11.11.2023 18:13

Лабораторная работа 4/input.csv

@@ -0,0 +1,5 @@
+longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value
+-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000
+-118.300000,34.260000,43.000000,1510.000000,310.000000,809.000000,277.000000,3.599000,176500.000000
+-117.810000,33.780000,27.000000,3589.000000,507.000000,1484.000000,495.000000,5.793400,270500.000000
+-118.360000,33.820000,28.000000,67.000000,15.000000,49.000000,11.000000,6.135900,330000.000000

Лабораторная работа 4/input.json

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+[
+  {
+    "score": 0.0009456152645028281,
+    "weight": 1
+  },
+  {
+    "score": 0.00020640167757499364,
+    "weight": 1
+  },
+  {
+    "score": 0,
+    "weight": 1
+  },
+  {
+    "score": 1.6557065217391307,
+    "weight": 1
+  },
+  {
+    "score": 0,
+    "weight": 1
+  },
+  {
+    "score": 0.6066065217391303,
+    "weight": 1
+  },
+  {
+    "score": 0.03126181644071977,
+    "weight": 1
+  },
+  {
+    "score": 0.001253973281817707,
+    "weight": 1
+  }
+]

Лабораторная работа 4/output.json

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+[
+    {
+        "longitude": "-122.050000",
+        "latitude": "37.370000",
+        "housing_median_age": "27.000000",
+        "total_rooms": "3885.000000",
+        "total_bedrooms": "661.000000",
+        "population": "1537.000000",
+        "households": "606.000000",
+        "median_income": "6.608500",
+        "median_house_value": "344700.000000"
+    },
+    {
+        "longitude": "-118.300000",
+        "latitude": "34.260000",
+        "housing_median_age": "43.000000",
+        "total_rooms": "1510.000000",
+        "total_bedrooms": "310.000000",
+        "population": "809.000000",
+        "households": "277.000000",
+        "median_income": "3.599000",
+        "median_house_value": "176500.000000"
+    },
+    {
+        "longitude": "-117.810000",
+        "latitude": "33.780000",
+        "housing_median_age": "27.000000",
+        "total_rooms": "3589.000000",
+        "total_bedrooms": "507.000000",
+        "population": "1484.000000",
+        "households": "495.000000",
+        "median_income": "5.793400",
+        "median_house_value": "270500.000000"
+    },
+    {
+        "longitude": "-118.360000",
+        "latitude": "33.820000",
+        "housing_median_age": "28.000000",
+        "total_rooms": "67.000000",
+        "total_bedrooms": "15.000000",
+        "population": "49.000000",
+        "households": "11.000000",
+        "median_income": "6.135900",
+        "median_house_value": "330000.000000"
+    }
+]

Лабораторная работа 4/task_1.py

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+import json
+
+
+def task(path) -> float:
+    with open(path, 'r') as f:
+        data = json.load(f)
+
+    sum_of_products = sum([element["score"] * element["weight"] for element in data])
+
+    return round(sum_of_products, 3)
+
+
+print(task("input.json"))

Лабораторная работа 4/task_2.py

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+import csv
+import json
+
+
+INPUT_FILENAME = "input.csv"
+OUTPUT_FILENAME = "output.json"
+
+
+def task() -> None:
+    data = []
+    with open(INPUT_FILENAME, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            data.append(row)
+
+    with open(OUTPUT_FILENAME, 'w') as f:
+        json.dump(data, f, indent=4)
+
+
+if __name__ == '__main__':
+    # Нужно для проверки
+    task()
+
+    with open(OUTPUT_FILENAME) as output_f:
+        for line in output_f:
+            print(line, end="")