAby9sMHvX0/r0oH92489zKUIAiCIJzFhbx/i91GZ6PWwcAnfO+gGfo8BEbXyTAcLhdH8svA7fR/kdtFtEmP4rTtmk63h//9lcvg2elc9W05uZokSuUmr8ClU6yR/03szPT+zbA53Ww8VsQbSw9QkJmKNXMXtpyDeFwOqbaLz8FVSj2RBEEQBKGOiGWjcwlKgNsWS12T9/0IhkjoeguYEkB7CXYznYdKu5vFR+y0TBlH4IbXfV5T0X4Kf52w8NaETsyYvxOn23tCbVi7SEw6FSeKKgC4smUIs8dGQ0UB5CxjZHIU/+yVQrlCy3COErTyGanpI+CJ6wFjZ8Kv/4CiYzWfXKWr/n9bmVTFF6RWBBr/fWAEQRAE4WKI4OV8mOKkr7Zj6qVnj1alwGJ3k9VqPK32fwulGV7n3VEdyQvuylvzDzKqYzQ392nGrL/Sqs4btUoevKolP27PJCFER59mQcweGwU/3oPs9FL/2iAME79Gtv3DqsAFQJaxWapzM/JtmHeT9+AS+kqJvR6PVD9mxb/h4G/SfurWo2DwsxDayDtNC4IgCA2KWDa6EPXUbFBdls5tXU3c9kM2B6/5DnOfx6WAILw1xVe+QOmYudw8/wQAi3ZnM6Zz7Mmqn1om90pg/t19cLjdKBUyWhntfHlTC/jzDe/ABcBaguybidD/sZqDsBRIQVNYy+pjIc1h7IdScbuS4/DpEDiwWKro63ZJM1WfDYHi47X34giCIAiXHTHz0tCVpOP89UkM/Z7nyeEpXP/NPrrGX8W1HYbjQYbaGMG2DSWkF0u9jjwecFvNfDSxLeUuJT/uyGTU/9bicHloEW6ge3x75LYiZLvn+34+mxkqiyAwqkYVYU/ePhj3GbLMzRDeBkKbS3k/LgdsmQXWkpqPV1EEu+ZD/xkgV9Q8LwiCIAgXSAQvDZnHg+v4enZ2epabPj1C5/ggXhnfkWC9isJyO2qlnG83p/P7gTyv2wJcpTSzHGTgD3IyS6wAGHVKXhgcSkTWSmShof6L14EUtOiCawQvZYEt+D0njL6tbiLCeFpvJWspHF7q//EO/go9poM++IJfAkEQBEE4kwheGjJbGbnaFtw+L51Kh4v1xwpZf6yQp0a05rc9OexIL6lxS/tYI6H5m1CU7mXO+DvROMswaBQYjMEov7tJKnA35gOphkt5Xs3nBAhLqZFXg0JFQcxgHp61g6SwAL6e3ovooJOJunKV7y7Vp+iCQKG6mFdAEARBEGoQOS8NmUJJhtNISYXD6/BHa47xwnVtaBEe4HW8RXgAHwwPIjRnLfLmA0j+ZRzx84YQ/Of/odo+u7oy77a5uPv5yGsBPJHtcRuipV1Dp2gCKRj3HWtzFEzvFYlaIWfVoeqEXnQm6Pug/5+j7wNi15EgCIJwyYiZl4ZMpcfsrlm5tshiZ85fqcwdbSLfFkpGqYNYk4qYysNELHsCz+j/IZs1orouTKsRsP796gcwZ5EddRWGwa9iWv+ylJcik+NIGUlqt6fRBcYRc98Wio/vxqkMRB2eiKwwlSmZL6BwVlDYcywFQRGUWx0YtCdnVOK6Q6fJsPNr78F2nwZRHWvpBRIEQRAuRyJ4aeCSIkw+jy/YkcP93RLptGQsnWRyqCymtNu9ZF43n5itr3sXtFOowGmt+rZwwHMctQfhChtNq4mDMcmtWNxKZu8oZ9bc4zw8RM2knok8tL2Q69voGH7gJXQHFlTdH3rsD0JDW+C6+SfQxksHDREw7AXofTfs/0VqrdBmlLTF3GqWtl5by0CplvJpREVeQRAE4SKJZaMGLsyoZ3zXmBrH3R7YXRaI54Y5ENsNS7tJfK8ajctuRZaz0/vi4+ug5VAAbC2Gs1vTg7vmbmXqnG30/eAg7d47zjOrLbSIi6LS4WJ7egkGjZIbusXTy1joFbhUKTyKYusccJ0MktxuKQlYFwK974VBT0NABBxeDt9Phfm3wJ75UFEIy56RasIIgiAIwkUQwUsDZ9KpeHJEG54c3opgvbREEx+i4/3JXeiXEonMGAOdJpHf+5/8d2UWgSo3BJ0xq7H/Z+hwA+iCyen1NNO/2kWlw+V1ydK9ORwvtNAzKYQOsSbkchkDW4URefhb/4Pb/oU0o1KeBxs/hI8GwNvt4dPBkLUdFj0MP9wBmVulnkybPoEFt0P32+DnB6HkxKV9sQRBEITLglg2agTCA7XcMaAF13WJxenyoFHKq7cqBzQDUzw7dmTi9oC1vBg63SgFLKe4HPDbEzDlO1YeVtRoHXDKNxvT+cewVvRKCoaKIoLMWXjcfjpqA7gd4LbDqtdh2xdS64RmV0gF6oqOSVukz1RZDNvmSgXurCVAwkW/LoIgCMLlSQQvjYRCLiPapPN9Ui7HdTIeKSWQ6EPzYPhLsPI/UuNEgJITlJWXcaxE6/sxgPxyG53jTcSoymHrN1CagazDDbB3oe8b2owBjww8wLhPwGWD7F2Q0AfS/vT/wxxcDNe9L+XCCIIgCMIFEsFLI1Rhc5JXZmNTWhEWm5OeSSF0SQhGKZfx+Q4Lz4SEE3hkJdwwB6dcjcMNxepYKvVRtI0p9vu4baIDCXUVoCovlhJtg+LAboG4HpCx2ftifQh0ngg/TIeINlLX6Q3vSzkuRcek+/2RKaQWAjpRtE4QBEG4cCJ4aSTsThdFFgduj5s1hwp4euFuTl/9Gd4ukjnTejLl0410HTGSq5snEPLrP1AqNGRd9z17SlV8sWwvt/RJJMqoJcdsrfEc/xwQRqjlMGTvhrBk+O5WaVfQmI8gc5uU4+KohNbXQvIQ+PFeKfE2fSNs/1LqPF2WI33f9RbYOtv3D9N2NJRmQbN+tfNiCYIgCE2aSNhtBDKKK3ht6UFu+nQ92aVWnvzBO3ABWLI3l8O5ZXx1ey8W7K/gicNt2HH1fDImLmPiV0fQKBVsOFbEfxfv59XrO9KnRWjVvVFGLe9N7EjHyo1wZAUk9YeVz0nF5a56Bla9CCfWQb8ZMOkbCIyBr2/w3jHkdsLyf0Pve6QCdwWHoN24mj9MYDR0mwrtxoIp9qJfE4fLTU6plezSSirsznPfIAiCIDQZYualgcssqWTCzPUUWux8e2dvFmzN8HvtrHVpPDWiNV0TggnSq/j2QAVXOCrJKrVic7qrHu+Bb7YzqWc8U/sm4nR7KLc5STbYMYZ3guBw0JoguhOoDbBgevUTHFgk1W8Z8wFEtofsk1uyA6OlxoumOKlVwJTv4MRGiO8pBSnbZksBTfNB0HECaExgCL/o1ySrpJKvNhzn283p2Jxurm4byQODW9IsRI9cLrvoxxUEQRAaBxG8NGBut4fFu7LIKrUyuWcC208Uk1/uf/dPYbkdtULOR2uOAdKMSkpEIABaVXVH59JKBzNXH/O69/cHe8Ki26HwKK4b5kC/R1F8Oqjmk3jcUiLwoKfhp/vBGAuj/wdLnpRmW05peTV0uRnUARCSDAolBDeX6sK4HTUf9zxll1Zy82cbOZpvqTr2w/ZMVuzP5ef7+5EYFnCWuwVBEISmQCwbNWDmSge/7MwG4MpW4Xy3NZOuCUF+r++aEIRRV90AsbjCTszJ5ombUgsZ1CrC532DW4URuv9LqTaLzYxiwTQ85hxpi7XPgWVJsycAg56CxTO8AxeAw8ukrdJqvbSkFJQI5TnwQS/4610wZ57Xa3CmLWnFXoFL1ZCsTj5ZcwzrGfVrBEEQhKZHBC8NmEIhQ6eWZkxcbg/7s80khgUQEViz35FCLuPBwS3RqRRM6SXVTrE53WSXVtIuxsisv9KY1DOeoW0jkZ1cWZHJYFjbMP7bx41pwyvVD+a0IfecY3ZEJgOVXlpaKk7zfc2e76WaL+ZMkMshOFHalbTxQyi+8AJ1dqebhdv9Bz3L9ufWaGIpCIIgND0ieGnAArUqpvaRegfZnW5MOhXPL9rH6zd0YlCrCE6ld7SJDuTL23vxy84sRr63luAANR/d3I3b+iaSXlzB2xM707dFKA9+u52EED2f3tKdL6b1ZPl93Xg9cjlR34+prgcD4KiQ8l5UfurKhDSXApG2Y6E83/c1IM3cWArg0BJY/KiUBDxmJsiV0k4kfzM7fshlYND4X+nUqxXIxd9oQRCEJk/kvDRw3eKNDGoZzPdbM5jWL4m3lh/ivq+2Mb5bHB9M6Ybb45HqvpitzFl/HID3fj+CTqWgR2IwN/VuRmiAmn8OT8EDlNnd0uyErYzk32+DM/sgnVRWZoahb2P67W7wnLa1SamBIc/BLw9Ds754ojvhN0VWpaNqmgek+i+HfoPWI8FWKs3KKFT+7q5BqZBzc59m/Lwzy+f5W/okEm6oOSslCIIgNC0ieGngIoKNvDo8msN5ZRTLA3h8eCs+XnOM2evS+HLDcUZ2jObeQcnc+cUWHr06hZTIQFxuD0UWO1+sTyO9uIJKh4t3Vx4hvaiClpEGpl6RSE6pi9a9nyLix4k1n1QfSoYynq9Tg7n3xuVEHZiFouiotMOo1TWw9k3I3CJ9xXWHyHaQu7fm43SeAvt+8j628xu4fZnUYVrlv9qvPy3CA5jSK4GvNnovO3VJCOLaDtHIZGK3kSAIQlMn83g8vhvdNFJmsxmTyURpaSlGo7G+h3NpWApg6yzceQdwhbUhP+IKSgJbkl/hZkdGKSaNkmZhAby94hA7M0oBSAjR88r4Dqw+lF9jZxHAkyNaE6KwMkS5g5A/n5WeAyCmCxkD3+KWn0s5VmBBr5az+r7OhG9/B4pS4chyacbkFH043Poznt/+gSxtrXRMoYJOkyChN/x0n/fMjVyJ58Yvkf35Jox6G8LbcKFrPUUWOyeKLMzbnE6l3cW4rnG0igok0njhwZAgCILQMFzI+7cIXhoLa5nUwTl/PzlBXbh+zgEyiiuJNGr4/NYeTPpkA2ard7G2z27tzl1zt/psxBioUfLS+A7MXZfKY32MtAtxk2dxszbLwzvri8gvswHw0tj2XKffjX7BFO8HaH899L1fyo/xeCAwBpfTSk5hKRFBBlSbZ8KOr6Wt1adxpFxL+cDnCf6sFyh1cPefUv7MRfJ4PGK2RRAEoQm4kPdvsWzUWGgDpa/Q5hw7UkBGsZRgGx+sZ9Wh/KrARS6DTnFBGLRKKuwuvx2k5XIZMSYdU/u1oFgGR9xyQgNtQAEd40zEGmRM6h5HXHgQ+rIztib3ugtaDoOvrq+esTHGIO91D/rm17I+28oVRWkozghcUAeQ2/1xZm8s4R+tx6PZ+y3unfORD3gU5AouhghcBEEQLj8ieGmEDuWWVf1/83ADm1OLABjbJZbRnWLYnFZEmdVJmEHt8/7mYQH83+h2vL3yMH8ezsfjgXYxRl4YHMqEim+40ViBwl6G3DUNVi6E3vdBjztg8ydShd0ut8CnV4HTJhWj6z4NynKQOSsJ8pQRYgrhaP+3CU9cSvDuz8Bmxpo4hPwu9/HAb8VkljqYPnQoUXu/RZ66iuJO0wkODqmT104QBEFo/ETw0gglnVZFtrjCTqRJy/iusbSMCGTq7Oruz90TgwkJUFNk8a7K+89r2/DwvB10jDXxzo2dUSrkyGUyvtmfy+0dp9Jq4QgY/hJs+ljaMr35Y+h2G6RcDUVpUpsApw06XA9xPWH+LeCSnkMGtOt1D5vjp/Hk/s7c3PkzAtRyDpoVxGU7eaxvEMfLQCmXcnMcATHMXJvO1Cv1RImcFUEQBOE8iKoY9aDM6iCtwMK+LDPpRRUXXBW2ZWRg1azKmkP5DGsbyciOMby85IDXdfM3n+D16zugUVb/MXeJD2JPVin3D0qmfZyJf/24h3u/2saD32zH6fZQogzHctOvUh5L2+vAWgL5B6RaLaZ4qSN0/gFQqKHDBFjyRFXgcops44d0du+htNLJjN+ysbrlTDZsZ+zOO+i39BompT5NqFEPHSeQ134aX23J4Vhe+cW9mILQiBVabGSXVFJUbqvvoQhCoyJmXupYdkkl/1m0j6V7c3B7QKOUc3PvZtx1ZQvCfVTO9SUmSMfX03tz+xebSS+qZE+WGafLO7/kwSsimJxYTlDqeyy7bTJL05zsLXAxvnMU29LLyDVbq3ogAdhdbn7YlklOqZXXRiYQkPanlHB7Suoa2PIZ3PwjxHSVEnUP/ua9k+g06nVv8sKQj1h/IoAhubPQH1ooNWkMCEdenArfT8V93Yf8nmei3FbMrsxS+iaHXfDrKQiNUUmFnS3Hi3lj2UFSCyw0DzPw2LCUk01VfS/3CoJQrU5mXt5//30SExPRarX06tWLTZs2+b129uzZyGQyry+ttmksJxRZbDw0bwe/7ZECF5BK+H+6NpWPVh+9oBmYlKhAvr+7L4sf7MfV7SK9yuJf0yaYqaZtRC0Yg3bT/2j2ZR/uPP4o78jfom9ANr2bhzB3w3Gfj7vuaCGlbh3smlfzpDkL1r4tzcgExkKZ72Jxp67taizjgR4G9Go5jHpXun//L9IOpAlzkR9eSmyAlGgcH+ynmq8gNDE2h4vvtmYwfc4W9meXYXW42ZdtZtrsLfywPRO7U/TnEoRzqfXgZd68ecyYMYN///vfbNu2jU6dOjFs2DDy8vL83mM0GsnOzq76On7c9xttY5NfZmPTyeTaM83dcLxqe/L5ijRqaRdjonWUkSFtI6uO398jkOA1z3pfnL4J9v2E8vub0ankVNj9/4LMyCsEXbDvk3t/gJJ06HYLxHTxP7jozmgCglCVZUrLTfNvlpaebGbp/t8eh+QhJAcpMGiUBOlVlBQVgK3M/2MKQhOQXWrl9aUHfZ57bcnBqp2EgiD4V+vBy5tvvskdd9zB1KlTadu2LTNnzkSv1/P555/7vUcmkxEVFVX1FRkZ6ffaxiSzxP8vJZvTTZm1evakzOogz2zFXHl+/X9aRQXSOioQlUJGsKsAnFbfF5ZmEKjycLYdxqE6Odhrdm4GpA7Rljz48V5oPQq0QTWvkcnhigelujQBYbDqRakf0g1zoN8MiO0Gve8BtYHIoADmTu3Gi78eoCA/B35+WJqhEZqEIouN1AILJ4os5/13uanLLrVic3ov88pkMDAlnOn9kyi3Oikw2ygot1FuE6+ZIPhSqzkvdrudrVu38tRTT1Udk8vlDBkyhPXr1/u9r7y8nGbNmuF2u+natSsvvvgi7dq183mtzWbDZquesTCbzZfuB7jEIgI1PHFlFMOSFCidFVgVBr4/YOPzzfm4PR70aiXlNidHcst4e+VhDuWUkRgWwMNDWtI6yohR57sPUK7ZSmmlnQ9v6src9cdBVnjWcYQ7cxiYEs4fB2s2VYwI1BBj0ng3ajxdyjDpN+1V/4QDi2HiV7D8GcjcJp0PToThr8Du76BZP9CHgD4MRrwKP95dXRcGQBeM+qYfaK+pIESvILPMRfKRZVLfo3EfS/cKjZLd6WJ/dhn//HE3ezKlf5P9W4bxf6Pa0Tw84LKtz1Na6aixPNw8LIB/j2rL6kP5rNify8bUIiZ0i0Mmk/HbnmzuGZhMSoSBQD///gXhclSrwUtBQQEul6vGzElkZCQHDhzweU+rVq34/PPP6dixI6Wlpbz++uv07duXvXv3EhcXV+P6l156ieeee65Wxn+ptdKZaVPwXxQbV0oH5ApmtL2RQTfezxe7rQTrVaw6mMf9X2+vuier1Mq6o4W8NLYD47rGolFVF3NzuNzsyijhoW93kFFciVIuY1zXWEyRCaAJ9L0EY4ojsHAn/7mqN3eWWtmfU31NmEHNFzfEE12yA5KHSq0ATqcxQr9HYPcPsPED6ZghEnrcDoP+BQHhoNDg2fElsiMrIOEKKbG37/2w7J/egQuA24ls6dOoRrzKAz0MaJVy6fojy6GiQAQvjVhaYQXXz1yHw1Wd0P3n4QLGz1zHovv7EReir8fR1R+ny02lw0mkUUOu2YZOpeD/RrfjoW+3U3xa3tqm1CKGtYuka0Iw4z9cx8vjOzC2s/e/f0G4nNVqe4CsrCxiY2NZt24dffr0qTr++OOPs3r1ajZu3HjOx3A4HLRp04ZJkybx/PPP1zjva+YlPj6+4bUHsBTAt5MhvebPbO90M5VXvUCFR8Pwd/6k1Mf0ulYlZ8WMK4kLrv6lfzS/nGve+bPGFPQVzU3MuaIY5fe3eJfnV6hh/OfSMk5ZNrnTNpFtkXE4p4RYdSWJynxi1jwBxcelvkNOK2yZLeWpJA2AduOkx/tynM8f0T343+S0mIClvAyj+TCRYaHSvQDfnNYAMqQ5XPmEtLxUngux3bGoQ7DkHCXip0nSNbcthsR+5/XSCg1Lhc3JUwt389MO38t//7y2DdP7JV2Wsy8Ol5u569KICdbz0LfbGdMlljKrg1935/i8/oMpXXn8+1043W6WP3Il8Zdp0CdcHhpMe4CwsDAUCgW5ublex3Nzc4mKijqvx1CpVHTp0oUjR474PK/RaNBozm+Lcb0qz/MZuACod3+D+spHSa8M8Rm4AFgdbvLMtqrgxeVy892W9BqBC8Bfx0qZk5zE1Lv/Qr75U8jfDzHdpEJzumCY+DV4PEQ68ojQeOi8834oOQ7hraDLzVJjxUPLoDQdrntP6kG0+hXYOgeU/rdxyrfOZqt8EA/8kkW0KYDPx4fT2pqBLPC0P+vgJGkJ6ecHoCy76rA+qhPKcbMoHvyalGzsL2FYaPDKbE6/iekAqw7mcVOvZujUl98sgkohZ0i7KJ79cTcf3dyNAI2SyZ9s8Hv9X0cK6JIQxJ+HC8gqrRTBiyCcVKsJu2q1mm7durFy5cqqY263m5UrV3rNxJyNy+Vi9+7dREdH19Yw68Zpb9Q1uJ1gKztnc2WFvPqTqtnqrOog7ctnG3PJ0ybBiFdg8nwY8n8QlgwBoRCSBKHNQaVD5qyEAY/BjV/BVc9AQCTs+1lKxB3zIagCpNmWHneCLgjsZykmZzOjU0ljzC61MuLzw+SFdAdDBChPBpgDHoOd38Kw/0oJvBO/hisfR2ZOR7H0KVYpriD7xt+kJahGyu50kVFcwcZjhaw/WkB6UQWVF1iIsDFTKWSE+mlNARBl1KJUXH6zLqfEB+t46pq2fLclgzKrAxn+XwuvM02qha4g/D21XqRuxowZ3HrrrXTv3p2ePXvy9ttvY7FYmDp1KgC33HILsbGxvPTSSwD85z//oXfv3iQnJ1NSUsJrr73G8ePHmT59em0PtXad7c1YJgd1ACFKddVa+ClyGQxpE8l1nWPRqRVkl1QSYdTiwUNCsJ71+E7OjQ/RS8GOQiV9+aJQSYm2vz9fHZQEJ8GwF8Flg+PrYPd8sJXjSRmGq8+DVFjtGPMPQOHRGg9nSxrK6hPeM0d3/5zFl1dZ0fd5ANnWORDZTpqFWjRDqt4LkNAbxn2K8pcHadfdycubPbw4LoSAGs/Q8FlsTlYeyOOJ73dVBSxqhZxnRrbhus6xfpOum5KQAA33XJnMfV9v83n+1r6JqBSXb3FvmUyGTiUnSK8kPljPNR2i+NHPEtsVLcNYOH8nerWCUEMjmGEWhDpS679BbrzxRl5//XWeffZZOnfuzI4dO1iyZElVEu+JEyfIzq6elSguLuaOO+6gTZs2XHPNNZjNZtatW0fbtm1re6i1yxAFEW18n2szCgwRRBq1vDOxC6qTn0qVchlvTOhMTJCOx7/fydVvreHa/61lzro0HE4P13WJQe7nQ9u0fkmYzvFG6cnbL5X3P302pTgVfrhDSsRd9LAUwOTsQrbmNZSzh1Hu8PBbz7kUjPhYCrpOUQeQ3/UBvtvpnZSbVlhJYWh3nF1ugynfQd5+WPlcdeACcGID/PIgDP0PKpeVRbtzKGik5dKPF1bw4DfbvWZa7C43z/y0l4O5l08Nm97NQ5jUM97rmEwGz4xsQ2JoYwxLL61Qgwarw83U2Zu5c0BzQgNqzlRd0yGKo3kWKhwunhzRmst4skoQaqjVhN36cCEJP3WuOA2+nQK5e6qPtbgKrnsfjDHAqSWHSr7dnE5skI7NaUUs2lVzyemRIS0Z1SmaDceK+e/ifVjs1Z/y7x3UgtGdYmgebvA7FE95PrIvx0HOLt8XDHwKDi2FLO9Pz5UdbuY55y3Y3TKeid1O8OqncbUcQU63R7lvSSk7MssJ1qt4tH8EY1rr0VbmIs/ejkwfgiyqPcy7GUpO+H7O0e9y1NibwZ8e4beH+tMmuoH9+Z2D3enm6YW7+X5rhs/zV6aE8/7krhi0l0dXjpIKO3llNjYcK0SrUtAjMYTwQA0GzeXx859LrtnKi4v3M7hNBBFGLX8cyGPd0UICtUpu7BGP2+Phz8MFXNshmr+OFHDXlS2IFM1LhSaswSTsCmcIToSbF0rF2yqKpFyQgHCvLcFqpYLm4QaeGNaK1MIK/v3zXp8P9eHqo4zpEku4Uc0bEzrhdHlwuj0YtUoCtUqiTGf/JWe3W9EU+K7yCUDBIQiKrxG86A4sYPQ1dzJ5Xjq39xtHcLP2lKhjuObjQ5RWOog0apg/IZoERRGynx+E7J3VN0/4wn/gAlBwBEXk1WiU8kb5Bmd1uDiW76e4H3CiqIJKh/OyCV6C9GqC9GpSIgPreygNUqRRywtj21NYbsft8dAlwURIgJpKuwu7w0XraCP7s808Mm8Hn97aXQQugnCay+O3aENiiJC+zkGhkJNT6r8ir9XhptzqZEByOPnlNvLMVuRyGeEGDRGBWlTKs68IVjjlaIKTpA7RvoQkSc0YzySTVSUYrtqbQTvX9wQkj+KN8W2579vdfD42hmZHv5QCtNMDl9BkKnVRyHvei2b/Qt8JzKEtiNfZuLl3s/NuUtmQ6NQKOseb2Hai2Of5djFGAtTin5xQLVCrIlArLe82DzeQXVpJudXJ2iMFPPfLPtrHGll43xWi95cgnEH8Jm3AzpXcqVUp0KgUxAXrveq/+GSrgPJsqQu0QoVVHUZBj8cI+9VHIrRSC/G9Yc3rNU7Z24zneIUUWKhkLsjcjnbzZwzocjtrHnyK8JLtENcTFt4FKcMhrhs58deyJcfF/N8rUSvHcvOVE2nrOkD4svvAdTLBV6XHE9sduSWP+wd1RNsIi3GpFHKm9G7GlxtOYD+jy7dcBvcOTEbfCGeUhPNjc7oottiRyWSEBahRXERScrRJByZICNUzrmscerUclaLx/VsQhNp2+ab817JKh4v8kjIceUdwH1uDZ/9iPHkHoLxmSX5/IgI1RPtZ/unaLIiQs2xH9VKcBmtegU8GwXvdYP4tRBVtRtOsO+a+T3nvRgoIxzX5e+zHN0NgFKhO+8RniCC7wz0EmaRlgEGJmqqcGfX2z4hwZiJP3wiB0dIW6KB4cqKHMO3nAu7/KZ3MEiujk1WEaqEkuAOlNywAuUKq6TLuY2RrXgV7OWpX421MFx+s48vpPYk77ZNypFHD57f1IClM1OhoijweD8cLLXzxVxovLN7PtFmbee+PI2SfZeb0XDRKBSadSgQuguCHSNitBRV2J0eyC2nnOojiu1ug8uQygkyGp/PNyAY+AaaarQ582Z9tZvInG7xKh8cF6/h6ei8SzmfXRlEa/HQvHP/L+7hMhmfSfJaUJ5Oks9BcXYLaXgIyBZVh7dBU5iMrOoJMEwguB+VF2WRHXsm0H3N4akQb9qZmcKfqV0wb36x+zFt+BnMORLWFWdfgjmjP5/Ev8MLvOdzZM5w7kvIIX/tvKDwC6gBcXW5D0e1myDsAa9+UAiG5Atf01ShiOpzX63M6j8dD7smGdnanm/BADWEGNbp6WKrJNVsptthxAyF6aQv85VhR9nJwotDCsQILa48UUGZ10iMxGLcHvtp4nI9u6n7O/DNBECQiYbeeZZdYaaUpQfHZBO8Ghx4Psu1f4Alvjaz3PZyzKh3QOiqQJQ8PIL/MSnpRJSadiubhAUSZznMNvCy7ZuByaizL/8Wwid9ywzwzj/cNotdfj8ENc9AuuBnZ6fkqQQnoJs3nnRUVOF0eWoSo6ZO/DtOaN70f01EJMR1hyZNgM1OYcgNfbyyjTXQgdzbLIuynW6uvtVtQbHwfT9YWZO3GVO96cruQrX1D2oGlOf8ttU6Xm92Zpdz95daqOjlqhZwHBiczpVczQnxsRa1NkUatSLC8DJRU2Fm8O5tXllQnv8/bnE7baCMzrk5hy/EiRnaMqccRCkLTJJaNasGBnFJUaX/47cwsW//u2XfdnCarpJI1h/KZufoY+7LNRBq156zf4iXtT//n8g8id1by2bgYWsZH4RnzISx92jtwASg5gWLeZN4Z4GblJBMtNSUEO4u8l5QAl8YELjscWwWAR67E7nLzcC8jYWv/7XMIsvSNYIwFdXWgIs/cAvYLq4mSVWpl8icbvQr82V1u3lh2iL+OFJzlTkG4eDmlVq/A5ZR92WZWH8zncG4ZtsuourIg1BUx81ILZMiRFRzyf0FZDnjO/QvtWH45Ez5aT0G5verY+38c4f3JXbmqdcT5dZg9W2dmhQrkSoLe7ww3zMETlgJHV/q+tugYCrcD/aFfpV5HHa/H0/EGip0qbOoQArASqPLgcVql5RGPh+C0Xxnd+jESDG4p78af3D3SNvLck9vCjdHV7QTO06qDeX5L8L+5/BC9m4cQHihmQi6VSrsLm9NFoEZ5UYmpTcVve/y3/fhxeybvT+mC3EclyTKrg+xSK4t2ZlNosXF1u0haRxnFbJ0gnKfL97dOLQoJUOGO7e7/gtBk/yX7TyqpsPP0wt1egQuA2wMPfbuDvLLzrECb2F9KivWlzXXVs0Mu+9n7FoHUEmDrbFjzKszsh+zoStRuGzfN3ct9P6eTWuoGRwUkXQmA6shSJrdRSt2s5WeJk/VhYDvtufv/44IbM+7J9N/nKa3QgtPVpFK76k2xxc6GY4U88M02bjuZmHqiqIImljp33k7PRTtTud1JlFFXoxVCmdXBd1szuPqtNbz7+2G+2niCWz/fzNRZm/5Wkq8gXE5E8FILEkIDsEd3h4Aw3xcMfBqCEnyeKiq3UVrhOPkm4bszr93lZn+2+fwGowuB6z70LuUPENoCBj0N30k9ptj/C2hNUqDhjyHCO8BZ/iwGWx739AjizyPF/Ly/FJk5E/o+ABojeNzE/XozkcFGnG3H+n5MhRpnfB+pq7VMhrvfDIjrdn4/22k6xwf5Pdc8zHBZ99K5VEor7Xy85igTP97Aiv157Egv4a0Vh5nw0TpOFFWQVmDhYI6ZzOJKnK6a3c6boqFtIv2e650U6rPcQU6plf/8sq/G8X3ZZcz6Kw2HSywzCcK5iGWjWhATpCO3NB71TT+h+Pne6mJtWhOegU8ja9a3xj25ZivpRRXkl9n483A+Izv5TvKTy6BNtBH1+b4ZG8Kh+UC4Zx0cWCwtWTW/EiLawq+PQ/Ex6bqDv+IZ8Tp0m4ps00c1H6f5QMjeIdWJCWoG7cdLXabNmQxo2ZZ2MWW0DNXg2fQJMks+3DAbDi2BExuwVpShG/gvFLl7kOXvr35MhQr7DV+R4Ykgbvwcyk2twBBOiD70/H620/RvGY5Bo6Tc5qxx7h/DUghrhEXvGpo8s40PVx/zOhakV/HyuI7855d9/H4wD48HjDoljwxJ4brOsXWeKF3XUqICaRsdyL5s7xwtpVzGUyNaU2Zz4C7xEBmoqVpeW+yj3ccpX288wbQrEs8/IV8QLlMieKklkSYdFZrWqG74CqWtBJxWqb+PMQ5U3m+kuWYruzNKmL0ujbVHpC7R7WJMxAXryCiunka+oVsc13SIZkd6CbszSwk1qIkN1hEScI435sBI6ev0xpBFxyBjY/X3bieywsPQ5148ciWyrZ9LS0pyJbS/HnpMkxJxJ34NFYWwfS6U5+GJ7YExugfT+sTSJUaFbHMW5B+Er66HlkOh5dXYtBHc9dMxpvX6mFbKHAJyNmEPiKY4ohdf7rUzqquaH7JaMSIkBq1LwVmydPyKDdLx7Z29ueerraQXSa+ZTqXg0atT6JV04cGQUNOaQzVrFD0+rDUv/rqfQ7nVM3LmSifP/bIPtULOpJ4JPnM+mopIo5bPbuvB7L/S+GrjCSx2J32ah3LvoBa8tuwgfx4uwKRT8djVrRjZKZpgvZriCrvfx7PYnbgvzxU4QbggInipRXqtErTxQPxZrztRWMGJooqqwAVg7objPDIkhce+34nHAzf1bkZogJqpszdXXfPG8kMMbRvBf8d2IOJCk1FN8dDnflj1kvS9QoXMYeH73cV0a38/id2ngt2CTKmR8lE2zYSIdnBkBWz5vOphZOV5yOVa+vZ8lLWZTsbH90WefxA8bqmx46Gl2FrdzZrDBaw5DCEBapLCrqK00sGRvOMAjO2exJcb0rmyVSQRxoubIZHLZbSPNfH93X0pstixO92EGtSEGzTnl9gsnNOZaUOBGiVGrdIrcDndWysOcVWbCKlqbBMWbdLx2LAUpl6RhM3p4rfdOTz0zQ4KLVKQUlrp4Jmf9qBVybm+WxxD20YyZ/1xn4/Vt0UoAaIKsyCck0gEqGcut5vMkkrmb/HuRHwgp4zVh/L5YEpXrmgRypUpYbyz8nCN+5fvy2PFvrwLf2KFCrpPk/JTTuW5BEYTFqAhMG8LsvwDyFY8J23pnjUM9i6EqPZegUtF+5vYP34lT9tu4d6fstiRUcbh7s9ib3mt11NVWqrzc4osdrYeL+ZIXvUbXrnNSZf4IDYeKwT+3qf0SKOWNtFGOsUHEResF4HLJXRlincOV3SQlqP5/pO8C8rtVNgvj/wNlUJBlElLdqmVl5ccqApcTvf6soPkmq2kRAbSIbZmAS6VQsbTI9pcWCkEQbhMieClnsmQoVcrfOZq/Lwzi2d/2ssd/ZNYddB/W4FP/jxGfpn1wp/cEAEDn8Zz32Zcd6/HY6+kj+YIYb8/BpUl0H4sLHoE3C4IToKc3VW32hOv4o+4u7lmTirf78xne3oJX21K59r3NrClxxt4Wo2sujZYYcPfysHJXdXcekUiK/bnIYrQNlyRRi1TelUnmhdXOIg4y9ZejVKO5hwNQpua3Rneu950KgW390vik1u68+9R7bDYXARqlXxyS3fuG9gCo1aJTAb9W4bx431X0DLSUE8jF4TGRcxP1jO5XEazED0DUsL5emPNwnX5ZTZsLvdZt0YXWexc9OYOtR6ZWo98/m3Iet2JZuX/Sa0LCg9DQl8wZ0rXedxe253zezzG4/MyOXOHrNPtYcb3+/hm2v+IHPAUSjwEuzyM7xTOdztqBmDD2kYSpFfxn0X7GNE+ihB9007wbMyC9GpmDE1hcJsIZq4+RpHFTotwA0adEnNlzeB7Qvd4wgyXV6J0Qmh1/yqDRsm7k7owd/1xPv8rFY9Hqvo8pXcC9w5swcNDU7i5TzPcHunaczViFQShmgheGoDQQA239mnGLzuyKDtjBiY2SEewXs3QtpEs35fr8/4+zUMxaC9+eaTEYiW376uEajyEFaeC2wmx3b0L3BWnSgm/MhnIFOS4TVjsJT4fL8ds5WCBncOyMB6Zv4O7eobzSN9QNBot87dkYne5USlkjO0Sy4Tu8dw6axMmnZoRHaKadHJnUxBq0HBV60i6NwvB7nITqFHw5e29uPXzTV41T65IDuX+q5IbZXfwv6NdjLFq19uDg1vy3u+H2XaipOq83eVm1l9pyGXwj2Gtxa4iQbhIInhpAMIMGjxuN1/c3pOZq4/x+4FclHI513aMZnSnGEBGv+Qwooxacszey0MqhYxHhrbEoLm4T21FFjsfbCjmsw0ZLL65GWEKNZRlQUgSZG2XEntL06W1nT0/wIB/wJ9v4j5HUTIPHn7emc3QNlG8+Wcm3ZqZuH9gIqM6xaKQy1DKZaw+lM9/Fu3j3oHJJEcY0Covrze6xuz0WYL2MSYWP9ifE4UVFFhsJIcbiDBqzr0LrgmKNun4cnovbp+9maSwAK/A5XRfbjjBrX2TSAgRncYF4WJcXgvSDVi4UUdiWAD3D2rBxzd3540JnWgVaSDMoKFDnImYIB3z7+7DyI7RKE7OTnRtFsSCe/qSFHb+DQzPdCinDDV2/pgaT6zKjLv9eGgxGLbNhQ0fwtDnqpeLdn4DtjK4fhZRBhVale+/PuEGDRV2F1kllYQapGWgD9YXYHPL+XFHJjd9tpGnFu4mJEDNf8d04EShhW83paNXi+ClMZLLZcQE6ejdIpSRHWNoHW28LAMXAIVcRsdYE4sf7IfjLGu5NqebcmvNpTZBEM6PmHlpQIL1aoL1akor7Nicbq5MCffaNpkQoueV8R15ckRr3B4PgVoVwX8jR6TS7iLAU87kdlosVhtFCg30nIHp+DI49Bvk7YMdX8PUJbBtjlRsrzQDVDqCDDr+b2Qbnly41+sx5TJ4cUxrlu3Lp220keOFFgByzDa09hJGJ2sY16UXIQFq8sut3PPlVnLMVhY/2A+DVqz5C42fXC4jyqQjLsh/nppchgjWBeFvEMFLA2Q6S0ASoFFesjoQ5VYHR4pdvLg0l/xy6RdtvxY2/jtqIs3K8yBtrVRF99BvUs+ilkOhPA++n0agzcw1E38i5bZWvLcun7QiG20jddzbw0jivleJaX8Hhcqoqro03WN1mP76L90q87EMe4sNeTY+WXOMF8a2J0in+luzR4LQEEWatDQL1XO8sKLGuRHtowkLFMnpgnCxRPByGduVUcwjC/Z7HVt7tJiJs3byw9R7iF7/LnS8Eb6dIjVuPIPxhyl0Hf8p70auxdoiAX3JYfQ/fAP2clq0HMJ72+y43B7UCjl3dDWgnb8AnDY8hxexveRKtp0o4WBOGXf0by4SdYUmJ9KoZfbUHkybvYXUAkvV8T7NQ/jXyDYXnacmCIIIXi5b+SVlvPjbQZ/nskut7M22ILtrH0GWY2h9BC6A1KTRZcew+T0MciW0vhau+ie4nDgdNgwaJdd3i+WmdhoS1j0NTml2R7P5Azr17A1IlYTHdo298ArBQv3zeMCSL22j1wWD8vLMczmbpDAD8+7qTa7ZRmG5jRiTjrBA9WWbEyQIl4pI2L0cuRzYLCUczbf4vWR9po1nlmVSZD/HurxcIXWonvKd9Aa27j3YOouAikwe7RNIXJAGuUxGefMR0rUAFYUYNdJfvTKrE/fl0YC4aTFnw+ZPYfa18OkQWPYsFKVRo/CPQESglg6xJga2iiAlKlAELoJwCYjg5XJUloMyfz/Bev/T1iEBGjKKK0mtDJAq8foSnATl+XDN67BgOmydLRW1KzqGbMW/iVg8laviZPxrZQH/d7w9+cM/ArkCZ9vxHCyRlokGtgrHqBUTgI1KWQ58fxv8+hgUHJK20m+aCZ8MlOoBCYIg1DIRvFyOKgoJ3/k+d/YM83larZDTOiqQ/dllvLCmiNwRn4LyjGUdtQHX+M9wh7WEPQulTtNnkGfvoK3nMHdf2YKxPZKxR/eAKd8ji+vGdQF7WDmtGU8NSUR/uTWic7urgjxKM8HVyPr/5O6FExtqHq8shj/flrqRC4Ig1KLL7F1DAECuRJG6ius738O+dmH8sreg6lSAWsG7k7rw6Z/SJ+j9ORa2ODrQ7aZVaI4tw1i0h9KwLhRED2DBdgczuhrRHF7i96mUO7/G3bojrUJkhG95C7bPRgGEA+FyBZ5R70LgGNBcJj1dLPmwewH8+RpYCqSltisegc6T/M9wNSQej7R93p/9P8KgJ0AVW2dDEgTh8iOCl8tRQBgEJxH+5zM8P+ZLHuxp4EC+DZNOQQtdJcWKMtYfk2ZSYkxayh0yes88Ruf47kQa+5G5u4I9S44DcFvnNkQr/K/huxRa9mSV0d+xG9X22d4n3S5kP90HMV0gsl1t/bQNh70CNsyEP1+vPlZZDCueBXMGDP534wjiVGdJrlaoERO6giDUNvFb5nIUGAXXfw697iZo4SRazuvPqE1TGLDiOmLnDyPCmkbryEAAhraL4uedUnPGHeklLN2bw55Mc9VDfbLdgrvjRL9PldtqMi1NLkxb3vU/ni2zuSyydi15sM7P67DlM+l8QyeTQddb/J/vcrMUHAuCINQiMfPSBLlcbhxuz9mb4kV3knb/FB6VvjdnAeBuNZKikE68eaOat1ccRqeUU251EhesY2THaAK1KvZnm1m6NweHy8O6o8W4J4xEfnAR5HnXjKlsPY51pWGYNG4o991UEoDS41IzSHkTL9pVUeSzXg4AbpeU/BzSvG7HdDFCWkDv+0EbCOGtwWmFvQuh6Cj0uB0Uon6JIAi1SwQvTYi50sGJogrmbkgjt9TGVW0iuKp1BHHBPpq/yRUgqznxljnoba7/cBvdmgVxY/cEQgPVDEgJJ63QwoJtmRRZ7HRvFsxnt/bgzeWHGJCggbXvQP/HcLpcKPd+h0uhI6/1zawvi+CJ37K4u3cEA6O7ozq61PfAWwwBZRMPXKBm0vOZVI2kSZ9KB21GwrJ/waqXQGOEbrfBiFfBFFffoxME4TIg83iaVmEGs9mMyWSitLQUo9FY38OpFaWVDnLNVv46IiXa9ksOIyhAxS87s/nPL/u8rg0zqJl/Vx+ah/vIpSjNhI8HSImjAD2m85l+Gs8vTQPgk1u6cyy/nNQCC99uTve6VadS8MGUrqSo8on98grweKi48ll+0VzD1gwLKw8WU2iRZhmMOiXLJgYTNW+4NMNwOn0I3LkaghLO/kNXFIGtXFq2CAiT3kAbm/J8+GK01DPqTMGJMG2ptKTXwFhsTkorHQAE6VXoc7bArOE1a7rE94Yb5zaOxGNBEBqcC3n/Fjkv9aDM6iCvzEq5zXHB9xZZbLy78jBXv7WG537Zx3O/7OPqt9dworCC5xfVfFMsKLfz31/3U2b18VyBUXDNG1XfuuJ6sSlD2uYaF6yjoNzKoNYReKjZRK7S4WLm6qME5GyoehPTr3udfmE2Np8orwpcACpsLvLUcdin/ARhKdUPktgPpi49e+DisELmNvhmIrzTAd7rBr89ASXp/u+pLX83zjeEww1zwBDpfVwfAhO/bnCBi8fj4Vh+OY99t5P+r/7BgFf/4PHvd5LmCvW9vJW+QdR5EQShToiZlzpkrnRwIMfMuysPk1ZYQUpkIA8ObknLCAMBGiUWmxOrzYFeZkWHQ5phkHvHl38eyufmzzd5HWsZYWB05xjeWHbI5/PKZLDmH4OID/GxLGErg/yDWNK2UhY/iAxXEMFaGccKbXz45wnyyqy0jzUxsUc8s/5KY/WhfK/b19wcQsJ3wyGxPyQNAE0gOc3HcTg9hw2ZDmIMcvq1imZdhp3Z647z1fVRhKrsyNR6aZuwLvjsL1rObvh4oJQTc7qQ5nDbIjDW8pZcR6VUhG3XfCg8As0HQYtB554pOpvSDMjdB7l7IKI1RHaEoIa33HKiqIJR/1tbNetySkiAmp9uDCP+64E1bxr4FAx8sm4G2IgVW+y4PR6C9WrR10sQTrqQ92+R81JHbA4Xi3Zl8/TC3VXHMoor+f1AHu9P7kKLCANvLD1IamEFbSN03N0ziCTHFnTxncEYA0gzNh+uPlrjsVUKOXan/906Hg+43L5jVLtCT5qqFe8cl7Pu9zRu6t2MSruTT9emeY1zxb5c3pnYheOFFtJOdsmVyUCm0sPkeVLRsj3fg1xJlL2cqKiO9C/7FLcikX3ld/DUwsP0a27Ck7UDT8YyZKPeAfU5OklbS2H5v2sGLiAVeMvcVrvBi9MGR1bC/Jul/j0gJabqQ2DqEghvdXGPa4qTvlKuvnRjvcQcLjffbjpRI3ABKLLY+fmIi7sTr0SRttr7pF7sNPLH6XKTVWplzaF8vtl0AqfLw9gusYzuHENMUCNcBhWEeiSClzqSV2bjP4v2+jz3zx/38NzodizfL22VPZJXzqK9+Xw6PpGBy/6N/OrnwBiD3emmsLx6OSbapOWm3s1IjgggPFDL/34/4vPxO8aZMOp87wA5nFfOuA/WYXO6pV2wCcFMm7O5xnVOt4c3lx/ilj6J/Ofk8lT/5DAMoVHw5S1QnFZ9ce5ePJHt8Yz/jAKnjqcXpPPaqESuDEgnfNH9oDWB1Xzu4MVWDsfX+j+//xdoM+rsj/F3lOXAgturA5dTKorg5wdg0rdSINMEmSsdrNzvf+v20mNWpqRcQdDpwYtMLs1KCTWUWx2kFVbw9MLd7MoorTr+8pIDfLnxOPPu6kOsCGAE4byJnJc6kldmxerwPTtSUuFAo/TOKXF74Ikl2eQmXw8ZUjBh1CkZkCJ9su0SH8TzY9rz255s7pq7jdUH87m2Q82cCZVCxn+ua09IQM3dPCUVdp5ftA+7y83AVuF8OKUrBeU2v6kdR/PLiQ6SdsyYdCqeGZ5E8J4vvAOXk2S5e5Cnb0SrkvPBmFhuaAERP94ozWboQkB+HnGzTA7aIP/nA6PP/Rh/R/4BaRuwL+kbpSCmiVIp5Bh1/v+MgnRKVI7qej/IZDB2ZoPL22kojhdWsPV4sVfgckpGcSULtmbgcl0GtY4E4RIRwUsdUcjP/lLLfCx755fbKFJFwfa54LCiUiiY0qsZRq2SR4amcP/X26oKxv3v98P0bRHGMyPb0DoqkHCDhhHto/jl/n60jQ70+ZzlVidbjxfz7sQutAg38Omfx9Ceo4m0Xq3k0SEt+OXmBJKzFklLRf7sno+5wsZLq3Ix25xV22hdvR+QklfPxRABve7xf77jjed+jL/D7r/rNuB7OauJMOpU3NHff82Z6f0SCWg7AnrfC0Ofg/s2Q+uR555NuwyVWR2sP1bIiv3+ax39sC2DoooLT+AXhMuVWDaqI+GBGoxaJWZrzTe8KKPWZ24BgEwmk+qDnKzJEh+i55f7+/Hp2lSvmRy3R1p+Sok08PDgFBQKGasP5qNVK1ArfUckMhk8NLglC7ZmsOpQPh+NjaN1oBmFXOYzR6ZjnAk5cFdCNuq5Y6XkTB+1YqqfQMGefAe/7ivk0cE9MY7+H+78Q8hShkiJwlbzya3PkaDwMUa5Qur5c3QlpP3pfe6a12q/pkhUB//nTHHS8ldjV1kMyEAXVONU12bBjO0cw8IdWV7HJ/aIp11sMBj6QVK/uhlnI1Zhd5FXZkPu6xPKSXKZDJG2KwjnTwQvdSQyUMNbN3bmji+2cHpcoFLI+Ne1bXjvj5r5KhGBGkKsGdB9etUxhVyGUadie3qxz+c5lFvOOysPM7x9FF9vOkH7WCOJob4/DQcHqOnWLJjXlx1Cq5LT3lBG8LYv+M/Qe/nn0kyvaw0aJc+PbsPmQ5n0z3hdygI+vAzaXue7bgng6XIzHQNhUqdQigty4McJyLpPRWavgMWPwpEVUi+fHndIJedPJiZ7OdXKoDgVDi2T3mRThkvHNb5nlC6ZgAjoPg22fF7z3DWvg7GWl61qkzkTDq+A7V8AMunnbD7Q688gzKDhmVFtmdoviSV7cpAB17SPINogJ8Tgv5+V4E2nUpBTWsnVbSNr7NY75cYe8T6XdgVB8E0EL3VEqZDTNzmMpQ8PYO6G4xzKLaNDrImJPRPILq3kYG6Z9/VyGW9eG0Nkzteg6gS//0fqG+O0YXLZmXltGO9uVDF/Z2GN54owaig5OZNjsbtqnD9Fr1aSWSzVdQnRq1GWpqHfN49R3cLodMtkZu2sJLPMSZ9YFWNaqsGWjcN12uNlbIG+D0lNFXPPSEZOvAKZ20HMnN48NvQdbKY+4HYg2/QxKDXgcUn5JE6rVKX10BIpAdZXzoQhQvqK73Wer/YlojPBwKchrofUTNGcLbVVGPLcxTWSdNqhIl8K/DRG0NbTVv7STPjqBsg77c8sY7PUIHPi114BTEiAhhClg45dPLBtDnyzQNrCf+UTUp0e0cfonIw6FVN6NSO10ELPpBA2pXrnSrUINzC6U4zYMi0IF0DUeakHDpcbm9OFVqlAqZBjsTk5Xmjh0z+PcSTfQvtIHVO7hxDvTEdbuAe2fwWDn4HfnqwuAqZQU9bzIZboRvKPX71nSd66sTP/XbyPgnI7ix/sR7uYmssbRRY7WSUVpBVWcP/X29GrFay4QUfMgtHSBeoA7C1HYtdHoCvYjeLEerJuWsW0nwpYfGUGip9O5qJoTTDyLSjLhoO/gUwB3adKb9SLZ0DnyZA8BI/GiMxaAgWHpJmMq56RdvKc7uaF0OIq3y9aZYm0xFRfSzWVxVKysVJz7to0vpSkS00Zt38pBWzJQ6VckdCWoKjjzxCbP5P+bHwZ+xF0Oq3RpscDBxbDvCk1r+33CPR7VOpxJJyVudLBb7tzUCikZaTfdufgcLkZ0zmWQa0jiA0WO40EQdR5aeBUCjkqRXWuSIBGSdsYEy+N60ilw4UeK+rNM2HzJ1CeBxO/gh/ulOqenOKyE7j+NQYPjaVjXEt2ZZQik8Ht/ZJILSinoNzOyI7RRJtq/lIssth47/cjtI0x0jrKiF6toMLu4pAtmJjgRGn3kN2Ceu88qiayu9yM3hjG86MjcATGIx//GbL8A7DzG/h+GkS0gXbjoO1oOLhEmikaM1OaUflmIrJT242jO8O1b0rLRWfa6yN4MWfC4ZWwY66UX9P9dqkgXl0s2bjs0izF0T+kAnXN+kqzExcavJRmwtzrqptgAhxeCmlrpNYIF1sv5mJUFMK2L/yf3zoLWo2oDhLLsuHXR31f+9c70nKfCF7OyahTMaZLDPnlNkorHPRtHopOrSTSqEGpEPsmBOFCieClAdGoFGhUCihMhz/+Kx0MTYbi496By2lCNr3Oh9cvYn1uMxJC9fy6O4dFO7N5ZXwHrmod6XMd/UhuOf1ahvPyb/sJ1Kp4eVwHHvtuF08tz2fe9XNJWDYd8g9W39D6WkgegnHfl3SJ6Yril5eRFRyUqtwOeQ7KcsFllQIPcy5s/BD6PgzH/4I9C7yfPHsH/PYPmDRPWjqxnbbd9sygoDQTVvyftLwS0hwOLYUf7oC4njDhi9oNYFxOqfDel+OrO0FveF9aUrlt8YV1f87Y5B24nOKohNWvwKj/gaYud+mcZbLV4/E+XVks1bvxea1b+nvSGDphNwAalYK4YD1xFzFxJwiCNxG8NESnF0XrNvXsfXxKMzCXW8goVtGvZRh3X9mC+wYlEx7oO6HS7nSzM6OEQ7nlHMotB6Sk4Q9v6sqh3DK2lsiJueErFJZsZBWFoAuVljWK05Ar1cjnXlf9YBVFkHE7nuEvQWxvZEXHILw1XP1fPKZ4ZF+M9j3mwqPSjFLfh+CP56uPd5hw2mvgAUs+hCTC8fVSYHPtm1Jy8JrX4MR6aD/uHC/k31CWBd9Mqg5cTjFnwc8Pwo1f+tyhU4PbBXt+8H/+yEqwldZd8KILkXKnsnf6Pt9tqpTrc8q56vE0lk7YDZDN4UKhkKE8RxkFQRBqEsFLQ6QNkoKA5ldKvY3O1vfGGEuG2cnbK4+jVyu5vX9zFGdJ/HO5PbSMDOT10/ogbThWxIZjRQxMCeetvjaUH40Fl0N64zpZy8QzdQmy357w+ZiyFc9J3YS1Jpg7BsrzkE34wn+BN5BmYJKvqg5eBj7tvfW58Ah8fYMU5Jyy/2foeSf0vge2fAYth9bejqPCo2Av930u7U+oKDi/4EUmB32o//Na49m3m19qMhm0ugY2fyoV4TtdVEfp79zpdCEQ2V7qw3QmlR5CkmpvrE1UZnElaw7ls3RvDqGBGm7unUBiaABBerHbSBDOlwheGiJDOIx+Dyx58O1kaQeI1uRz6ai452O8t1l6k/1w9VFGdY7xmedyik6tICRAjc1HL6TxrdUEr7hPClzAqwibzJLnvcRzOqcV5CpY8uRpwcbJ+jT+AhhjjPSGeNWzkDIUTAnVwYDdAr8/7x24nLLpY6mXUu6+v9/l+WysJWc/f+aMjD8yGXS7Vcol8aXnndKW7LpkioWbfpDybrbNBRnSjEvykJrb1Q3hUhLvrBHef/5yBYz/TFTUvUAniiqYMHM9OebqfxcLtmbwyJCWTL0iyW8bD0EQvIn5yoYquqNURwWk/JdxH0NwYvV5hRrHFY+x0t2VnSdLjhdXOM7aoPGU4AA1HeNq7tppZXRJu4F8OdfsgFLj/Ul+/8/+K+CGND/ZrTkLBjwqfeI/fRajoggOLPL/XMfXwxUP1u5W44i2/s8FhF/YrqegRBjgY9aqWV/ocH2NzuF1whQr1Xa5aYEUyPirswPSa3H3Whj+spT/1G8G3LtB6mOkELMF56vC5uSNZQe9ApdT3lpxmFwfxwVB8E3MvDRYsuqZh9y9sOQpuOIhMERKMyJKLYdJ4rHPD1fdEW7QoFae+40wPljPMyPbcuNH60mOMJASGYi50oHsbHUmKoulDs7mzJrn9KE1y8Lv/UHabeS0STtW5ErI2gameBjzoZQLYoiCkgwfy2Ie6bw/Hre0a6k2GSKg/fW+2x8MexEMF5AsrA+GPvdCu+ukBGZrmfT/oS0hMPLSjflinE9jSbkcgptJy3U97zi/vlRCDcUVdhbvyvZ7fsX+PFpGip1bgnA+xG+hhkqplnrFHPxV+r7oGCx6pOq0o9VoftQ+6HXLg4OTiQzUntfDd4g1sezh/qw5XMDmtGKahQYQFBIszYLk7Kp5w8aZeMZ+hOyr672XghQq6RO5tcwrRwa3Swq6ukyBw8ule04FX2VZsPSfMOYDWPKEFAwEN6t+TI0Rmg+CY3/4Hny7MbVfHE0XDMNfkorSrXsHLAUQlgJXvwAJvS98tkQXJH1dTHG7hkQELhfNg9Sd3Z9Ke9PtlSUIl5r4TdSQJQ3wPduhUFPccwZfzZEavWmUcu4a0IJrOkSfd5XOnFIrkz/dRF6ZrerYyv25rJj0FvqvR9doSujsMpWygCT0t69GdWAh8qxt0nJCUn9Y/4E0g9DtNikRFKD/Y1LeyJxR1Q+y6WNIGgjDnpd2Ddkr4NBvUuJtt9ukazweKVDoN0Pq3Oyo8B54+/EQ1Iw6YYiAPvdJSzsnZ7sw1HF+SiPm8XjINduwOV2oFHIiAzUoLuOaJoFaFX1bhLLuaM2q2ABD2tTzLNwlZnO4QAYaP73VBOHvEBV2G7riNFj5AuxbKL2BxveGEa9QamxJrsWN1eEiWK8mPFCDVnV+vyTKrA4e+nYHvx+omRDbPSGQb8ZHoNj5DfL09biM8Th63s3P6XoeX5SGTAZXtAjlg1FRGA//BDGdpdmTvP1Sv5/KYqki8NX/gfm3+B7A8FekJaPgBKljdtZ2uGG2lNNTfBx+e0KapVGopGq8aX9Ku1763CvNyIgAosErsthYvi+XN5cfItdsw6RTMb1/EhN7xBN+nrODjVlBuQ2ny0OgVkmApvoz4v5sM2Pe/6tGwvzwdpG8OK4DIQGNv2dUrtnKroxSvt54ArkcburVjHaxRiIugz934e+5kPdvEbw0BjYLVBZJ/YA0xvPLUziLE4UWrnx9ld/NOq+Na0dhmZWBLQxklrv5dmsey/fnel3Ts5mRuWPC0Kz8l5S4aYqXknoriiGqAwUlpTjsVnRFBwjaMVPKezkltAVcPxu+mQgJfaS6I8GJEJQA6RtAqYOSNGkrrr0CynPBXgaJA6ROz2pRW6QhsztdzP4rjRd/O1Dj3A3d4nhmZNsmu6smv8zGmkP5zFx9lCKLnZ5JITw8pCVJYQGolQqcLjdphRV8vPooa44UEBmo4dlRbVEr5GSXWokwaog26Yg0Ns43+lyzlfu/2sbm496NY/snh/HGhE5ENNKfS6gboj1AU6MJuLgiZhWF0kK7PkTasos0lV/pcJ11l3Gm2c7SvfkkRppQK9U1AheATcfNFJfpiDqyQuoOfVLR0HdYm6nj3bVmskqgTVR3nhi6gDapswnc/rF0kdUsLSkZIqSE2AOL4PZlUoCz+jU49nv1E8X3lJoAfn+7NKNz0w8QKiq6NmR5ZTbeXnnY57nvt2Vw78AWTTJ4KbbYeWHRPn7amVV17Lc9OazYn8t3d/elc3wQdpcbtVLO2K6x3NA9nvBADQdzy9ibVcrSPbkczC0jPkTHnKk9aR7uo4VGA/fnofwagQvAn0cK2Hq8mBEdGnEndqFBuXwXoJsyc5a03DJ3rNRTZ+NMqdQ+cCCnjI2pRaRE+v/F2C7GREZxBXKZDKvD964fjVKOp9L7l1R5t3v4KK8tD/5wlCN55VTYXWw9UcqEr9NYE3kL7tie0oVJAyBnjzQDA+C04ln6NBz93TtwAUjfBKtelpaRilOlGSihQSutdFDhp5u5xwMZJzuZNzU5ZqtX4HKKw+Xh2Z/2kF1ayRfr0hj8xiomfbKRGz5az+j31lJpd1Fa4WB8tzhev6EjeWYb02ZvpqhcyhdynyXJtyEprrDzxYbjfs/PWZ9GmdVRhyMSmjIRvDQ15iz4aoK0Myl7J+TslorHzb2OnMISps7azGdrU3lwcEt85fYObRvJvmwz5TYnSWEBfvNowgxqTOHeW5wLUibyyaZ8n9f/34pccns8Ji0FdbkJzBlSPZeTZGlr/ddOydhcvUvnjERioeHRniNB06BtmhO+6/0k4oLUxX1vlpmXlxzE4aoORsxWJ499t5NBrSN4ZckBftqRxYdTuvLq+I7MWX+cu+Zu5Y3lBzleaPH7QaKhcLs9OFz+60zZnW4aSRwmNAJN87fI5ezYasjdXfN4cRo5ZltVgayfdmTx8S3dmfVXKttPlBBm0DC9XxJXtAxj3sY0nh/dDoWjHI9bRp/moaw/Vv2L+a4BSUzsGoHTVYhnyvfIbGVw6DdSzTK/v5zyy22UGFoTfftyOL4O2o6RZoROkSs4a8NAW5mUtHuhHZ2FOhccoKZzfBA70ku8jseYtMQE6Yg2Nc28B63K/2fB8V3jeNfPUprT7eGPg/m8NLY9oQYNJ4orcLk9dG8WTNsYad1/xf5cWkUG0iLcQHSQ/wra9SlYr2ZM51j2Z9fMdQLpNTA1weXChiqjqIJyuxOny4NBoyAxrPEtQ56NCF6aEqsZts32fS6mGyXl1duOl+/LZUtaEdd3i2dsl1jMlU5aRweyaGcWd/SNIdSRi7zkAM0MIfQYG8u/f9ewcHsWozpGM62THuPG/6Lb/ZVUhE5tgJ53og0MBjL8Dk+lUsPP08HtkGZQbvwSfnkIyvNwp1yLPHWN/59NEwj9HpGq2zYQFpuTgnIbeWU2dCoFYQYNkUYNMtn5bVdvqkIC1Lx1YycmfbyRHLOVq1NMPNrXRHDJPoIVlSitJlBF/+3E84amT4swZDLfXSvaxhj5Yn2a33tPFFbQIjyA9/84wvT+zQk3qAkxaJgxb0dVBW2ASKOGubf3IqUBFrOTy2WM7BTD3A3HaywNJobquaq12CVYV/ZlmXnmpz1sPZl/lBCi5/9GtaVNlJHo4JrBb1qBhdQCC2mFFlIiA4kL1tEstGaepdPpJL3ESqVdyt0y6VR+mwDXNhG8NDX+yvi3HU28zuZ1qLjCwSd/HgNAp1Lw4U1d2Zaax30t8pEvehAKj6AAjKEteH3U+8wY2JsAhRPDskdRH/ql+oHs5bD2TeJvm0yAWoHltHwHhVxGywgDLcIDCM5YKVXZBWk5K6Q5jP4f/HQ/5f3+iW79G/j6XOZJ7IcluA2Ed8ZgbBi1MArLbXy05hifrU3FdXK6KdKo4ZNbutMuxnTW5piXg6QwAwvv7UuxuZSk4g3ovhtT3TMLoN1YGPGKVLSwiQgP1PDsyLY898s+r+NhBjVtogJJiQxkY6rvnK0OcUbsTjeTeyXw75/3MrpTDKkFFq/ABSDXbOO2zzfxw71XENUAZ7Big3TMu7MP87ak88O2DOQyGTd0i2N8t7gGO2PU1BzLL2fypxsoqaj+93aiqILbv9jCd3f1qRG87Msq5bZZm71qfjUPC+CTW7rTIqJ6tia9yMK6o0W8ufwguWYbaoWc0Z1jePCqZBJ8BDq1TeS8NCVao9SvxpfAaEKP/8aINr4/7d7TL5YD2WZeHxKE8ssxUlfnUwqPopg7inhHKlp7kXfgcprIVY/xv4kdq9647+4Vxu+3xvB15z28k7iB0IhYuOLhqp1PFB2DrJ14bv6R17Y42NP2ERytRlefB1zNB3Gwz+v0eW8fj/xygiN5ZVXBQn3xeDws3ZvDx2uOVY2lRbiBf17TFpvDRXqR5bJLTJRqe5Sw5lA+R/PKKa2wEx2ko62+HN2PU70DF4C9C6VWCU2oUoNBo+T6rnEsfqAfk3omMKhVBC+Mac+P911BQmgA/xjWyud9OpWCgSkRRJu0PPbdLvLLbPRuHsIfB300JgWySq1klzbcpOfYYB0PXpXMD/f25fu7+3DvoGRiROBSZ34/kOcVuJzi8cBbKw6RWVQ9A59WaOGuL7d6BS4AxwosPLFgF5nF0rVOp5O1Rwp5YsEucs3StXaXm++3ZvDQtzs4Xlj3uYhi5qWhc9mhLBcqCqTOzQFhZ+/k26wfxPWEjE1ehz3WUoKO/Mj/9e1ErDGSr7YVUOlwEaxXcX/fCEYmK0ClJHTbZ747JrscsPNb1CnX+H1qVdoqug8tZ/n0FIoqnbTLXIDum1e9L+o0SSqxv/Sf0ve750Ob0YzuHMktszczd/LzdOp5Oy6Fhiy7niWpLt756jgWu4vl+/JYd6SQxQ/2JzGs7iP9U/LKbLxzWv7Cw4NbMrRtJKsO5bEptZC2MUbig/UkhgUQH9L0a9Icyilj2pzNXksFw9tF8eK49oTs+1HqReXLX+9Cu3FNqjN1oE5Fu1gTL4xpj8vt8eo11ioqkHcndubfP++l+OSbS0KInpfHdSC7tJLZ644TbtCQX27D7vScNa7LP+PNpqFRKuSiKF09sNicbPIzuwewO7MU22lJ1UUWO+lFvgPhLceLMVudxAIniq28tdx3097t6SXkmW0+l5lqkwheGrLKEtj3Eyz7p5SwClIxt+tnST2IFD7++IzRMOELPMfXItv8GXjcFLaahCJhKE5FOJELr+fxVmOYOuFWrDItOqeZyP2vIku+H/mBBZC5xf940jeg6DjhrEPWyh009+TRXG6Bda/WvGDnN5DwjlSQruSEtMx14i+iIwbyr2vbEOPJgpX/4asWr/Pv5TXzZyx2F5+tPca/Rratt7LjDpe76tPHkDYRdIwzcevnm7i2Uwyto40UWRws3nWUewclo1TIiDY13U+d2SWVTPl0I/nl3m+mS/bmcGVKOBOLUvG7gFaee/YGnI2YQi6rsXQYqFVxbccYuieGcKKoggC1kkqHkwq7i+SIQEZ1ikaGjLhgHTq1HJ1KQaWfHUbNQpt+UCxcuACN8qyzXJGBWuSnzWyXVPj4oHqaCpvUb8vqcNWYnTndrswSeiTVbQ6bWDZqyDK3wS8PVgcuILULmDNS2mrsjzGavISRvBf9Im9HvshNW1uwuUDFt9lRFF/1KurUFcQuGE2L768m5q9/Iu95J/LfHpNmd0xndng+/XFjkdnMUq6KD+5mV+AuzZS2aO+a5/9xdnwtfeIOTqJw+AekmXrhkqkYmBxEka45mWMXsOSo/39UfxzMp9THtGhdUSvkxJ1cN35wcEvMVidzp/fiypQwth4vIrO4gtuuSKTS7pSmaB3Wczxi43WswFIjcDll1rpUXC0G+785thuomm5g54tCLiMmSMeWtCI++0sK7HZllDDinTU8+9NenvlpD3d8sYVtJ0p49foOPh+jd/MQDBrxuVPw7YbucfjbM3B7vySvWeuzBToapRyTXg2AWqlAeZY8vvqoCF0nwcv7779PYmIiWq2WXr16sWnTprNe/91339G6dWu0Wi0dOnTg119/rYthNiwVhfD7f3yfs1vgwOKz3l5c6eD11dm8vTaX/dll/HEgj2PlSh4+0JZt1ywi/Yal2O78C9v4L5D98oDUAXrfT9B5iv8H7TwZlv8brnkNjDHe58JSKB36FtrNM6VP1OU1q/JW/2wFOKM6s3fkjzzwu4tBn59g4Id7+McP+/CotHy9NQejzv8vZ5NOhbIeG/xFGLU8PqwVk3vGU2Z18vlfqYx4508e+mYHMUYND3TXMeDo6/Rw76KVoQIKDoGzaebApBdV+D13KLccZ3S3mn9XQMpruvqFJrfj6HwNbhPJhqOF2Jxu3lx+2KvEgNPt4dUlBwnWq3ngqmQC1NIMo1IuY2THaG7v15x/LtxNQQNfOhLqR5BexWvXd0Sl8A42buwezxXJYV7HTFoVw9r5Tpq/pXczwg1S8GLUKrmmve/qyAFqBW2i674VT62H7/PmzWPGjBnMnDmTXr168fbbbzNs2DAOHjxIRETNrXPr1q1j0qRJvPTSS4wcOZKvv/6aMWPGsG3bNtq3b1/bw204HDbIP+j/fPpG6HUPyH2/iWuVCq9tm/O2pPP6DZ3YeKyQifMy+cfwVmzcXMg7nU6gKTkhXVRZLDVBHPYiLH9WagQJIFfC0P/AiQ1Sh2uZHMZ9AuV50gxQeGvcQYmsOCZnnEKLIncvxPeSxgig0lHRbhJFyeNAoSHCpEduNxOVupg3u8SS27cFT60sYs3hQvZ+upF5d/RgX66FpXt9B0DT+ycREqC+mFf1b8kzW3HZygn2lHJNcAHh7Zsx+bONVa9xmc3J+6tT2ZJm5P0uXQn7/gY8MV2kN+nyHAiKr/Mx17bkCP+1I4xaJSXKSKJuWwy/PS61kfB4pJm7Ea9B5GX07/kkt9vDiaIKUvPLeWJEaz5Zc8zvtXM3HOfG7nF8f08f3G6pgu/6YwX8uD0TlVJBRnEFYfW0TVVouGKD9PRLht8eGsDerFIq7C66xAcRoFHWyMGLDtLxr2vbEhGo5but6VgdboxaJVOvSOSG7vFVMy8RRi2PDkvhaEE5e7PMVffr1Qo+uaU7YYa6nwms9caMvXr1okePHrz33nsAuN1u4uPjeeCBB3jyySdrXH/jjTdisVhYtGhR1bHevXvTuXNnZs6cWeP6MzWZxoyWAvhitDQj4svg/4P+j/i9vczqYMb8HSzfV71jQSGXcV3nGK5tH02YUcPEjzawcoKGmAVjvG/ucQeeHtMhaxsyTSDoQ6XchK8nwJgPYNVLUmB1qkmkJR80Ro6P+RG1JZPoheNhyvdSPyKlhozrvuPNzVZ+P1zC/EnxJK+4HXneadtJtSZyrvuWqUvt7M+x8MKIRAa1DOJ/6wv5dnO619CGto3k+evaEVXHeSQHc8o4mHqcQeYfCdz4FgWD32TKpiQO5pb5vH7hlAS6LLoGbGbocz90nAjRvpcBGgOny01BuQ2PR6qQG6iVNrXnma1M+mQDR/Nr7jZ4fFgr7hzQXJols5ql2US3U6rZ04SSdC/Ekbxynvh+Jw8NSSHMoObheTs4lFvu89oOsSamXZFIqEFDelEFvVuEciSvnGV7c1DKZYzuHEvrqEBCDSKAEf6e0ko7BWU2Kh1udGoF0UYteh9Lk8cLLWSXWtmRXkK0SUuHWBNBeiUhAf/f3n2HR1WlDxz/Tm+ZmfTeAyT0XqWpKMUCikhTwAL28tN1xdXVVde6rmVV7IK9UqyASO+9EwiEhPReJslMpt7fHwOBIROKAiHxfJ4nzyP3njs5czPOvHPK+56baaOLpjCjw+Fg69atPPbYYw3H5HI5w4YNY/369X6vWb9+PQ899JDPseHDh7NgwQK/7e12O3b78eFTi8Xit12LYwiFSx+Hryc1PqfUQMfRp7zcqFXxr2s7UVKzlb35FlweCbdHYuWBUu4ckkJOuRWb002WM4RoY5Rv1efNHyAr2ol99Ieof7wLWc5a6DAaBj4E1XnHR4TsFu8PgKOO4IPfsSHsRowDHyegZD/cPI9Cq5yJ88vJrbBx3yURJG162jdwAaivJvLHSTwz4kfGfVXH8mwrNyQ7SQ41MveuASzeW0jncA39o+TYXG4sddYLGrwUVNl4bN4u3u9VgHH9fwCoM7fjQHHTq/rX5drpHtrGu25p51fQdcKF6u45V1Rdz5cbj/DZhiPU2l0MahPKoyPTSAozEG7SMueWPjzy/U42HPbeD41Szu2DkrimazSlNXZCjRpUWpN3K//ZsNd4S0hIHm+gbAg5D8/uwqmpd/LRmsPcfWkb7vliG5emhdMtLrDJ4KVTjIlFe4tYvLeYeXcN4MkFe1h7QgmCb7bkMbpbNI+OSBNbkYU/xaxTY9adfjQ7IcRAQoiBfsnN///ieQ1eysrKcLvdRET4zqlFRESwf7//FNJFRUV+2xcVFflt/8ILL/D000+fmw5fbOL7w+VPeUc6jm1fNoTCVa+B2+Udgm9qZZbDSoxUyrxR4CYYizqMQreZEJOBKLO2oU7K40vL+XL0F0T/NAlqjt9jSWVAXZPjDVzAW/m5/z2w7N/+f19gPLaovoQHhbAzYDKRqjpC8zezXdmH3Apvsbqr26hRfbvY//W2SmKlIowaJTEmNeqyvUzsOpwKl5q7+oRgWv8iit++AbkKa8fxuLT3ogyOP+tb+kfszq/mxg46Qre82nBMgRu1Qo6jiVouoXo5OI6uB7HXeGs6tUDFlnqmf7qZ3fnHvxQsO1DKmkPl/HjvJaRFmYgL1vPeTT0pr3NQZ3ehUsqZtzWPQS8vJ0CjZEr/BKb0Tzy7pGoVh2HxE5Cx0Bu8RHWFUf+FyM6gaplbcC31LjpEmXn6p33U2F0s3lvEvLsHsGB7QaPXkUYpZ2SnKG6ds5m0SCNbjlT6BC7H/LCjgGu7RlNSYyMxJIBA/YWfThWE5tDil6w/9thjPiM1FouFuLhWsrZAH+wNYG78xLtjRaECpxVWv+LddXTHaghKaHydtRK2fwbLnkHhdqIAQrVmQsd+BIZBIJMRadKSGKInu9zK5B9lPH/5dyQoylDUFqIMa4spQIcyY+Hxx/S4Yee33j6cLCiJgyO/4rYFReRUbGw4fHXHNkzqH0iMWce9l7chxlB1yqRkClsZAdoIJvaKRl6XT1G1lSvf3cDglGAeGzSD5JIM1Llr0G99Dw4vhmm/gjnmD9/eM7WvwELvYAVUHa+YG3LwO0Z3msx3OxsXopTLoF+UAlYcDdCTL2266ORFLqO4xidwOcbh9vDiwv28Oak7Rq0Ks16NWa9mZUYpUz8+viC/1u5i1opMtuVU8takHoSeyRRHVS58PMJ30XfhTpg9AmasgsiO5+KpNYtIs4acCitalZy7hrZBp1LwxsRuvLI4g8xS7whMu4gA/nZlKm8uO4jLIzG8YyQ/7Mhv8jE/35DDDT1j2J5TzYQ+8eiaKKYqCK3Jed2yERoaikKhoLjYd+FlcXExkZH+57wjIyPPqr1Go8FkMvn8tBq1Jd6t0l9NhHm3wzc3wbwZ3jfy+mrv4lp/8rfAkn/6ZjWtr4avxkO1d3FuuEnL7Ft6kxxqIKusjonf5HLF9w6ezUrDGpCIpDaD6aTA4OAiaN94uqpw6Cvc9H0+OSftPPl5bymL9hbyzk09eHdlJvsqOWVhRXdIOx4YEEZs1UbKw/viVJnolRDEykMVjP4sm+xBr3inD8AbvGUuP90dPCfaRxk5WOmCsLSGY7q9X/NgDyUpYb6JmWQyeP3aOMK3veY9oNTA5U96R8xaoIW7/Y94Aqw6WEpNvavh3yU19Tz9o/81WhsOV5B/Ur2bKquD3AoreZXWhnwSgPfv6m+3mscFK57zTR3QTOrsTgqrbBRW23C4zixXTZBOhUohR6OU8+bE7pTX2qmzu9iaVcl7N/Xg5/sG8u5NPbmxVxzP/rKPzdneujQapRyro+nfUedwEWnS8cKv+8UOJOEv47wGL2q1mp49e7J06dKGYx6Ph6VLl9K/f3+/1/Tv39+nPcCSJUuabN+quR3ebbbgP5lX7ubGx6wVsOJ5/4/nccP2LxpGP5JCA/h6Rj9+vm8gn97ah/n3XMJ9l7clI68I2U/3IdMH+ebhqM4DyQ0n5u7QBpLtCW9I2nayb7fkUVJr50i5lbe31FHd929+20lJQ1CZo+geH4isroz/LM/nzs+30Tc5hFmTe6CQy/jv+mpqu0w9ftGe77zbxs+zzrFmvt1TR0nf42u3cNUT88NYvri8nk/GxXPbJQn8Y2Q7lt7ZmWEF76PL+AHaXgHTl0Nou/Pex/MlSN90FeAAjdJn1rLO7uZwWdN/j83Z3jUxDrebPfnV3P7JFga9vJyh/1nBzLm7vMGv2wkZp0iNcGRdswYvLreHQyU1PPTtLgb/ZzlXvLqKlxYdoKDq9On69Rol8cF6JvWJY8m+YoamhjP1402M7RXLo3N343R5eHFhOv/+Jd0n6+m2nEoGtmk6+B3cNpQauxOH29MoQBSE1uq8J8t46KGH+OCDD/jkk09IT0/nrrvuoq6ujltuuQWAKVOm+CzofeCBB1i0aBH//e9/2b9/P//617/YsmUL99577/nu6sVHroKgpKbPR/rZauqyQ+WRxsePKdnrk/4/3KQlOdRAYoiemnoXHg8MNpeiyl4Ba9+A0bN812ssfBSp+00w8RtIHQkdriXH1vQahHqnB7vTO5+/8mAFP3oGUHn5K8erQyu10P1m7CNf55I3dzH8vd0sVw/B4XKTU2HlrWWHeG9lJk9e04FVh6qoiR54/MHVAd5t3OdZTKCe18d14IeiUCqGv3V89MhaQeTqxxkSWMIdPY3syLVQZFeR1+U+au7aAWM/hoiOoGy56xCu7eYnR8tRk/rGExpw/Lkp5bJTJrI6tr09u7SW62etY8vRircuj8SPuwq58d31FFicYDpFokRD6AX5mzflSLmVa99ay+K9RTjdErV2Fx+tyeKmDzdSeAYBTEygjmu7xRAaoOGDVYepd3nIrbCyNaeSJelFPDu6U6PMvMv2lzC2R4zfQDI2SEe/5BC+2uQdUVUqmr7/gtCanPd3gfHjx1NaWsqTTz5JUVER3bp1Y9GiRQ2LcnNycpCfkKtkwIABfPnllzzxxBP84x//oG3btixYsOCvlePlGGMEDJkJC+5ofE6l836z93c8vD1kr/H/mLF9QXH8A6es1s5Hq7P4YPVhXB6JMV0jeVXxqfdkzgbvYsnr34e6UjxOOxUhPTCHRKDK/N37Ia7UkhTR9HoOo0aJ23N8MeI/fyvk56QO3HfZXML1EBdqxGUp42+/FGLUKukfY2bB7jKu7x7DvO3ehb4786q5qZ+M5DADcucJxer63umdlrkAUo0ugoJzqAnph3rKb2ic1ShcNuSl+2DzxywOf5hf9xTx654iXh/fjTe3FPHs6CgCL0jvzp8os45HR6Ty0iLfnEPto4xMHZCISnF8fUVIgJprukYzf/vx9RlymXd7+9VdokiLNFFcXc/rvx/0u9C5yFLP2kOljOt7B2z+wH+HBjwAAY3zQ10IVoeLt5Yf8juFc7isjq05lVx9ml0/GpUClUJOpxgzs1ZkkhZpZHe+t3L0rBWHmdw3ga9n9OPD1YfZk28hwqRlQp849hRYeGNCd+Zuy+P3fcUoFDJGdYpidLdo6p1ufk8vIUCjJOoirDQtCOfDBfkKc++99zY5crJixYpGx8aNG8e4cePOc69aiLbDYNDD3lGQY0njAsJh/Bf+v6HqAuGyf8LHwxufU+mg8w0NO5QkSWLLkQr2F9UQHahrWLPis6Y2d5N3rY0xEnn76whscyXKioPeJHSSBzbMIqrXTJJDDX6nDG4flMiPOwt8juVV2imVJ1Jik1ib4SQhOJ6/jTJQUFXPr7sL0WsUxATqmNI/gU/Xe0eRVh8s476B0YTu8m5V9vSYgiOoHXuyK5i3LQ+PBGN7xJIUZjizRaFnKyCc8MgY+HSI71SVMZKca7/jjW+Or9F4Z0Um43vHUu9soiBhC2LSqZjcN4FLU8OZvyOfyjoHozpH0T7K1CgluF6t5G9XtmNXXhWZpXWoFXJeHd+VzdmVzJy7mzqHm1fGdWH94aa3mC/aV8LoTh1Qj3oFFj7i+2LseD208/O6vkAsNmeTlZ4BftpZwIiOkafN/mzWeUdQVAoZtXaXT8LFLzbmcFXnSIINaqZdkkhlnYM3fj9IfpUNjVLOQ1e0Y/qgZMprHchlkF9l45mf9iFJEi/d0IXwZkjTLgjNocXvNmr1DKHe/Crdb/bmYlHpvMFLQFST2XUJ74j7+o9RLPqbdw0MQFASZcNn4VGEEw7gtOGuymdA0Q9cGniE6g4DydN34MnllZQMmkhU+gLfx0y+FJIHoSw/4M3tYsmH0LZw/QdEuouZMzqYh39XsflIFeBdZHhLn3DGpbjZln184XC4UcPz13Xi0bm7KbJ4a/5EmDQkhhj4+4hU1mWWkVtpY/babO4ckswtlyQye202GpWcvjEq5LkJ2IcsYnFhAEGlcm7+6Hi+oK835zK8YwT/HtOJsHNd0VYuh6hucNd6yNmAvSidquAu5Gnb8cC8Espqj0/FHSiuISk0wKeicEtm0qkw6VQ8dgYpwGOC9HwxvR8ZRTU4XB6+2pTD0v3HP/DLax0E6dUNVZVPFq5XoFTrvNXHUy7zLkp31EHSYDBGN2uuF7lMRoBGSVUTfTfrVD5F707kdHuosjqQy2SEmzTYj7gY3jGSn3cVkhhiQKOUY3d5mLUik47RJnQqBc/9ku7zGFqVgkFtQzGo5Zh1er7dnMeWnEpGdIpiZKdI2oYHoGrGshmCcCGd9wy7F1qrybD7J5Ra6lm+v4ghUW60zipqnRK7K5W8vKaKCJOGDyZ3ISBvFXwz2XchsCmG7Gu+5UAlXF7/O8qoDiBTgi4QuyoQZXk6ivnTj488yGTQ63aktKuQrX+LqktfJJ8wbE4P4XIL4ZtfRrt/HkXXz+PFbUp+2lvG46Pa8/XmHDKKaxmYbOYfgwIJqdmPwlaBK7IbmuA47pyXzcajZd0/mNKTe7/czme39SXerMYpSewvqkPCu8bi6Z/3caTcd5fTuzf1YEQTdTjOlUV7Cnnmp30UVDcuumjSKvlqRj86RrfM7dHnyoGiGoa/vsrnWOcYMyM6RfKfxf5LX/w8owudki/OVAcej8RHa7MaBRXH/HDPJXSNC2x0PLfCytebc/llVwEapYKb+ycwNDWMilo7z/6SzswRaRRU1/PwtzsbptP+dmUqKWEGvt2SS0Wdg6Gp4YzuFs3C3QW8vzqbF67vzDebc4kL0lFaa6dztJlbByb5zYoqCC3FRZNhV7jw6p1uCqrrWbi3lJcWVxFu0nBjL++HQVZ5PofL6lBZS+C7qY13MFnyiV3/L+z9n4esyqPZfWUUT12L0mUj5Lubfa+RJO/ahMjOMGQmgYvvR3fNLFyOcgxrX4ED3jwxkXPH8FzXW/m/m8ZiCTDyzM+1DG1j5j+9awmbOxZcxwMAT/JlvHfd/7jyg1pKahysPFDKI8NTyau04vJ48HjALcGKAyUE6lQ8Pqo9Mz7b6vM0PlydxYCUUEy6pnfK/FlpkSaKm9iWOrFvPDEi4ymZJY0zx+7Or2bagESGtgtjRYZvjpzHLo0iznz+/mZ/llwu49qu0fy2t6hhG/Mxdw1JJj6kcSLC3Aor189a51N5+4kFe+iZEMRbk7rz0tgujHt3PamRRt65qQcHS2opr3UQG6QjLcrIc2M6UVhdz7bcSr7ZnEu/5BCu6x6Dy+1he04lZbV2Hri8Lb0Tg0XgIvyliFd7K7Mrr4qJH2zEfbRMbXmdg6d/2sd13WO4fWAyP+7MR16y27sryQ+lq5Y25ctRrHsdgKpB/+LzdDd3SHP9b9cGZBve9mY/7XsHmg8Goonp6d1lc4zLjmHPF8g8Tso7PAjA44MCCfv2Op+dTwDyw8tQb/uQx0bcyv99t4dau4trukTx3qrDPD5/DzanN7PtNV2j6BIXSGG1jT5JwWzKOr6Oos7hwuU59+tNauqd2BxutCoFkWYt793ck0e/30m3+CBkwObsSjrFmJjWP1FkOoUmi2fOnLeLh69I5Z7L2rDyQAkmjZzLop1E6OUYzU3nAboYRJi0vD25B5kltfy4s4AAjYrrukcTHahr9Dd3uNzMWZflE7gcs/VIJTnlVjJLaymvc7Aus5x1meWkhBkw61R8vzWXDlEmBqSE8J/fMhque3/1YV4a24VL2oSy+P8Go1HICRa1jYS/IBG8tCKlNXYem7enIXA50fzt+Xw8rTerMkqR1xf6ufqobpNQnFACoDx2GNlbrQQoDzV9TVWud6Hwl+PAafOuU7jkfu+ojNtJbZdbyEqbwbtba5iAlvZRRoKKNzQKXI7Rb/+YwVOnAXB1lyg+35jDx2uzG8473B7mbsunos5Jr8QgbhuY6BO8jOoUdUZ1Os5Ubb2TgyW1vLH0IBlFNSSGGnhwWFs6RBl556ZezN+ej9vj4Z3JPUgI0RP1Fxt1sTpcWGxOkKDe5SGjuAaNSk58kJ6RnSJZuMeb6M6kU3JDj1h6JAQRoFFSUGVjYJtQqmrraROlBH0oKC7+t6Rwo5Zwo5b+KadOPFhpdfLjzqb/X9uVV8WWo2vEjjmxwOWO3CpGd/dNFClJMHPuLpY9PJTEUN8EiYLwV3Lxv1MIZ8xS72xIMe7PgSILY3vGIIs9xZuuPvR4dlO1gcJaD9nlNio69yN43w9+L5HaXoGscJc3cAHvLqR1b8GYd3Hs/YllUbdy/6dZAJgNeq7pGo2mdqnfxwLAbkEjd7N4WiIp5nL6Bam5pn0vsiwecitsfLr+CDkVVpYfKOHuocmEGzUNCx5DA9Rc1z2mUa6MP8rl9rAio5R7v9zecKygup4rO0Yye212wwczeBPyXdEhnOev63zuFwxfhFxuT0MunvbRJgqqbHyyLptjsbNGKefFsZ0xqBVklNTy8BXtmL02mznrslEpvLV77hqawppDZYzomnbqX9ZCnZj3ZmCbUMb2jEWtkCOXe5NsldU5SAo1MG1AIhEmLRISlXVOPlmXfXSRb+PFwR4JNhwuF8GL8JcmgpdWRNFUkcajgvRqrugQgVxeBx3GwL4Fjdo4AqJRB0R4AxinjUCdgt351ZReMZhgbSDUVzW6xtn/QVSHl+Lz2w8vxx2YSMFlb6Auqee9m8NZlVHK/O15vHh9Fwi7BDa97r+joW1Ru2pJnTsSnFYCFCri24+jrv39vLm9jMdGpfHlxhxWHyzDUVNG3IFPWHbzWLZUBdC9TSyxweeuCGJJjZ3H5+/xORYbpEOtkPsELscs2VfCdd0rGdX5/C4Yvhhkl1u59q01hAZo6JUYzOwTRscA7C4PD327kwV3X4JCBje+v6EhR4rd5WHBjnw2Z1fwwZSezdD78y/UoGZ8rzheX3qQmSPTsDvdPP3TXqqsTlQKGaO7xXDPpW3oFR/M8wvTyTqaaiA2SMffh6cik8l4Z0Wm38eutPoftRSEvwqxr64VCdSr6JsU7PecQi6jf0oIIQEab8HHUS/Dlc8dT/gV1Y2SsXM5bDcj9T+ak0fyEG49RHywnvsXllNw3Tzv4lwAcyyWof+m+LYtZMvjkGJ7+fy+ysHP8K35Fsa+t5k7P9/K3V9so7zOwduTevLED3uoNbWF4GS/fXVd/izqXx/0FqEEcDvR7vmSjlv/ydgOBu7/aju3D0xCp1IQqHIi3/gOMV9dxrXqLcQbz22G0Yo6B9U232+/w9pH8NOugiau8C4YPvma1qbO7uK/vx3A6nBzXfeYhgyvJ5MkmLs1j/WHy/0md8uvsrEzr/p8d7dZKBRybuwdx/hesdgcbl77/WDDSIrTLfH91jwenbuLIxV1DYELQF6ljf/7didxQTqf4ycacJopK0Fo7UTw0ooE6tU8d11nAv2kEX/+uk6EGU9Y2BcQAf3uxjV9JZY7d/Bzl7e4dbkGc20mMoUK+t0NChXhq5/g4zERWOpdXD+3im/bv0n27eksvfI3bk3vybiv8pi1Oo86c1uko4ULPXH9+U05lMcW5lFe5/2G6PZILNpTxH8WH+Dvw9O4es4hyq//Fk/atSA7+jI0x+K+4ROURduhYPvJTwH14SVcFifD5ZH4enMuUwckEFZzwDtNBch+uh/qmk4i9kf4m33SquS+hQRPYnW4cPnJINuaWGxOlqZ773WwQU1hddOp8Y9UWClqovYVwKI9RdQ7m76fLVl0oI47h6Ywe22W3/ObsipICNGjPik/i9sj8fHabK7u0ngEb2CbUGKC/lrrqgThZGLaqJVJCTPw830DWbKvmFUZZcQGabm5XyJJulpUxdugeC+Y4yAsFcyx5LvMbMyr4JlFBVzTKYTwfe/CkdUwdjZ0vwnqq2mjdjJ3eg/2lzpQK+V8uauM91cdbvidORVW1h4qY9n0r9Euf5Ky1Em88kup3/7tK7QQqFNhd3kY/XkuP93xOu4BT+By2smzKojWuoleOdXvtQDq2gL0Ku9U1iOXxhL+zT+Pn/S4vEFPUOK5up0EGzREmDQ+hSe351RxSZvQJkcMRnSKJPA8btO+GMhkMrQqOQ63h+zyOjpEmVh1sMxv254JQRw+xVqsIL0KZVMJF1sBp0vCUt90cHak3EpogLpRzqA9+dW8c1MPssrr2HqkkiC9mlsvSeTGXnHnJ4u0ILQgInhpZWQyGbFBem65JIlJfeNRyuUoLHnwxXhvUcZjdEEw5QfyaqPJLqujfZSRACXYowZTNvQ1Ssor0Fd6MBqiKau0s7OigkiznpggHR+sPtzo95bWOpj8XR5vX/8ydklBaa2fitdHZZbWEhOo5eErU/l8Swn/XXr88ZZOjWnyOgCPLph6VyXhRg3Bh3/wZvo9kfPcVtWNMGl4Y0J3bv5oI063dyXqxqwK7hiSQrgxj5KTcr2EGNSM7RGLopVnOg0JUDGhdxzvr85i3rZ8XhnXldWHyjg55WWARkmvxKBTFmycMiDxtCn1WzKNSo5cBn42AQIQEqChxk9wExOoJTpQx4dTemF1uFHIZYQGaM7ZYnRBaMla7zuGgEapQOGogYV/9w1cAGyV8Pn1dDbV8d3WPG65JIm0+EjetFzCZW9uYfLXWRRKIdy9II/rPj3Ev37ez79/TWdVRuMPqGN25VmwuiQ0lRmNhsFPFBuk55/XdKRDhI7/rfAdTl9yxI076VL/FwYlsa9Gj9sjcU8vI4Fb32rcJqZX42Nnyen2kF9l5VBJDXmVNjrHmFj84GBmDE6mf3II0wYkkhSq5/s7+3NTv3gMagV6tYIJveOYf/cA4s7hguGLlUqhYNolSaSEGai2OflpRz5vTexB9AmFATtEmZg9rTfP/5pOVKCOS9o0Tu0/Y3AySSGte9dMSICaER0j/Z4LNqiRy6DGzzTkvZe3xahVEahXEx2oI8KkFYGLIBwlRl5aiJKaeipqHdhdHoINasKMGrQqxekvrCuDjIVNnjPWHSFIryKjyEKQQcM7q7yFEK/rEcMPOwoaKt6Cd/Hl6d48VfXlhGV8zdiuU/lqW+P1JwEaJWlRRkos9djsNIxmHPP2+jIun/w8KXUzvMn0jjHHkTvyE56aW8b0gfF0d2yGqiO+D97rtj9dcbis1s7Xm3J4b+VhauwuNEo5E/vEc/fQFB4dnkq9y4NGKW8YKfjn1R2459I2gHc31xn9TVqJ6EAdn9/el21HqjBplaw5VMb9w9pi1qlQyGTYnG4ySmrYk2/h0e93MXNkGhN7x7P+cDlqpZxL2oTSLiKAoCaS2bUWARoVT1zdgdxKK7vzLQ3Hg/Qq3prUnfxKa8NWf/Bur35sVBppkX/N8iaCcCZE8HKRkySJjOIa7vp8W0PVZo1Szn2XtWFS33iCDaeZ+3bZaHKoBJCVH+SjG0ewKtfNm8uOJ6K7LC2c+7/yXTSbX2UjKdTQ5BB411gzgaVb0O79hnunPsTBcidbjhxPo27SKnn1xm7MWnaI39KLWXp/P0xapc96gBq7ixu+yuP5K9/mimgnyoqDeMyxVKii2FGu5cWx0eSWW5DCOiO1G4EsbxMYo2Dgw97ifbrAU9+PU6h3uvl4TRazTtieand5mLMum2JLPS9c37lRFlWNUkGU+a+7eDLKrOOKDmpeW5LBe6t8pxMVchkfTfWOhDncHp75eR8mnZIuMYG4PB7yKq08c22n5uj2BRcdqOPjab0pqKono7jm6JoViX/9uJf4YD0fTu2F0+VBJpOREh5AaIAavVq8PQtCU8T/HRe5/Cob49/f4JOsyu7y8MpvGUSZdYztGXvqB9CYvD92S+NzKj1EdSVWXcc1bYx8tkHVUOnZ42k8KgLeTL0PDmvHq0syfI4b1AoeH5WGeevH4HbgqS1jRKdIpg9OJrusjuQwAx4JXvs9g70F3r5sz7Nw96A4XlziO3VUbXPy9qZq+tzUCVVAHG6Pmw83VrJoXy55lVY8ErykU7L4zjeJ0rlAofZW3/6TSmvsfLTG/66QhXuK+NvwVJH234+SGjufrD/S6LjbI7Fsfwl3Dknm3ZXewMZic7HmUBlBehVvTOjOaVITtSphRi1hRm1D8cZKq4P3b+6FBJh1ytN/EREEoYFY83KR255T5TfLJsCrSzIotjSuauzDGAmDHm58PGEATP4ONryD7JNrMH07lq/6ZvPh2HgUchl1DhfhRt830wEpIQxpF0b/5BDm3tWfa7tG0TsxiLuHpvD1jH58tDaL0o63gkyGS4KP1mSxeG8huRVWvt2Si1wG13SJ5pouUdxzaQqp0cHc0D2SRy5PxKD2TrfIZHB5aigfTO6KXVIw9O2dHLRoGNg2jMo6R8OIj8Xm4vcsK5iiz0ngAt6g6djQvT+FVed2MXBr4XB5/OZwAfh0/RF6JwbzzuQeDO8YSZ+kYO4emsKrN3Zj3aEyzH629f9VBOnVJIYaSAo1iMBFEM6SGHm5yJ245uRk+VU2nKf4sAVAoYIOo711hDa+A9YKCIyDITPh8+uPF2isLSZo8X0MbDeap4c9yNebcrljSDLP/pwOwL+u7UiV1cG/f9lHpdVJlFnDx9P68O2WXDweic3ZFVzRIZISrRz9Tb8hGdrwz6vt1NpdJIYYqHe6Kau1s3R/MWFGDdd1j8Vqd7K22MbtA+IY3TWKGrsLrUpJiBaOVLu45p0NACzYkU+kWcukvvE+UxPh5zgFv0596vUqYtTFP61KQaBe1WSQbXe52ZlbzdDUMIqq69lwuJzP1h/hx/sGolOJtyBBEM6eeOe4yHWIMjZ5LtKkRaU8g8EzQyhojDD8BVDpwBQDi//ht7K0NuMHhna7m38vr6ZvUjDPjenE/iILOeV1fL05l4l94umXHILD5cEjSd6CfECXuEAq6xwcqpKxxxnBvz7eiONoojaZDKb2TyQ5zMDdQ9vw1aYcftldiEmrpKSmHrNOxZvLDrH1SCXPX9eJn3cVsi6zvKFPKzNKuX1QEh2jTby3yntMp1LQIercLmgMMajpmxTMxhOKPB4TG6TzTfInNIgwarj30jb8+5f0RufignUkhgYwd1s+B4pruKJ9BPdd1pa4YB1xQa1/V5YgCOeHCF4ucr0SgzFqlH63Uj4wrG2jqR2/NEbodAPs+R5+ewKu+i/kbmyyeVTJKhbPmIhdYWBjViUT+8Rz80ebeGdyDz5Zf4SP12YhSd6Fw29M6IbbA++syKS0pp7/jOvKzR9t8nk8SYI567J5cWxntudWMqV/AtNmb0apkPG/Cd2547OtfHZbHzRKOZ9tyPEJXMAbqDhdEg6Xd85Iq5Iz55beRJmbGHlxOaC2yLsdXKn1Fps0NN6me7JAvZpXxnVl6sebGhZHA4QZNcye1psIU+svtvhHKBRyxnSPoabexbsrMxum3nrEBzJzZHumfbypIR/OigOldI8P5J3JPZGLbb+CIPxBIni5yEWbdXw1ox8zPt3SkIFTKZcxfVAyV3aIQHamKx4DwqDPHd4pJEctyBXg8b9OQYGbOruL6+ZsxO7y8PakHjwzuiOvLz3I9pyqhnajOkexPaeqYSpnZKdIftzRdM2fbzblMiQ1jLJaBy6PhMsjUWt3oZDL+GRdNkPahfL91rxG113bLZqVGSX88+oOzJrcg86xZiKNWv+JzerKYedXsOJ5cBwNQKK7w/UfQGjb096muGA9X8/oR26ljUMlNcQH60kMNfyldxSdidAADXdfmsINPWOptjnRqRTU2l1Mm72JypOmk7bnVLH8QAkT+8Q3U28FQWjpRPBykZPLZXSKMTP/7ksoq7NT7/QQFqAmNECDXnOWfz65HEzR2CwVaNqNQr7/J7/NXG1GcMfnWQ3foEMD1CjkMp/ABeCartHM+HRLw79DDGqyyv0XkgMorK7HpFWhOiHocHsk5DIZqw+WcUOvuEbXdI010zbciM3hpqbexbD24aiVTaxNkSQ4uBh+e9z3eMF2+ORquH0pmE+zOwsIN2kJN2npmRB02rbCcRqlgrhgPXF4CzdO/3RLo8DlmM83HGF4xwixUFUQhD9E7DZqISLMWjpGm+mZEER8iOHsA5ejDpfWMv27DHJ7zvQWZzzZ0H9Qrggn94SdNRqlnP1FNT7N9GoFFXV2XCckfMksq6NTtLnJ390xxoRbktidXwWASiEj2KCm1u7CqFWhUyn4anpfxnSPZmSnSF4c25kp/RM4VFpDhEnLLXM2N0rH76OmCJb9u+lzRbv9nxPOOY8k4TxFcUrvmqkL2KFmYHO6KKy2UWypx93KC3UKwoUmRl7+QgqqbEx4fwMlNXZuKrfy3jXziSpZRVDuEjyGCOQ9bgaHldJyb4E9pVzGFR0iUMrlJIX6pnB3e6RGJQA2HC7njsHJfL7hCHUnbZ2Vy2iotfR/R5PA3T20DfO3e2sTTe4Xz39/O8BdQ1PoGhNI17hAKq0Oau0uth+pYkWGt9BjYVU9sU0t9HTVN6515HMDtkPqyDO+X8IfZ9SquKFnLJuzK/2eH9MtmqBWunvL45E4UmHl7eWH+G1vERqVgkl94pnQJ05MPwrCOSKCl7+QXXlVDSMXuRU2Rn2STZfYznSP6o1GqeYOez0hai3Btgo6RBmZObI9v+wuZMrsTTxxVXvignXkVnhHZOwuD0qFHINa0RCoSBK88tsB/jexO//9LYN9hd5kdHHBOh4b2Z4wg4YP1xwmLdLIhN5x7Cmw8POuQvomBZMSGsDL2Qe41eXh6Z/3Nfkc3KfIFoxCDfpg73Zwf8JS/8BdE/6owW3DSA41+Cx+Bog2axnTPabV1unJqbAy+q01xzNH17t4Y+lBFu0t5JNb+hApAhhB+NNE8PJXYatic2Zpo8O78qrZlefNJTOlfTye8v2EB0Ty6rjuTJ2zhWKLN9h5ZfEBXhzbhScW7CGnwgp4dxD9+7rO/O27nbiPzgHsybfw/K/pvD25B26PhMcjoVcrKa2tx4PENV2jCTdq+D29hNp6J1/e3pcAjYIb3/fmdLHa3YQFaCitbTw9pFHKiQk8xRu/MRIGPAi/P9n4nMYIsb3P5o4Jf1LU0dpHC7bn882WXNweieu7x3Bj7zhiWsE26WJLPRabE5VCTrBBjUmnot7p5p0Vh3xKXhxzoKiWHbnVjBDBiyD8aSJ4+SuwVcOm90k2DmuySYRJg9wQSlbAtdTYXazYV9IQuAAUVNczc+4u7r2sLQkhegqrbQTq1cQEallw9wB+2FlAZkkdPRICGdY+ghcXprPigHf6SSaDMd1iuLFXHJ+uP8LGwxX0SAhEpZDz0858buqXSL3Tuybg0/XZPDIilUfn7mpUkunxq9oTeqqt4XIFdJsIlYdh2yfHazoFRMCkb8B0+sW6wrkVHajjjiEpjOsVh4REsF7tf5dYC1Jb72RDVgVP/bCX/KNrwwa1DeXZ0Z3QquQsSW9ckPSYedvyGNY+vMXfA0FobiJ4+SuoK4XlzzFo4pU+1WuPkcngvZt78dLKLH7YUcD9l7dhT34113WL4sZUFZGqOpArOGBR89Lqw4QY1EzqG4ckwcdrspnSPwGny83Q1FB6JQYz/r31Pt88JclbEynarGVSn3hWHyxjw2Hv1I5KIWPaJUkN0ws786qJPVDKB1N68c3mXA4U1ZAQrOPey9vSPtKI7nRVmwPC4YpnYMD9UJULWqO3cKMxir9UIZ0LrLDKxvbcKn7bW0R0oI4x3WOINmsJ0KpQyGWtKsHfngILt3+yxefY6oNljH9/Pd/e0f+Ur9EAjRK5eB0Kwp8mgpeLjCRJFFvqKat1YHO6CTdqCPsj26JPVOYtohi59p98cuNLTJ+b65P07omRaSxNL+aHHQVclhbOpanhRBo12OrtBGjcmCrSCVnxKEmmGLqNepP3MgwkBhv4aG0WdwxJ4XBpHe0iTAxpF8rqQ+V+h8wBvtiUw+xpvlM3TrdEWa2DJ6/pwPdb81i0p4hfdhdyuLSWf1/XiSizDqNWiVF7FjVwtGbvT0jK2d8r4azlVliZ+MEG8iptpEYYKa9z8OvuQu4cmsLIjpFIEmjVCrSnCzxbgIo6B8/5ySQMUGyxk1dp5aZ+Cby0aL/fNpP7JYjkfIJwDojg5SLi9kjsLahm+qfH15oo5DKmD0xi+uBkQgLO/ttrRZ0DR2BXDH3/D+OuOfTacD+Lxj1FjtOMpd5Nm2AlquBQRr21geEdIxjSLpxx76732QI9OKUNr4z5hvC512E+OI+p/f9JRb3EbQOT2ZpdwXursiiy1DOiUwRtwpouZ1BldWI7aRdSYoiezNJaXvh1P5/d1oerukQhSVBWa+e1JRn8d1y3swtchAvKanfxym8HiAhQ8fHocEJKN6CvyqC2Y3cK9WHkVJj4x/zdpIQFMGNwMgkhBgx/JhBvZnanmz0FTdcb+3FnIf83rB0LdxewK9+3kvtN/RIa7doTBOGPabnvIq1QQZWNie9v8Nlm7PZIvLvqMAmhBib0jjvjjLoVtQ42Zpfz1rJDFFXXM67HDdwzbRp6rMS4ncRkLoUNsyA4iayrv6HO4WJC73hu+2RzQ/4NjVLONV2jGZoaRo5eheu2nXy+vZwP/rcep9vbqF1EAC+N7cyTP+6lbYSRoe3CSIsyIkOG3eVm7rY81h7ypvuPMGnIrbT69PPBYe2YteIQIQFqMopr+Mf8Pb7Pw+ogoqkyAMJ55/FIFNfUU2V1opDLCNKrfaaAKqwOMkssfDRMTsTcK8Dp/fvqgDB9CJYJP2DWqViwo4Afdhbw5sTuXNkxArWiZY7CyOUywo0an/VgJ0oKMRBp1vLB1N7sLbAwd2suBo2SiX3iSQgxEGxondvDBeFCE8HLRWRjVkWj/CjHvPH7QS5LCz+j+jo19U7eW5XJe6sOI5PB26PjGFC/AuNnb0Btibcw4yUPwuRv8WjMaJ12hrQLY0duVUPgEqRX8dr4bszbls9D3+ykZ0IQA1JCmLW20Od3ZRTX8rfvd/H2pB4o5DJ+3FnAN5tzqHd6CNKrmDYgkaGp4Tz3Szp3D03hh6PlA9pFBPDEqDQ2ZVdy28BkksMMWGxOXhvfjc/WZ7PtaDZfz6m2RgvnVZ3dxbrMcv4xfzelR7fYp4QZeG18NzpEmVAq5HgkiScGBRHxy3UNgUsDazmmX+7klj4fsOZQOZIEM+fupntcYIvdbRRu1HDnkBSe/qnxdn6FXMbwTpEARJi0RJi0DG0XhkzGmZfxEAThjIjg5SKyv9DS5LkiS/0pM5aeqKzWwfurvfWGbu0dxpCSzzFse+94A0s+LHwEacijyGP7EJG1kpt73s6yzOO//4mrO/Dsz+lkltYCMLZnDC/86n8ev7TGTpXVwcLdRczfcTxJXKXVyWu/H+TuoSm8dmNX1Eo5Dw5ryzOjO1JtdZKor8clBfH4/D0UHq3bFGJQ88iIVGKD9Kw5VEZwK01k1hIcLKllxmdbfHZ9ZZbWMf69DSx6cBAJIQYCNEpSdDVQV+b/QYr3kKI/nq251u6ipMbeYoMXmUzG1V28Nb1+3Hm8jpdGKWfW5B5EnzRKKNa3CML5IfbrXUS6xDadWj82SIdaeWZ/rvRCS8MHzsSOOgw7PvTbTrbuf2COQa4LoWeMnkFtQgHvt0u3R2oIXAB0KiXldY4mf+f+ohoyy2r9npuzLpsusYGEGTUcLK5hf1ENn2/Mwa0K4I7PtjYELgDldQ4em7eba7pG8fLYzoSLSs7NoqbeyatLMhptVwewOd38uKMASZIINmgwy5t+XQDgPs35FibMqOWZ0R357f8G8+qNXflgSk+WPjSEQW1D0bSCRcmC0BKIkZeLSI+EIMw6FdW2xsXs/nZlKuHGM/sg1xwNcpRyGQZHWZPVo3HavFWYbeUE5i+jS/Rw4oP1xAXr2Jlb5dO03uUmUK+iqolCe8mhBkqaWAdgdbiptbt4f9Vh1Eo5Rq2KlFA9Kw5WNKydOZEkwXdb8nhpbJdWm4X1Ymd1uEkvaHokcHN2BXaXG61KiTIkHmRykPyMDGqMVEoBgHdrvEGtaBXbpgP1agL1atpFNL1AXRCE80eMvFxEYgJ1fHtHf1LCju9I0KrkPDYyjSHtwk59cb0FKg5DSTrtQrWoFXJcHgmP4jTZPO3VsO5/MG8G0Qtu4PNbetA52kzQSQsLF2zPZ3LfBL8PEWxQ0zYigCJLvd/zACqFnEMltUzoHc+vuwoZ2SGU77YWNNl+f1GNz44n4cLSKOXEBDX92mkTHoDq6KJbuSEc+szw2666/0xmbTk+Ivf8dZ0JbwXBiyAIzUuMvFxEZDIZqZFGvp7R37vF2eUmyKAmLEBz6uHoyiOw8FE49Bt0n0JM5xvZcXswpXYVcl0ARHSC4j2NrwtLA1slZcPewB6YgrK+guidb3JT73uodSuYtfxQwwLe1QfLGNkpivG94/h+a15DOYCEED3PXNsR8E43+av6PCAlhGqbgxev78LTP+3lrRs7EKGXSAkPYPMR/4X7EkMNaFUitm4ugXo1Dwxryy2zNzc6J5fBhD7xx0fFtEYY9DcIbQer/gM1hRCcjOvSJ9nmas+RqmJGdYrkzqEpJIcaUCvF1IogCH+OTJJa13YOi8WC2Wymuroak8nU3N05/yyFMGcUVGbB6Lchew3s+ub4VFFUN9xj3kXx7c1QfvD4dfoQqif9yvYSiedWlHCwpJawAA13DopnUGokyGRsya7gnz/sbQhUZDL41zUdGdgmhKrqatQaLWq1mpIaO7nldXSINnPLnM1UnjC1lBxq4IWxnZEBJZZ6OkaZyCgsp2NcOLUOFyPfWO13XcV3d/and2LwebxxwulU1jn4clMOry3JaBgF06sVvDa+G4PbhqJTn/TdR5Kgpgg8Lm+RTGMENqcLq92NTqX4c4kWBUFo9c7m81sELy1d5gr4bDR0GO0tTLjxvcZtgpORJn8PuZuQlaYjRXRCMkbxY1EwD/6Q3aj5NV2juLFnHJ9uOMKkPvGkF1qod7oZ3jGSQ6W1/Lq7EJ1KwWVp4eRV2njltwOM6hzFI1e2o97lIavMSkG1jbbhAQQb1Dz1w16eGd2JjYfL0akVmHUqLk0LxyNJrD5YxsPf7qT2aMZfjVLOv67pyFVdojDpRHK65ma1uyirdZBdXodaKScuSE+YSd1i87QIgnDxEsHLXyl4WfkyLH8Oxs2BBXc3zrVxVN3kX7BrQ8FpJcigpagwj6t/kjW5g+ibGf2Y+MEGPJJ3fcOTV3fgxYX72XfSdu5ru0bTOzGI2CA9M+ftothiJzRATZBeTaRZy5NXd6C81o5cJsPqdJMWYcSsVzV8a3e6PZRY7BRb6nFLElFm7emnyQRBEIRW52w+v8WigpYu8NgiWlmTgQuApzyTTFcoI76q4LcjHiz6+FNufT5SbiXE4F1YGRagYX1meaPABeDHnQUkhBh4/tf0hqyjZbUODpbUsvpgGf9behCjVsXaQ2X8tLOAzVkVPtMNKoV3YWiPhCB6JwYTG6QXgYsgCIJwSiJ4aeni+4Hy6BZqVdO7Q9RhKcz4bBslNXbu+yEXt/7Uu5dCjWrqnd51M1d0iOCnXU3vDPp+ay5pkf63jP66pwiFQsabyw8xrH0EscEtMzlZS+d2eyissnGkvI7i6npa2YCrIAh/MSJ4aeFqNeE4J82FA4ug22T/jQLjyZdFoFUpePyq9rw1qTt2j5xeCUF+mwdolEQH6rh9UBLgHR05Fsj4Y3N6UDWRQM/tkaioddAmPIDkMAOxwafZui2cc6U19by78jAj3ljNkP+s4Nq31/Dd1jwqTjHyJgiCcDETwUsLVlPv5PNN+Uxc6GFXu3uwd78VT5fx3m1Bx4S3xzPpO3LqVLw4tjPfb8ljxmdbue2TLTxwedtGOTfUCjn/vbErqzJKKamx8/G03gRoFFzRIaLJflzdJYqtTWx5DtAo0ajkvDS2C8F6NWFnmGjvRDani8IqG4VVNmxO11lf/1dmsTl5edEB/vPbgYbkh8UWO3//fhffb83F4Wo6KBUEQbhYib2LLViJxc6Liw4AcO3nNYQa1NzR/x6GTbyXOHUdSrkMyg4i//Zm2oyczbVfHmz4tl1R5+Cx+bt5enRHrHY323MrSQ410CnGjMcj8d/fMlDIZUSatPRICGJU5yiu6BBBZmkds5YfatgOnRZpJD5YT6BOxRE/fbxtYBIqhZwIk5awP5Dq/0h5Hf9bepCfd3kLQl7dJYr7L29LQojhNFcKAOW1dr7fluf33Bu/H2RU5yhiW2idIUEQ/rpE8NKCrTpY6vPvsjoHz/2eh3JYJJPKP0SZ/r33RHAy24ucjaYJ8ipt3PX5NvolB3P/ZW1Yl1lOhF6BAzl3DE5mSLsw/vPbAf67JKPhmk4xJv43sTvP/5LOiE6RjOocRVmdgxeu78zrvx/k9/RiPJI3Dfz0wcl0jwskQKNE/geq6uZWWLl+1jqfhcVzt+Wz4kApC+65hDixfua0cittfvPoANQ53FRbncT6nz0UBEG4aIlpo4tURZ2d8lp7Q4K4k9XZvcm/TqZWyBkW40FzLHABCExgR3HT0wMbDlcQZNAQGqAhu8rBR2uyMWpVvL08kw2HK3za7sm38OLC/bw9uQd6jZL1maVoFXLK6xzcMTiZH+8dyKe39uHlG7owIDkEuUzGweIaFHIZkiRRYqmnsNqGxU/9phO5PRLzt+f73RFVXudg/vZ83GdYZfuvzKQ99fcTzVlmMS6tsVNYbRPrZQRBaFZi5OUiU1xdz/IDJXy24QhOt4cx3WIY3T2GmMDjC10r6xx8tj6b1CjvPviEED3RZh0F1Ta0SgX6ok2+D1pbRGpC0x9SESYN5bX1BBvU3PbJFlweiQ+n9uL5hel+2+8tsFBtc9IrIYjf9hazOz+HPknBeCR49ud9DQnnYoN0vDO5B3LA7fEwZ102H67OoqzWTp+kYP4+PI22EQFo/WyNtticLN5b1GSfF+8t4uZ+CY1qMAm+IsxawowaSv2Ubegaa27YDn865bX2hq3veZU2UiONzByZRucYs0gmKAjCBSdGXi4ixZZ67vxiKzPn7WZvgYWM4lpeXnyAG99dT36lraHdvkILr/5+kLIaO19O78u0AYm0CQ9g2oBEHhuVhswU7fvAJen0j5DQq/3nT7lzSAo19W6e/3V/Qxp4h8vT5HQDeEc/8iqttI0IYGNWBY98v4tP12fz2viuDeuF8472OTHMwD/m7+bpn/aRX2XD7vKw+mAZY2atZU9+td/HVypkGE8xahCgUaJUiIrTpxNp0vLx1N4YT0rNH2nS8vqEbmcU/NXWO/lg9WEe/GYHh8vqcLg97M6vZvKHG1m6vxiXGAETBOECEyMvF5HtOVVsz6lqdDy/ysa3W3K5/7I2WJ1uZq04RFywjqhAHXd9vq1hFwmASafk69uGEByc7K0yfVTUqr/z5YS3mD4vn9Ja77dwuQwm9I6jY7SJijqnT1VotVKOXAZNFXaWy2Tc99UOIk1aXhrbmccX7GFvgYX1meUMaRvGigzvehylQk6l1cmSfSWNHsPtkXjyh718dlsfQgJ8RwCMWhW3DUxuNG11zO2DkjFqxTf+05HJZHSMNrHwwUHszK0is7SOzjEm0iJNRAWe2bb18joH76867PfcMz/to29SCNFn+FiCIAjnghh5aUYOl5uccivfbM7h112FfL0pp8m287bnUW51YHd5KLbYmTEohad/2usTuABYbC7u+HIHJWO+9TmuLNhCl42PsOCOHnx+Wx9mT+vNx9N6Y9Ao/U4prMooZVTnKL996ZMUzL4Cb7bdIks9jy/Ywz2XtgFg/vZ8Rh69rk14AGqFjPWZ5U0+r32FFmrq/W9/7h4XyLVdG/fh2q5RdI8LbPIxBV9yuYzYID1XdYnm/svbcmlaxBkHLuDNttxUEFtpdVJpFetfBEG4sMTISzNxuNxsyqrg1jlbcLg93NgrDtkpduQoZDJkeKdS+iQGE2nWcKTcfzmA3Aob5cowwu9ah7T1U2SWXKrjLicvuC+Lt5Uwpkcs2WV13P7pVsKNGq7tFs3B4lrCjRpKjgYyX23K4Y0J3QH4dXdhw4fXkHZhTBmQwNJ9xbx7U0/cHgmlQkZYgAatUk6t3YVOJUerkvP34al4JE45/aOQy1DI/T/vUKOGp67pyK0Dk/hhuzfD77XdookP1jcaqRHOH10T043HqBXiO5AgCBeWCF6aSXGNnds/9QYuAGsOlnLf5W1ZfqDx9ArA+N7xBBvUKOQybhuYxIHimlM+fr1LguiO2C7/N19tzCZQq8OMitQoDwaZg47hGubc0htJApvDTVyQjpkj0/jbdzvxSOB0Szzw9XYm9oln3l2X4PJ4qLI52ZpdQb3DjcsD93+1HbNeRWKInt6Jwbw2vhvfb81DIZfx4ZRevLcykxfHdqFfckiTU1DDO0YQfIp1FyEBGkICNHSLE/t5m0t0oA6TVonFzwhZaoTxlH8/QRCE80EEL80kvcBCvfP4QseCo/Vm+ieHsP6w7zRLSpiBMd2iG0YoEoL1qJVyzFoV1fWNtxyrFLKGHSBKhYI9hXXM3+7N1dI+ysh3E+JYn2vjb7/kUW1zolcrmNQnjin9E/n+zv7MWXeE/UUWEkMMDG4Xxndbc7ksLZx7vtjG+N5xLN5bzMascv4zrgt2l4cDRTWEBKgJNqh5fFR7bE43181awzs39SLCpMXh9vDC9Z15dO5un35Gm7XMHNkeg0a8DC9mEUYN79zUk2mzN+F0H49ATTolr0/oJkbBBEG44GRSK6vQdjYltZvT15tymDnP98Nco5Tz9OiOuNwSv+4uxOHyMK5XLIPbhhEVqKPK6qDIUs+SvcU43R76JoewLaeS138/6JMP5pZLEtEqFdw2MIlQo4Y9+dVc/eYaAJbfkYbdo2Dkh3tRyGQ8dEU7UsID2JRVgUoh46ou0UTqJOT7f8ATlMKMZTJ25FXxtyvbkRRqICZQz6Nzd/KPUR34x/zdDTuKAPRqBbOn9UatkKNTK0gINaA7ug261u6ioMrG/G155FfZGNY+gl6JwWKhZwvhcLkpqKpn4Z4i0gur6ZscwpB2YcQE6k453SkIgnCmzubzWwQvzWRvQTVX/W+N33O9E4J4a1J39Bplw46aijoHs5Yf4sM1WT5tr+4SxZB2YTzy/S4iTBqmDUhEo1TwzM/7WPTgINIiTdTanWzOquD13w/y2bgY/rWsjHk7S3hxbGd+31fM7+m+U1X3DknkdtUiAg//xM9dZ2EKCmNjVjkrM0oxa1Xcf3lbXv/9YKMRIgCzTsWcW3qTFGogUC+mEwRBEIQzczaf32K8vplEmrQMaRvGypNS/APcOjCJCLPviERGcU2jwAXg512FXJYWzpxpvSmoruf7rblsy6ki2KDG7vRQWGXjSHktZp2KV8d3w+mp5kBpPakRRmrrXY0CF4C3VmZz2eQr6bHrQ9rHhXPDh9saahkBTLsk0W/gAlBtc2Kpd4nARRAEQThvRPDSTEICNPxnXBc+XZ/NJ+uOUGN3kRJm4PGrOtArwXdxar3Txcd+ApdjPl1/hAEpIcxakUlcsI43JnTD6fawJbuClPAAIs1aftyeT3GNnan9E73FFEMMzG2iYB/AJ7vraTP4aV5dmuUTuAA4XKcerLPaReVnQRAE4fwRwUszCjdpeWBYOyb1TcDtkdCqFIQZGy9+dLqlU9aSqbY5MWiURJm1PDemM498v5Niy/HcLVFmLS+N7cLfvtvJL7sL+XBqbw4UWaiyNl1fqMzqxhLWg0XzMhqdc3k8mHWqRjlmjmkbEXCqpy0IgiAIf4pI0NDMVAo50YE64oL1fgMXAINayRUdIpp8jAEpIewvrOaOISk8/dM+n8AFoLC6nud/TWfG4GTqnR6+2HCELjFmeicGN/mYV6aFYFOH+D33zeZc7hyS7PfcyE6RhIrdJ4IgCMJ5JIKXFkAul3FV5yhC/OTTCNAouX1QEo+MSKNnfCCZpbV+H2N/UQ0JIQYAFu4p4suNOdw7NAmtn6rCYUYNvZPD+WRjPle0D290fl1mObV2N6+M60JskHdtjkmr5O6hKcwcmSbWuwiCIAjnlZg2aiFig/XMvWsAr/x2gIV7ivBIEpe2C+OxUe2JDzagkMvIcNbw/s09cUsSLrfEl5tyfFLzO1zH88qsP1zBYz3dLLgpkedWVbE6swKlXMawDhHcd1kb/j53F9llVj6c0ov1hysaTRFll9XRMdrE6+O7UWyx4/Z46BxjotLqaAiSBEEQBOF8EMFLC5IYauClsV14bGQaEt5tyUatCkmSSC+0cP9X2zlY4h15MetU3HtZG9Iijcxem41SLkNzwijLwLah1Buj0duquW9wLA9f2Q6DVoXkdlFV72RPvrd20RM/7OGTW/uwaE8RKzNKMOtUjOkW05Dtd9rszdQeXaD78tgudIg2XvD7IgiCIPy1iOClhTFolI0y0uZX2Rj/3nqf9O3VNifP/ZLOf2/sSlywjoFtQlmyrxgAo0bJxD7x/H6okn//csAnbf/wDuH83xWpDf8+VFLL9bPWcsuARJ66piMquQyDRsEn648wb1s+9hNGc/YVWRjUNvQ8PXNBEARB8BJrXlqB1RllfuvOAHy8JotnR3eiR3wQP+zI59qu0bxzUw+cbg/P/JzeqN7Q4n0lVFoddI4xNxzzSPDR2mwmvL+BabM3c6i0jq825foELgAdokyYdacu4icIgiAIf9Z5C14qKiqYPHkyJpOJwMBAbrvtNmpr/S8mPWbo0KHIZDKfnzvvvPN8dbHV2JRd0eS5A0U1RJm1dIw28+GUXkzpH8/+IgsL9xQ1ec2Li/bz4tjOjQruqRVyXhvfzW/OGaNGSa+EIPQasVhXEARBOL/O27TR5MmTKSwsZMmSJTidTm655RZmzJjBl19+ecrrpk+fzjPPPNPwb71ef7662Gq0jzIyf7v/c3HBelYfLOO5X9N55MpUKuoc3D4oiVd/a5y/5ZgDRTUo5TI+ntqLzdmVpBdaaBdpJC3SiN3ppl9yCHvyLQ0VsWODdLw5sTtmrZiFFARBEM6/8/Jpk56ezqJFi9i8eTO9evUC4M0332TUqFG88sorREdHN3mtXq8nMjLyjH+X3W7Hbj+e18RisfzxjrdQwztG8srijIZg4kRT+ifw7ZZcJAm2HKnksZFp7Mm3MCQ1jBGdo/hpZwHzt+f7XNM7IQicNsKMRkZ1juSqLpHkV9Yz7r31AIzuFs2syT1weTwo5DJKa+xoVXLMYou0IAiCcAGcl2mj9evXExgY2BC4AAwbNgy5XM7GjRtPee0XX3xBaGgonTp14rHHHsNqtZ6y/QsvvIDZbG74iYuLOyfPoSXRq5W8e3NPzDpVwzGFXMZtA5NweSTSC2u4oWcsl7QJ4eo313D7p1u458vtTP90C1FmLfdc2qbhOrkM/n6JmcSMj9mXW8LKjFKufXMtNSek/P9hRwG3f7qFOz/fxvRPt/LUj3tRKeQoFWIJlSAIgnD+nZeRl6KiIsLDfZObKZVKgoODKSpqeq3FpEmTSEhIIDo6ml27dvHoo49y4MAB5s2b1+Q1jz32GA899FDDvy0Wy18mgKmyOqi2OSmoqsfpcvP57X1xuNzklFsJ1Kupd7r5alMOOpWC4R0jmf7pFp/r3R6JWSsy+d+EboQbNQQb1DwzLII2259DU7kfT/jN/GP+TsBbBbtfcjAbDjdeX3NjrziCxKiLIAiCcIGcVfAyc+ZMXnrppVO2SU9P/8OdmTFjRsN/d+7cmaioKC6//HIyMzNJSUnxe41Go0Gj+eulo8+vtJJXaeOZn/ext+D4VNkVHSIY2yOWGZ9tQadS8Px1nTHrVPy8q6DJx/pyYw6f396H8upa+mx9GDJ+pXT4LF5afKihzazlmfxvYnfMOhVL9hXjkbwLeMf1iuWOIcmEiJIAgiAIwgVyVsHLww8/zLRp007ZJjk5mcjISEpKSnyOu1wuKioqzmo9S9++fQE4dOhQk8HLX1FpTT2bsip4a3lmo3IAS/YVY1ArGdsjlq835yKTyQg1anwCnJPlV9uod3gIUtixm5NQpl6D3ZTM4bLjoyw2p5t7v9zGhD7xvHdzT/RqJeFGDesyy3wy9wqCIAjC+XZWwUtYWBhhYWGnbde/f3+qqqrYunUrPXv2BGDZsmV4PJ6GgORM7NixA4CoqKiz6WarV1hVj06tbLKO0c+7Cpg1uQdfb85FrZSRW26lS4yZTVn+t1R3iDKhkMtYk+/mxaIxxBjlTDDEMm2AkTnrjjS0s7s8fLIum0/WZfPI8FR+3FFAWa2dKzuceUAqCIIgCH/WeVlh2b59e0aMGMH06dPZtGkTa9eu5d5772XChAkNO43y8/NJS0tj06ZNAGRmZvLss8+ydetWsrOz+fHHH5kyZQqDBw+mS5cu56ObLVZhtY3SGnuT510eCZdHontcIMmhAdwyMIlru0X7LcKokMu4a2gK0z/bwr9/PcCKjFK+2FrMNe9uJSHEwFWdGweOGqWctEgjB4pr+NuVqUSYtOf0+QmCIAjCqZy37SFffPEFaWlpXH755YwaNYqBAwfy/vvvN5x3Op0cOHCgYTeRWq3m999/58orryQtLY2HH36YsWPH8tNPP52vLl6UHC43eZVWduRWsje/msJqG5LkmwY32KAhwtT0GhO1Qo5GKee56zpzy+xNTP5wI5+uy+ajqb1pEx7Q0C42SMc7k3vw295iCqrqGz3Ov39JZ+qABJ9jARolr4/vxtebcnhpbGdGdIpELpf9yWctCIIgCGdOJp38ydjCWSwWzGYz1dXVmEym5u7OWamyOvhxZwEv/Lofm9MNQLhRw9uTetAtLhCV0htrZpbUsjWnktlrs0gvrGn0OJP6xHNz/3g+23CELzfmAiCTwVfT+7GvwEJ0oA4Jico6J/HBem76qOnt689f14mEEAMZxTWYtCqCDWrahBtQyuVEmLQicBEEQRDOibP5/BYpUS8imaW1WGwu/j4ild351fyyq5CSGjs3fbSRxQ8OJjHUAIBWJadnXCBdx3fnifm72XykEvDmaLm+RwyD24Wxv7CGeduOJ5+TJJi9NpuBbUK5+4utDTWNZk3ucco+VVqdzF63l2qrk3qnmzbhAcy+pY9PThlBEARBuJBE8HKRyK+0sSO3ih935lNtc9I3KYQPpvTi1SUZ7Mit4qddBdx3WVsAnG4PLy/ez8asCm65JIl7LmuDRiknTC9HJpPx5E8HuLZbDBqlgnrn8Z1Ai/cWARIfTe3NoZJa6p1uUsICaBsewMES/4t/20eZeG1JBq6j0c5N/RJE4CIIgiA0KzFtdBHIqajjmZ/28Xu67/ZynUrBrMk9+L9vd9AnMZhZk3tQVutgwvvryS5vnHn4oxsS6M0eDoZdSU6Fle05VXy6/kijdgC/3D+QjtHeytGbsyuY8P4G3CeVmB7TLYYIk4b3Vh0GoGusmfen9BILdAVBEIRz7mw+v0U+92ZWWGXjcEldo8AFvLlVPlqTxbiecXSJNaNUyDlYUuM3cAF4fmUZdklFtKKaX3cXMqF3HClhAY3aTeoTj1F7fPSkc4yZn+8byPCOEYQGqGkfZeQ/N3Rhcr94duVV0T85hKeu6cBLN3QRgYsgCILQ7MS0UTOrs7tYnlHa5Pm1mWXc1C+etEhvFNpUrhaAzNI66nVRqCUXS/aVcKTcyv8mdGNrTiVrD5Vh1Ki4NC0cjVJGqOF4On+tSkH7KBP/vbErBVX1/La3iHdXekdbBrUNBWBlRikjO4l8O4IgCELzEyMvzWz94XLUpyhoqJTLSA4LICZQB3i3NzfFqFGikHnYnFtH97hAnriqA3d/uZW8Sit3DkkhQKvAI3nokRCMXtM4bg3QqIgL1tM/JRS9WkFmaS1fbcrB4fLw/HWdiTSLURdBEASh+YmRl2ZWUF1P3+RgPlh92O/5qztHERuoa9gm3TspGLVCjsPdOCX/lJ4hhOhddIlsy1vt5bg8Hj65tQ9GtRK7W+KeS9sSZmw6P4zbI2GxOog0aZg9rRc2pwe5XEaIQY1WpTg3T1gQBEEQ/iQRvDSzK9pH8MvuQqYOSOSTddk+5yJNWu4f1ha9RonD5WZXXjVfbszh5Ru6MHPeLp+dRENSzEztFYYm0EiApGRfQQ3vr86kos7BpanhjOsVR5S56crP+ZVW9hVY+GzDESqsDvomhTCxTxwJQXpUInARBEEQLiJit1EzK62p56kf9pIUFkCHKBOL9hRSZXPSPzmEEZ0iST664PZQSS0j31iF0y3RNymY2wYmUVpjp8rqZGDbEKL1EiHmACwOeP33g8w5KRAy6ZTMu+sSnwy7xxRU2Xh3ZWajnUlGjZKv7+jXsCtJEARBEM4XkaSuBQkzavnXtR3ZcLiCLzceITXSyKVp4fRJCiba7F3fYne5+Wj1YZxub5y5MauCjVkVhBs1GDRK8qtsuD0eeiQE0TnG3ChwAbDYXLywMJ3Xx3fz2WkEUF7n8Lulusbu4vlf0nljQndCTzHdJAiCIAgXkgheLgLhJi3XdotmcLtQXG4Jk06JWnl8qqa23sW2nKpG15XU2KHGjl6tYEi7ML7alEthauMaRccs219CtdXZKHhZdcrdTuVY6p0ieBEEQRAuGmK30UUkUK8m1KjxCVzAm6zuVLuMogN1lNc5AKg/WhOpKX9kjrBVzSsKgiAILZ4IXloAvUbJXUNTmjx/XfcYFu4pJL3QQs+E4CbbDW0XRqC+cWr/oe3CmrymX3IwwX6uEQRBEITmIoKXFqJthJEnr+6A8oQqzkq5jIevbMemrAosNhd2l4cduVVM7hvf6HqjRsnjV7VvNGUEEGbUcFO/xtcY1AqevLoDQQYxZSQIgiBcPMRuoxbEandRVmsnvaiGijoHESYtc7fm8cvuwoY2A1NCeOXGruwvquG9lYeptDoYmhrGpD7xxAbpkZ8Q/JyoqNrG3gILH63JoqLOQf+UEG7qm0BiqAFFE9cIgiAIwrkidhu1UnqNkniNkmC9mvQiC7d9sgVLvYv2UUbig/Uo5DL+b1g7wo1aQo0aeiYE4XR7MGpVqE6RxRcg0qwj0qyjb1IIdpcbk06JSiHyuwiCIAgXHxG8tECldQ6e+nEfb03qTpBBw+bsCnbnVXNt1ygqrQ4en7+bCquDa7tG0yMhiODTBC4nCtAqCRAvC0EQBOEiJta8tED7Cy1UWR24PTD5ww0889M+0iIDUMjlVFmd9E0OYULveDZlVzLlo40UVNmau8uCIAiCcM6Ir9gtkEYl59aBSby4cD8Wm4tO0Ub6JIVwzxfbKKj25nmRyWBMtximD05h9tosHhme2mgLtiAIgiC0RCJ4aYHahhtxuyUOFNcAMHNke26Zs5kqq7OhjSTB/O35RJg05FZYKa9zEGVuOleMIAiCILQUYtqoBQozagjUe4ssRpm1FFTX+wQuJ/pqUy7DO0WKTHOCIAhCqyGClxZIq1IQadZi1qkIN2o4XFrbZNtqm5OwAA1GnRhkEwRBEFoHEby0UNFmLf+8qj0F1fUkhhqabBcaoCY6UEeARmTJFQRBEFoH8XW8hVIo5FzRMYJwkxaHy0OESUOxxd6o3b2XtiHuFHWRBEEQBKGlESMvLZhZp2ZwuzB6JgTy+W196Rh9PCOhRinngcvbck3XaFRil5EgCILQioiRl1YgyKAhyKDh01v7UFHnoN7lJlCnJtykQSMCF0EQBKGVEcFLKxISoCEkQBRRFARBEFo3MW0kCIIgCEKLIoIXQRAEQRBaFBG8CIIgCILQoojgRRAEQRCEFkUEL4IgCIIgtCgieBEEQRAEoUURwYsgCIIgCC2KCF4EQRAEQWhRRPAiCIIgCEKLIoIXQRAEQRBaFBG8CIIgCILQoojgRRAEQRCEFkUUZmxGDrebUoudkho7cpmMUKOGCKMGpULElIIgCILQFBG8NJOaeie/p5fwxPzd1DncAJi0Sl4Z15WBbUPRq8WfRhAEQRD8EV/xm0lmaR3/982OhsAFwFLv4s7Pt5JTYW3GngmCIAjCxU0EL82gzu7irWUH/Z7zSDB7TRYOl+cC90oQBEEQWgYRvDQDq8PN4dK6Js9nFNdic7qbPC8IgiAIf2UieGkGBo2C1Ehjk+c7RJvQqRQXsEeCIAiC0HKI4KUZ6NVK7rm0DTJZ43MKuYxpAxJRK8WfRhAEQRD8EZ+QzSQ51MA7k3sSqFc1HAsNUDN7Wm/ig/XN2DNBEARBuLiJ/bjNRK9RMqx9OL/eP4jyWgcyGYQEqIkwapHL/QzJCIIgCIIAiOClWSkVcqIDdUQH6pq7K4IgCILQYohpI0EQBEEQWhQRvAiCIAiC0KKI4EUQBEEQhBZFBC+CIAiCILQoIngRBEEQBKFFEcGLIAiCIAgtigheBEEQBEFoUUTwIgiCIAhCiyKCF0EQBEEQWhQRvAiCIAiC0KK0uvIAkiQBYLFYmrkngiAIgiCcqWOf28c+x0+l1QUvNTU1AMTFxTVzTwRBEARBOFs1NTWYzeZTtpFJZxLitCAej4eCggKMRiMy2Z+rzmyxWIiLiyM3NxeTyXSOeig0RdzvC0/c8wtL3O8LT9zzC++P3nNJkqipqSE6Ohq5/NSrWlrdyItcLic2NvacPqbJZBIv+gtI3O8LT9zzC0vc7wtP3PML74/c89ONuBwjFuwKgiAIgtCiiOBFEARBEIQWRQQvp6DRaHjqqafQaDTN3ZW/BHG/Lzxxzy8scb8vPHHPL7wLcc9b3YJdQRAEQRBaNzHyIgiCIAhCiyKCF0EQBEEQWhQRvAiCIAiC0KKI4EUQBEEQhBZFBC+CIAiCILQoIng5QUVFBZMnT8ZkMhEYGMhtt91GbW3tKa8ZOnQoMpnM5+fOO++8QD1ued5++20SExPRarX07duXTZs2nbL9d999R1paGlqtls6dO/Prr79eoJ62Hmdzz+fMmdPo9azVai9gb1u2VatWcc011xAdHY1MJmPBggWnvWbFihX06NEDjUZDmzZtmDNnznnvZ2tytvd8xYoVjV7jMpmMoqKiC9PhFu6FF16gd+/eGI1GwsPDGTNmDAcOHDjtdef6vVwELyeYPHkye/fuZcmSJfz888+sWrWKGTNmnPa66dOnU1hY2PDz8ssvX4DetjzffPMNDz30EE899RTbtm2ja9euDB8+nJKSEr/t161bx8SJE7ntttvYvn07Y8aMYcyYMezZs+cC97zlOtt7Dt6U3ie+no8cOXIBe9yy1dXV0bVrV95+++0zap+VlcVVV13FpZdeyo4dO3jwwQe5/fbbWbx48Xnuaetxtvf8mAMHDvi8zsPDw89TD1uXlStXcs8997BhwwaWLFmC0+nkyiuvpK6urslrzst7uSRIkiRJ+/btkwBp8+bNDccWLlwoyWQyKT8/v8nrhgwZIj3wwAMXoIctX58+faR77rmn4d9ut1uKjo6WXnjhBb/tb7zxRumqq67yOda3b1/pjjvuOK/9bE3O9p7Pnj1bMpvNF6h3rRsgzZ8//5Rt/v73v0sdO3b0OTZ+/Hhp+PDh57FnrdeZ3PPly5dLgFRZWXlB+tTalZSUSIC0cuXKJtucj/dyMfJy1Pr16wkMDKRXr14Nx4YNG4ZcLmfjxo2nvPaLL74gNDSUTp068dhjj2G1Ws93d1sch8PB1q1bGTZsWMMxuVzOsGHDWL9+vd9r1q9f79MeYPjw4U22F3z9kXsOUFtbS0JCAnFxcYwePZq9e/deiO7+JYnXePPp1q0bUVFRXHHFFaxdu7a5u9NiVVdXAxAcHNxkm/PxOm91VaX/qKKiokbDhkqlkuDg4FPOhU6aNImEhASio6PZtWsXjz76KAcOHGDevHnnu8stSllZGW63m4iICJ/jERER7N+/3+81RUVFftuLuekz80fueWpqKh9//DFdunShurqaV155hQEDBrB3795zXq1daPo1brFYsNls6HS6ZupZ6xUVFcW7775Lr169sNvtfPjhhwwdOpSNGzfSo0eP5u5ei+LxeHjwwQe55JJL6NSpU5Ptzsd7easPXmbOnMlLL710yjbp6el/+PFPXBPTuXNnoqKiuPzyy8nMzCQlJeUPP64gNIf+/fvTv3//hn8PGDCA9u3b89577/Hss882Y88E4dxITU0lNTW14d8DBgwgMzOT1157jc8++6wZe9by3HPPPezZs4c1a9Zc8N/d6oOXhx9+mGnTpp2yTXJyMpGRkY0WMbpcLioqKoiMjDzj39e3b18ADh06JIKXE4SGhqJQKCguLvY5Xlxc3OT9jYyMPKv2gq8/cs9PplKp6N69O4cOHTofXfzLa+o1bjKZxKjLBdSnT59m+QBuye69996GjS2nG5U9H+/lrX7NS1hYGGlpaaf8UavV9O/fn6qqKrZu3dpw7bJly/B4PA0ByZnYsWMH4B2aFI5Tq9X07NmTpUuXNhzzeDwsXbrU55v+ifr37+/THmDJkiVNthd8/ZF7fjK3283u3bvF6/k8Ea/xi8OOHTvEa/wMSZLEvffey/z581m2bBlJSUmnvea8vM7/8FLfVmjEiBFS9+7dpY0bN0pr1qyR2rZtK02cOLHhfF5enpSamipt3LhRkiRJOnTokPTMM89IW7ZskbKysqQffvhBSk5OlgYPHtxcT+Gi9vXXX0sajUaaM2eOtG/fPmnGjBlSYGCgVFRUJEmSJN18883SzJkzG9qvXbtWUiqV0iuvvCKlp6dLTz31lKRSqaTdu3c311Nocc72nj/99NPS4sWLpczMTGnr1q3ShAkTJK1WK+3du7e5nkKLUlNTI23fvl3avn27BEivvvqqtH37dunIkSOSJEnSzJkzpZtvvrmh/eHDhyW9Xi898sgjUnp6uvT2229LCoVCWrRoUXM9hRbnbO/5a6+9Ji1YsEA6ePCgtHv3bumBBx6Q5HK59PvvvzfXU2hR7rrrLslsNksrVqyQCgsLG36sVmtDmwvxXi6ClxOUl5dLEydOlAICAiSTySTdcsstUk1NTcP5rKwsCZCWL18uSZIk5eTkSIMHD5aCg4MljUYjtWnTRnrkkUek6urqZnoGF78333xTio+Pl9RqtdSnTx9pw4YNDeeGDBkiTZ061af9t99+K7Vr105Sq9VSx44dpV9++eUC97jlO5t7/uCDDza0jYiIkEaNGiVt27atGXrdMh3bhnvyz7F7PHXqVGnIkCGNrunWrZukVqul5ORkafbs2Re83y3Z2d7zl156SUpJSZG0Wq0UHBwsDR06VFq2bFnzdL4F8nevAZ/X7YV4L5cd7YwgCIIgCEKL0OrXvAiCIAiC0LqI4EUQBEEQhBZFBC+CIAiCILQoIngRBEEQBKFFEcGLIAiCIAgtigheBEEQBEFoUUTwIgiCIAhCiyKCF0EQBEEQWhQRvAiCIAiC0KKI4EUQBEEQhBZFBC+CIAiCILQo/w/SfWMU9o03NgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> The orange points are scam emails while blue points are not. We see that the orange points typically have low x values and higher y values than the blue points on average."
+      ],
+      "metadata": {
+        "id": "CuVb79lqA9fz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vFBSVF_Bnmeg"
+      },
+      "source": [
+        "### Q5.\n",
+        "\n",
+        "Run a linear regression of $y$ on the first 2,610 principal components of $X$. What is the $R^2$ on the training and test sets? (I used cross validation to determine that 2,610 was approximately optimal, but not all 2,711 components.)\n",
+        "\n",
+        "How does this performance compare to the linear regression?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "reduction = PCA(n_components=2610).fit(X_train)\n",
+        "Z_train = reduction.transform(X_train)\n",
+        "Z_test = reduction.transform(X_test)\n",
+        "\n",
+        "lm_k = LinearRegression().fit(Z_train,y_train)\n",
+        "y_hat_test = lm_k.predict(Z_test)\n",
+        "y_hat_train = lm_k.predict(Z_train)\n",
+        "\n",
+        "print('Train r2: ', r2_score(y_hat_train,y_train) )\n",
+        "print('Test r2: ', r2_score(y_hat_test,y_test) )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4Jm-VBny9Qxk",
+        "outputId": "e7697d32-fdbf-4fe2-9e3e-5ae2ef7971ad"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train r2:  0.692897536096375\n",
+            "Test r2:  0.5873905973217184\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> These values look better as the $R^2$ of the test is not 0 and we see it being clsoer to the training value of 0.692."
+      ],
+      "metadata": {
+        "id": "Ydq1QX_PBQFJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K7zB8UbVnmeg"
+      },
+      "source": [
+        "### Q6.\n",
+        "\n",
+        "Explain briefly in your own words what the advantage is in using the principal components to run this high-dimensional regression, rather than the original data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> Using principal components simplifies the regression by eliminating multi-collinearity. It also reduces complexity by allowing you to select a manageable number of components instead of a huge number of feature combinations. However, the trade-off is that principal components are less interpretable than original features, like individual words."
+      ],
+      "metadata": {
+        "id": "4hkqQU6dBleV"
+      }
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".txt",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file