diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..53999c5 Binary files /dev/null and b/.DS_Store differ diff --git a/Jeff - Exercises-1.ipynb b/Jeff - Exercises-1.ipynb new file mode 100644 index 0000000..a294fc8 --- /dev/null +++ b/Jeff - Exercises-1.ipynb @@ -0,0 +1,2199 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "css = open('style-table.css').read() + open('style-notebook.css').read()\n", + "HTML(''.format(css))" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titleyear
0Somewhere in the NYC2017
1Des hommes et des dieux2010
\n", + "
" + ], + "text/plain": [ + " title year\n", + "0 Somewhere in the NYC 2017\n", + "1 Des hommes et des dieux 2010" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None, encoding='utf-8')\n", + "titles.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "t = titles.tail(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titleyearnametypecharactern
0Suuri illusioni1985Homo $actorGuests22
1Gangsta Rap: The Glockumentary2007Too $hortactorHimselfNaN
2Menace II Society1993Too $hortactorLew-Loc27
3Porndogs: The Adventures of Sadie2009Too $hortactorBosco3
4Stop Pepper Palmer2014Too $hortactorHimselfNaN
\n", + "
" + ], + "text/plain": [ + " title year name type character n\n", + "0 Suuri illusioni 1985 Homo $ actor Guests 22\n", + "1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", + "2 Menace II Society 1993 Too $hort actor Lew-Loc 27\n", + "3 Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n", + "4 Stop Pepper Palmer 2014 Too $hort actor Himself NaN" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n", + "\n", + "cast.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "h = cast.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies are listed in the titles dataframe?" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "212811" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### What are the earliest two films listed in the titles dataframe?" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
209962Miss Jerry1894
57632Reproduction of the Corbett and Fitzsimmons Fight1897
\n", + "
" + ], + "text/plain": [ + " title year\n", + "209962 Miss Jerry 1894\n", + "57632 Reproduction of the Corbett and Fitzsimmons Fight 1897" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles.sort('year').head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies have the title \"Hamlet\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "19" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles[titles.title == 'Hamlet'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies are titled \"North by Northwest\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles[titles.title == 'North by Northwest'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### When was the first movie titled \"Hamlet\" made?" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
174843Hamlet1910
\n", + "
" + ], + "text/plain": [ + " title year\n", + "174843 Hamlet 1910" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hamlet = titles[titles.title == 'Hamlet']\n", + "Hamlet.sort('year').head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### List all of the \"Treasure Island\" movies from earliest to most recent." + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
173619Treasure Island1918
120133Treasure Island1920
207185Treasure Island1934
138319Treasure Island1950
111943Treasure Island1972
144435Treasure Island1973
39995Treasure Island1985
115341Treasure Island1999
\n", + "
" + ], + "text/plain": [ + " title year\n", + "173619 Treasure Island 1918\n", + "120133 Treasure Island 1920\n", + "207185 Treasure Island 1934\n", + "138319 Treasure Island 1950\n", + "111943 Treasure Island 1972\n", + "144435 Treasure Island 1973\n", + "39995 Treasure Island 1985\n", + "115341 Treasure Island 1999" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles[titles.title == 'Treasure Island'].sort('year')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies were made in the year 1950?" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1033" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles[titles.year == 1950])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies were made in the year 1960?" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1423" + ] + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles[titles.year == 1960])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many movies were made from 1950 through 1959?" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12051" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(titles[(titles.year > 1949) & (titles.year < 1960)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### In what years has a movie titled \"Batman\" been released?" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
125067Batman1943
141537Batman1989
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" + ], + "text/plain": [ + " title year\n", + "125067 Batman 1943\n", + "141537 Batman 1989" + ] + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles[titles.title == 'Batman']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles were there in the movie \"Inception\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
0Suuri illusioni1985Homo $actorGuests22
\n", + "
" + ], + "text/plain": [ + " title year name type character n\n", + "0 Suuri illusioni 1985 Homo $ actor Guests 22" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "72" + ] + }, + "execution_count": 246, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.title == 'Inception'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles in the movie \"Inception\" are NOT ranked by an \"n\" value?" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = cast[cast.title == 'Inception']\n", + "len(c[c.n.isnull()])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### But how many roles in the movie \"Inception\" did receive an \"n\" value?" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "51" + ] + }, + "execution_count": 248, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(c[c.n.notnull()])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Display the cast of \"North by Northwest\" in their correct \"n\"-value order, ignoring roles that did not earn a numeric \"n\" value." + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "nw = cast[cast.title == 'North by Northwest']" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "nw = nw[nw.n.notnull()]" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
768520North by Northwest1959Cary GrantactorRoger O. Thornhill1
3064038North by Northwest1959Eva Marie SaintactressEve Kendall2
1284677North by Northwest1959James MasonactorPhillip Vandamm3
2758169North by Northwest1959Jessie Royce LandisactressClara Thornhill4
313200North by Northwest1959Leo G. CarrollactorThe Professor5
2667606North by Northwest1959Josephine HutchinsonactressMrs. Townsend6
1495479North by Northwest1959Philip OberactorLester Townsend7
1123585North by Northwest1959Martin LandauactorLeonard8
2153857North by Northwest1959Adam WilliamsactorValerian9
1597034North by Northwest1959Edward PlattactorVictor Larrabee10
586825North by Northwest1959Robert EllensteinactorLicht11
2020534North by Northwest1959Les TremayneactorAuctioneer12
408497North by Northwest1959Philip CoolidgeactorDr. Cross13
1330774North by Northwest1959Patrick McVeyactorSergeant Flamm14
179711North by Northwest1959Edward BinnsactorCaptain Junket15
1220657North by Northwest1959Ken LynchactorCharley - Chicago Policeman16
\n", + "
" + ], + "text/plain": [ + " title year name type \\\n", + "768520 North by Northwest 1959 Cary Grant actor \n", + "3064038 North by Northwest 1959 Eva Marie Saint actress \n", + "1284677 North by Northwest 1959 James Mason actor \n", + "2758169 North by Northwest 1959 Jessie Royce Landis actress \n", + "313200 North by Northwest 1959 Leo G. Carroll actor \n", + "2667606 North by Northwest 1959 Josephine Hutchinson actress \n", + "1495479 North by Northwest 1959 Philip Ober actor \n", + "1123585 North by Northwest 1959 Martin Landau actor \n", + "2153857 North by Northwest 1959 Adam Williams actor \n", + "1597034 North by Northwest 1959 Edward Platt actor \n", + "586825 North by Northwest 1959 Robert Ellenstein actor \n", + "2020534 North by Northwest 1959 Les Tremayne actor \n", + "408497 North by Northwest 1959 Philip Coolidge actor \n", + "1330774 North by Northwest 1959 Patrick McVey actor \n", + "179711 North by Northwest 1959 Edward Binns actor \n", + "1220657 North by Northwest 1959 Ken Lynch actor \n", + "\n", + " character n \n", + "768520 Roger O. Thornhill 1 \n", + "3064038 Eve Kendall 2 \n", + "1284677 Phillip Vandamm 3 \n", + "2758169 Clara Thornhill 4 \n", + "313200 The Professor 5 \n", + "2667606 Mrs. Townsend 6 \n", + "1495479 Lester Townsend 7 \n", + "1123585 Leonard 8 \n", + "2153857 Valerian 9 \n", + "1597034 Victor Larrabee 10 \n", + "586825 Licht 11 \n", + "2020534 Auctioneer 12 \n", + "408497 Dr. Cross 13 \n", + "1330774 Sergeant Flamm 14 \n", + "179711 Captain Junket 15 \n", + "1220657 Charley - Chicago Policeman 16 " + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nw.sort('n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Display the entire cast, in \"n\"-order, of the 1972 film \"Sleuth\"." + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sl = cast[(cast.title == 'Sleuth') & (cast.year == 1972)]" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
1504207Sleuth1972Laurence OlivieractorAndrew Wyke1
286557Sleuth1972Michael CaineactorMilo Tindle2
328520Sleuth1972Alec CawthorneactorInspector Doppler3
1291935Sleuth1972John (II) MatthewsactorDetective Sergeant Tarrant4
2391225Sleuth1972Eve (III) ChanningactressMarguerite Wyke5
1277367Sleuth1972Teddy MartinactorPolice Constable Higgs6
\n", + "
" + ], + "text/plain": [ + " title year name type \\\n", + "1504207 Sleuth 1972 Laurence Olivier actor \n", + "286557 Sleuth 1972 Michael Caine actor \n", + "328520 Sleuth 1972 Alec Cawthorne actor \n", + "1291935 Sleuth 1972 John (II) Matthews actor \n", + "2391225 Sleuth 1972 Eve (III) Channing actress \n", + "1277367 Sleuth 1972 Teddy Martin actor \n", + "\n", + " character n \n", + "1504207 Andrew Wyke 1 \n", + "286557 Milo Tindle 2 \n", + "328520 Inspector Doppler 3 \n", + "1291935 Detective Sergeant Tarrant 4 \n", + "2391225 Marguerite Wyke 5 \n", + "1277367 Police Constable Higgs 6 " + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sl.sort('n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Now display the entire cast, in \"n\"-order, of the 2007 version of \"Sleuth\"." + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sl2 = cast[(cast.title == 'Sleuth') & (cast.year == 2007)]" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
286558Sleuth2007Michael CaineactorAndrew1
1139941Sleuth2007Jude LawactorMilo2
1592307Sleuth2007Harold PinteractorMan on T.V.3
227168Sleuth2007Kenneth BranaghactorOther Man on T.V.NaN
328521Sleuth2007Alec (II) CawthorneactorInspector DopplerNaN
2391224Sleuth2007Eve (II) ChanningactressMarguerite WykeNaN
2939217Sleuth2007Carmel O'SullivanactressMaggieNaN
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" + ], + "text/plain": [ + " title year name type character n\n", + "286558 Sleuth 2007 Michael Caine actor Andrew 1\n", + "1139941 Sleuth 2007 Jude Law actor Milo 2\n", + "1592307 Sleuth 2007 Harold Pinter actor Man on T.V. 3\n", + "227168 Sleuth 2007 Kenneth Branagh actor Other Man on T.V. NaN\n", + "328521 Sleuth 2007 Alec (II) Cawthorne actor Inspector Doppler NaN\n", + "2391224 Sleuth 2007 Eve (II) Channing actress Marguerite Wyke NaN\n", + "2939217 Sleuth 2007 Carmel O'Sullivan actress Maggie NaN" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sl2.sort('n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles were credited in the silent 1921 version of Hamlet?" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ham = cast[(cast.title == \"Hamlet\") & (cast.year == 1921)]" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ham.n.count()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles were credited in Branagh’s 1996 Hamlet?" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bham = cast[(cast.title == \"Hamlet\") & (cast.year == 1996)]" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "55" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(bham)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many \"Hamlet\" roles have been listed in all film credits through history?" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "81" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.character == 'Hamlet'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many people have played an \"Ophelia\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "96" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.character == 'Ophelia'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many people have played a role called \"The Dude\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 262, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.character == 'The Dude'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many people have played a role called \"The Stranger\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "190" + ] + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.character == 'The Stranger'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles has Sidney Poitier played throughout his career?" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "43" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.name == 'Sidney Poitier'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles has Judi Dench played?" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "51" + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[cast.name == 'Judi Dench'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### List the supporting roles (having n=2) played by Cary Grant in the 1940s, in order by year." + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cg = cast[(cast.name == 'Cary Grant') & (cast.n == 2)]" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cg = cg[(cg.year > 1939) & (cg.year < 1950)]" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
768517My Favorite Wife1940Cary GrantactorNick2
768527Penny Serenade1941Cary GrantactorRoger Adams2
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" + ], + "text/plain": [ + " title year name type character n\n", + "768517 My Favorite Wife 1940 Cary Grant actor Nick 2\n", + "768527 Penny Serenade 1941 Cary Grant actor Roger Adams 2" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### List the leading roles that Cary Grant played in the 1940s in order by year." + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cg2 = cast[(cast.name == 'Cary Grant') & (cast.n == 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/.direnv/python-3.4.3/lib/python3.4/site-packages/pandas/core/frame.py:1815: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " \"DataFrame index.\", UserWarning)\n" + ] + } + ], + "source": [ + "cg2 = cg2[(cast.year > 1939) & (cast.year < 1950)]" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
768542The Howards of Virginia1940Cary GrantactorMatt Howard1
768499His Girl Friday1940Cary GrantactorWalter Burns1
768544The Philadelphia Story1940Cary GrantactorC. K. Dexter Haven1
768532Suspicion1941Cary GrantactorJohnnie1
768546The Talk of the Town1942Cary GrantactorLeopold Dilg1
768523Once Upon a Honeymoon1942Cary GrantactorPatrick 'Pat' O'Toole1
768490Destination Tokyo1943Cary GrantactorCapt. Cassidy1
768515Mr. Lucky1943Cary GrantactorJoe Adams1
768516Mr. Lucky1943Cary GrantactorJoe Bascopolous1
768524Once Upon a Time1944Cary GrantactorJerry Flynn1
768482Arsenic and Old Lace1944Cary GrantactorMortimer Brewster1
768519None But the Lonely Heart1944Cary GrantactorErnie Mott1
768518Night and Day1946Cary GrantactorCole Porter1
768521Notorious1946Cary GrantactorDevlin1
768538The Bachelor and the Bobby-Soxer1947Cary GrantactorDick1
768539The Bishop's Wife1947Cary GrantactorDudley1
768514Mr. Blandings Builds His Dream House1948Cary GrantactorJim Blandings1
768494Every Girl Should Be Married1948Cary GrantactorDr. Madison Brown1
768503I Was a Male War Bride1949Cary GrantactorCapt. Henri Rochard1
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K. Dexter Haven 1 \n", + "768532 Johnnie 1 \n", + "768546 Leopold Dilg 1 \n", + "768523 Patrick 'Pat' O'Toole 1 \n", + "768490 Capt. Cassidy 1 \n", + "768515 Joe Adams 1 \n", + "768516 Joe Bascopolous 1 \n", + "768524 Jerry Flynn 1 \n", + "768482 Mortimer Brewster 1 \n", + "768519 Ernie Mott 1 \n", + "768518 Cole Porter 1 \n", + "768521 Devlin 1 \n", + "768538 Dick 1 \n", + "768539 Dudley 1 \n", + "768514 Jim Blandings 1 \n", + "768494 Dr. Madison Brown 1 \n", + "768503 Capt. Henri Rochard 1 " + ] + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cg2.sort('year')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles were available for actors in the 1950s?" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "act = cast[(cast.type == 'actor') & (cast.year // 10 == 195)]" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "147404" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(act)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles were avilable for actresses in the 1950s?" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "wom = cast[(cast.type == 'actress') & (cast.year // 10 == 195)]" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "53793" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(wom)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many leading roles (n=1) were available from the beginning of film history through 1980?" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "61285" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[(cast.n == 1) & (cast.year < 1981)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many non-leading roles were available through from the beginning of film history through 1980?" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1044310" + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[(cast.n != 1) & (cast.year < 1981)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many roles through 1980 were minor enough that they did not warrant a numeric \"n\" rank?" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "413378" + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cast[(cast.n.isnull()) & (cast.year < 1981)])" + ] + } + ], + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Jeff - Exercises-2.ipynb b/Jeff - Exercises-2.ipynb new file mode 100644 index 0000000..af3a3f5 --- /dev/null +++ b/Jeff - Exercises-2.ipynb @@ -0,0 +1,1380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "css = open('style-table.css').read() + open('style-notebook.css').read()\n", + "HTML(''.format(css))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
0Somewhere in the NYC2017
1Des hommes et des dieux2010
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" + ], + "text/plain": [ + " title year\n", + "0 Somewhere in the NYC 2017\n", + "1 Des hommes et des dieux 2010" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None)\n", + "titles.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titles.year.value_counts().sort_index().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cast' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcharacter\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Kermit the Frog'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'scatter'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'cast' is not defined" + ] + } + ], + "source": [ + "c = cast\n", + "c = c[c.character == 'Kermit the Frog']\n", + "c.plot(x='year', y='n', kind = 'scatter')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'c' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'n'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'c' is not defined" + ] + } + ], + "source": [ + "c.head()[['year', 'n']]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyearnametypecharactern
0Suuri illusioni1985Homo $actorGuests22
1Gangsta Rap: The Glockumentary2007Too $hortactorHimselfNaN
2Menace II Society1993Too $hortactorLew-Loc27
3Porndogs: The Adventures of Sadie2009Too $hortactorBosco3
4Stop Pepper Palmer2014Too $hortactorHimselfNaN
5Townbiz2010Too $hortactorHimselfNaN
6For Thy Love 22009Bee Moe $limactorThug 1NaN
7Desire (III)2014Syaiful 'AriffinactorActor Playing Eteocles from 'Antigone'NaN
8When the Man Went South2014Taipaleti 'Atu'akeactorTwo Palms - Ua'i Paame8
9Little Angel (Angelita)2015Michael 'babeepower' VieraactorChico9
10Mixing Nia1998Michael 'babeepower' VieraactorRapperNaN
11The Replacements2000Steven 'Bear'BoydactorDefensive Tackle - Washington SentinelsNaN
12Dysfunktion2015Kirlew 'bliss' VilbonactorBlissNaN
13My Song for You2010George 'Bootsy' ThomasactorCooley's Customer16
14My Song for You2010George 'Bootsy' ThomasactorCelebration Guest16
15B-Girl2009Jesse 'Casper' BrownactorBattle Judge25
16Battle of the Year2013Jesse 'Casper' BrownactorRebel9
17Kickin' It Old Skool2007Jesse 'Casper' BrownactorCole10
18Step Up All In2014Jesse 'Casper' BrownactorGrim Knight Dancer61
19The LXD: The Secrets of the Ra2011Jesse 'Casper' BrownactorFangzNaN
\n", + "
" + ], + "text/plain": [ + " title year name \\\n", + "0 Suuri illusioni 1985 Homo $ \n", + "1 Gangsta Rap: The Glockumentary 2007 Too $hort \n", + "2 Menace II Society 1993 Too $hort \n", + "3 Porndogs: The Adventures of Sadie 2009 Too $hort \n", + "4 Stop Pepper Palmer 2014 Too $hort \n", + "5 Townbiz 2010 Too $hort \n", + "6 For Thy Love 2 2009 Bee Moe $lim \n", + "7 Desire (III) 2014 Syaiful 'Ariffin \n", + "8 When the Man Went South 2014 Taipaleti 'Atu'ake \n", + "9 Little Angel (Angelita) 2015 Michael 'babeepower' Viera \n", + "10 Mixing Nia 1998 Michael 'babeepower' Viera \n", + "11 The Replacements 2000 Steven 'Bear'Boyd \n", + "12 Dysfunktion 2015 Kirlew 'bliss' Vilbon \n", + "13 My Song for You 2010 George 'Bootsy' Thomas \n", + "14 My Song for You 2010 George 'Bootsy' Thomas \n", + "15 B-Girl 2009 Jesse 'Casper' Brown \n", + "16 Battle of the Year 2013 Jesse 'Casper' Brown \n", + "17 Kickin' It Old Skool 2007 Jesse 'Casper' Brown \n", + "18 Step Up All In 2014 Jesse 'Casper' Brown \n", + "19 The LXD: The Secrets of the Ra 2011 Jesse 'Casper' Brown \n", + "\n", + " type character n \n", + "0 actor Guests 22 \n", + "1 actor Himself NaN \n", + "2 actor Lew-Loc 27 \n", + "3 actor Bosco 3 \n", + "4 actor Himself NaN \n", + "5 actor Himself NaN \n", + "6 actor Thug 1 NaN \n", + "7 actor Actor Playing Eteocles from 'Antigone' NaN \n", + "8 actor Two Palms - Ua'i Paame 8 \n", + "9 actor Chico 9 \n", + "10 actor Rapper NaN \n", + "11 actor Defensive Tackle - Washington Sentinels NaN \n", + "12 actor Bliss NaN \n", + "13 actor Cooley's Customer 16 \n", + "14 actor Celebration Guest 16 \n", + "15 actor Battle Judge 25 \n", + "16 actor Rebel 9 \n", + "17 actor Cole 10 \n", + "18 actor Grim Knight Dancer 61 \n", + "19 actor Fangz NaN " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n", + "cast.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### What are the ten most common movie names of all time?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Hamlet 19\n", + "Macbeth 14\n", + "Carmen 14\n", + "The Three Musketeers 12\n", + "The Outsider 11\n", + "Maya 11\n", + "She 11\n", + "Blood Money 11\n", + "The Promise 10\n", + "Anna Karenina 10\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles.title.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of films that have been released each decade over the history of cinema." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(titles.year // 10 *10).value_counts().sort_index().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of \"Hamlet\" films made each decade." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4jO7VMR/bzA0pvwKXdAvdPO8PgH8CnA7cBFxFN2d791ZoyNKRoSFLR4aGLB0Z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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ham = titles[titles.title == 'Hamlet']\n", + "(ham.year // 10 *10).value_counts().sort_index().plot(kind='bar')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ham" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of \"Rustler\" characters in each decade of the history of film." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/cAQs4JeasCYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rust = cast[cast.character == 'Rustler']\n", + "(rust.year // 10 *10).value_counts().sort_index().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of \"Batman\" characters each decade." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RV5jEwJYkWYlIO8JKpPAoZhr1POwj0naeggVs44Nm+/jDVJqeSVQidfrG3OTj+oLP2e7f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\nAg5sSZoIB7YkTYQDW5ImwoEtSRPx/wFqFj0Qe5mJFAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bat = cast[cast.character == 'Batman']\n", + "(bat.year // 10 * 10).value_counts().sort_index().plot(kind=\"bar\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### What are the 11 most common character names in movie history?" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Himself 18819\n", + "Dancer 10952\n", + "Extra 8684\n", + "Reporter 7563\n", + "Doctor 6749\n", + "Policeman 6443\n", + "Student 6339\n", + "Nurse 6108\n", + "Bartender 6083\n", + "Minor Role 5793\n", + "Party Guest 5765\n", + "dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast.character.value_counts().head(11)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Who are the 10 people most often credited as \"Herself\" in film history?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "her = cast[cast.character == 'Herself']" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Joyce Brothers 14\n", + "Queen Elizabeth II 11\n", + "Margaret Thatcher 8\n", + "Mary Jo Pehl 7\n", + "Joan Rivers 7\n", + "Juhi Chawla 5\n", + "Kareena Kapoor 5\n", + "Sally Jessy Raphael 5\n", + "Bunny Yeager 5\n", + "Chris Evert 5\n", + "dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "her.name.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Who are the 10 people most often credited as \"Himself\" in film history?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "him = cast[cast.character == 'Himself']" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Adolf Hitler 92\n", + "Richard Nixon 38\n", + "Ronald Reagan 30\n", + "John F. Kennedy 26\n", + "Ron Jeremy 23\n", + "Winston Churchill 21\n", + "Bill Clinton 20\n", + "Franklin D. Roosevelt 20\n", + "George W. Bush 20\n", + "Martin Luther King 19\n", + "dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "him.name.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Which actors or actresses appeared in the most movies in the year 1945?" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aora = cast[cast.year == 1945]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Emmett Vogan 39\n", + "Sam (II) Harris 30\n", + "Harold Miller 28\n", + "Bess Flowers 28\n", + "Nolan Leary 27\n", + "Frank O'Connor 26\n", + "Charles Sullivan 24\n", + "Pierre Watkin 24\n", + "Franklyn Farnum 24\n", + "Tom London 24\n", + "dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aora.name.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Which actors or actresses appeared in the most movies in the year 1985?" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aora = cast[cast.year == 1985]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Shakti Kapoor 19\n", + "Mammootty 17\n", + "Sukumari 16\n", + "Lou Scheimer 15\n", + "Aruna Irani 14\n", + "Deven Verma 13\n", + "Rajesh Khanna 13\n", + "Raj Babbar 13\n", + "Mohanlal 13\n", + "Asrani 12\n", + "dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aora.name.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot how many roles Mammootty has played in each year of his career." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mam = cast[cast.name == 'Mammootty']" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SAcxYevCwpkZ80NWYCibqVaAyBXgR2QA4SFVnAKjqalWNJgDF5JFW06EHH6wH\n32Xt9Qow2k3uKYTI2msyAwF+q82BbXJqm3YD/LFwwwEkA5JR4el9nAMcXeTz2IysPfiJwHMiMlNE\n/iQiPxGRaH2oEtA1lSRD46awv0LyodfTuNWydiJZ7BxY+ibwHPDeHG4/nqFlCmr5EzAMXr5XlVdz\n0JQ7qvwFeBZoe5JZvUVRQpE1wK8N7AVcrKp7Aa8BX6k9SERmici57uf06k9MEZkSaltVbwp5/azb\nNW2T5vxN4MJNrb0G9q2Aw48oSl8s7QWfOxF4RJXXq157CNgh9P1h9k5w2thmx4McDFwEH70whvby\n+PdYuz0bLjulneNF+CBwX/Xr7vdZ7udcPCKq6dNSRWRz4HZVnei2DwS+oqpHVR2jqhp9MZ5uQITb\ngTNV/S463K2IsAg4QbXSc+1NRPgiMFmVv6na90PgL6p8N/C9HwGOUuWhkPfpBkTYF5ilOrhMcp3j\n1gHuBSYBo1R5o/5x/mJnph68qj4DLBWR7dyuQ2lcHjR3aj+dYyGLLjfFO2hRoy5sr0IzaSJqr30Y\nyKAZ0BV8spN7Jtvx4N3x0bTXIDzqWgBsKMKkFsedSZKM8meStNHgdJJFcwrwCxFZRJJFc74fSUYN\nla/BzxeqIi4KT5WMhKoB1gHymM06BnhXtbuqm4bCjQvNpUk2jQjbAKcBp5KsXZvHOEl2s19VF5H0\nIKIjsjzlATLq2h54KOQU7y5sr0Jns8bQXm5yzbbA4sq+ZGyArQifC9/OAOsAMbRXPTzrmk0yDvlv\ntS+4bzw/Ar6pyhMi9AN9Hu/dEJvJGj/bg/mcNVgPHvYElriaKNU8CYwRCZpltAVt2jM9xB+A3UV4\nT53XPg5MgIFxkdx68KUM8CXz/IIvKtCF7WUefB17RkSmOLvgYcLaNG377xBNew3Bpy5V3iRZSHxQ\n8TH3Qftd4IuqrHK7LcAbA+yA9eBrsdms9f33CqF9+FQBvoeoVyP+a8B1NRlw/ZhFk52SeX7BLZou\nbK9CF/2IpL0GZdDAIF0PEtaHTxXgI2mvIQTQdTVwqAjrAoiwB8m6smfWHGc9eAPcIsVbkaRVGWvo\naQ/efe3fEri/wSF59ODbHmTtFVR5nmRW8SFuHeVLgK+q8lzNocuATd3fd1BKGeBL5PltAyytM5Dm\nlS5sr1734PcGFtVWZ6yZzRqNRRNBe9UlkK5Kjfi/BRS4rPYA9749TfIhHZTcaiIYmbAMmvoUvuhH\nwTTz3yG4qvlIAAASQ0lEQVQZZJ0kwrBAC7VbFk1j5gB/BD4CHOYGvetRsWn+ElJMKXvwJfL8cgnw\nXdheK4CNs15XhP/bIJ2tnXNPBV2e9d6eqBvgK+3lCns9T4DZks56GEfSA22LLny+OrgmjwAvAD9T\nZVGTQ/vJYaC1lAG+RARPkexSniDxMEenPdEtSn0u8M0M5+4LfA/4fNpzPTNkgLUOoWyaTYBXXFqg\nUZ+pwDktjslloLWUAb5Enl8uKZLd1l6qvA3cRzLZJy17kwyEHS7Cge1rYW3gUuDfYP7/crMTc0eE\nTYD3kNgwNa8Naq9QAT51imS3PV+dosoTbYyb9WM9+J7HPPjG3EW2UhmTgZuAM4BLRBje5nlfIrGG\nzgQZBs0rBwZkb+BPTbzdCg8RJlXSMmj8YD34rJTB83Me8TCSxQSC0qXttYAkWKelYm/8hiRQnd7q\nBBHGA/8MfCmpCXTEf5JkShRBQ3umpr1CLd+Xugffpc9XaPqxHnxPsz3wYMgiY11O1gA/GVjg2vXv\ngLNcga5mfAf4serAeEi9GYt50SqDpkIoi8YyaPywFBgfeqm/Ugb4knh+uZUo6NL2egAY5wZN27we\nY0ny5x8FUOVR4Afup9E5h5Esx/Yva/ZuBLC9COPavbdHqhfZHkRNey0FNhJhfc/3Nw/eA24c6XmS\n9gxGKQN8STD/vQkuv3shiSfdLpOBu2v86wuBnUQ4uvZgN+X8YuAUVV5f88qK1cB8GHpOSNwHykja\nyJ12/8dH8N+Ltzo0/ugnsE1TygBfEs8vtxTJLm6vtDbNEHvDZTt8CfihCKNqjj8LWKzK7+voKsKm\n2Zs19tIQ6rRXCB8+9SBrFz9foQk+0NpRgBeRYSJyj4jM9SXIGMCqSLYmbSbNZOoMUKpyPXA78E+V\nfSJsS7Jq2WkNrjUfeH+WXPwOaGjPNCCED289eH/0E3kP/jSSgkdRDQR2u+fnUvfei/OKQ9PF7ZW2\nB98sQP498Lci7ORy3C8CLlBlaT1dqqwA7gAOS3H/Tqn7AVWhTnt5TZV0cwHGAqlm8nbx8xWaeHvw\nIrIlcCTwUyhm0keJ2RpYFrrIWAl4lGSx47GtDnSpjiNI/qiGoMrTwHkknvuxJD3V77e4bKWwVHDc\nh07aHrxvi2Yz4PnaImdGZvqJNcCTrFLyj9BywkXuROCt1SWFrlxLFHRre7mBxLtprxc/GbirRdrp\nxcD6wEzgC1Ur8DTSNQf4cOhUN8cEkm/KTzY6oE57VYqO+Rpry2TPdOvzlQOPE9iiyVRNUkSOAp5V\n1Xuafc0RkVkkn1KQzAJcWGnUynm2XXd7B5j1mshnp0SiJ9pt0AXAZBF5o9nx8JOPA89Vysg0ud7n\ngENBhovQsv1BlwHvE5FhIf+/8NPzYdQ9qsdpyvZ5CZggIhM713POAfAvy0L8/3pze/w6sGyCyIgP\nwKoTSOjHJ6qa+gc4nyTP9jGSqnKvAf9Rc4xmubaPH2BKUff2oQv0MtCTYtMVY3uBfhx0ThvHzQOd\n7lsX6NdAvxW2HXRH0OdBx6dtL9A/gB7hSccXQS8N8T4W8RODLtDloONqdKmv62f66qaq56jqBFWd\nCHwS+IOqfibLtYy6WBXJ9mk50Or866YDlB0wB5geqviYu+7FwHmqmbJXfPrwlkHjn6ADrb68uaiy\naLR4b60uKXTlOsmpy9vrcWCEG0RtxHuBVRkDZCtdfyKZfBRqBaVPkSxucnGrAxu0l89UyUxlCrr8\n+QpNPwF9+I4DvKrerKpF1eUoHa4c7AhSpqL1KqoorfPhQ/XeK/cPkk0jwkYkdeu/oNkzV3wGeOvB\n+6crevBREUF+a13a1LU98JALHLnQ5e0FrW2atOmFTamjK9Ss1vOB36pyZzsHN2gvn7nwmQJ8CZ6v\nkFiA7zHMf0/PAlr34L0F+DrcBOwswma+LuhWj5pO65WBWvEEsLGnGbdWC94//QS0aMSN2vq/sIiq\nqk2ASokIFwIrVaurFxrNcP77vcDY2m8+Lgf8RWCSKs8F1PBrYL4qMzxca23gTuA7qvzcw/XuBU5U\n5U8dXGMdYCWwnrZebMRoExF2Ba5UZec1+/zFTuvBx4dVkUyJGzx9m/pfdbcFVoQM7o7Z+PPhK6tH\n/cLT9Xz48OOAZyy4e+dxoC9UFlYpA3wk3toQ0njwgaUMosvbq0Ijm8a7PdNA1zzgAyKM7OzatatH\ndawL/PjwmRf6KMnzFQRVVgJvkSxm7p1SBvhuxRUZm0hSx9tIx13UH2gNlkFTjSovknyQHNrhpWpX\nj/KBj1x4y6AJRz+BfPhSBvhI8luH0IauicBTqryZg5wBuri9qmmUSeM1gwaa6uooXbL+6lHt00SX\nD4sm8wBrSZ6vkATLpMlUi8YIhvnv2VkA7C3CWhWf2BUB24OkIFkezAa+KsIY99W7bdzqURcxZPUo\nLzwE7CjStAf+c1XObPK69eDDESzAl7IHH4O3Vo82dBWSItnF7TWAG0RdQTKoWmEH4GlNarcH16XK\nYyS9+K9nuOxZwH1as3qUJ10rSQL05AY/BwHHi/C+JpfPHODL8HwFpp9AFo314ONie/LrbZaRik3z\nsNv2bs+0wVnAEhEubzctUYRJJKtH7RlKlBsjaKbhy8AlIuzdYNZs5kFWoyWPAx8IceFS9uAj8tYG\n0YauQpbp6+L2qqW2ZEGQAdZmulR5nmRy0iXt1IlvtXqUL11t8CvgWeDUBq9n7sGX6PkKRT82yNoT\nmAffGbUDraFnsDZiJrCaSvH55hxLkmPeavWooLiUzJOBc0SYUOcQ8+DDYR58GiLy1gbRfHEUNgbW\nJamvnyvd2F4NuBvYU4S1RRgB7Arck7cuN8j7BeC8ZuULRBhDkhbZcPUon7paocrDJN8mvjf4uqwP\nDANeLkJXKCLS9RIwTIQNfV+4lAG+S8m9yFjZcIOpT5FYXTsDj6nyakFaFgOXA99qctjXgXmq3JqP\nqrb4V2B3EY6s2jeOJH3Xns0AuHbtJ0AvvpQBPiJvbRAtdBVmz3RpezWiYtMEs2dS6PoaMEWEKbUv\niLAXyWI5ZxWgq8k1eJPEqvlR1azcjuyZkj1foQhi05QywHcp5r/7oVKyoIgMmkG4bw+nARc7ywgY\nyM+/BDhblReK0tcIVa4hGZyuVLK0DJrw9BNgoDVzgBeRCSJyo4gsEZH7RKTR6HvuROStDaKFrsLK\nBHdpezWiUrIgWImClLquAv4CfLlq3+eBVcAsf6q8v49nACeJsAMd9uBL9nyFIkgPvpM8+FXAGaq6\nUERGA3eLyHWq+oAnbb1GISmSJeQeksFVSEoIF4oqKsIpwF0iXAm8DpwHfDDmyoyqPCXCN0iWCryX\npK68EY5+YH/fF/VWD15ErgJ+qKo3uG2rB98mrv73q8BGqrxRtJ5uR4QlwNuq4SYOpUWEc4D3kWRM\nPKPKPxYsqSXuubwL2JKkhMKVBUsqLW6Bl4tVmewzdnqZySoifSSz8O7wcb0eZGuSKfUW3P1wF0l9\n+Jj4NrAIGAXsVLCWtlBltQhfAG7HPPjQhMmFV9WOfoDRJINZx9TsVxKP8Vz3czowper1KaG2K7/n\ndb8U26fX16s/gVm/KUpft7VX6/+P7gbHfja+9vrEiaCTY2uvNtrzKBh7eP7tFefzFeh5mgWfXg0j\n/wXQynGd/nRk0YjIcOB3wDxVrZkcUZxFIyJTNK4UKKC+Llfg6T+BnVSzTSQJoSsGTFc6TFc6YtMl\nwoPAR0GW+IqdmQO8iAjJRI4XVPWMOq8XFuC7Bedx3g38q5q/aRg9jQjzgR+A/N5X7OwkD/4A4FPA\nB0TkHvcz1YeoHuJUkgJPvypaiGEYhePdh88c4FX1j6q6lqruoap7up/5PsVlJbL81gGqdbmCTucA\nJ2vBU8C7ob1iwnSlw3S1TTwB3uiY7wEXqQ7ULjcMo7fpx/NsVm958EMubB58Q1whpx8Au2jO668a\nhhEnIhwAfBtkf1+x01Z0yhlXwOlHwBctuBuGUUU/nnvwpbRoIvTWgAFd5wALNCnoFAWRt1d0mK50\nmK62eRrY2OcFrQefKx/ZCjgJ2KNoJYZhxIUq74qwFNjG1zXNg88Jt/bmDcBs1WKXZzMMI05EuAHk\nkK7w4EUYH/L6XcaHgY1IlkQzDMOox+M+LxbaoilowYVrR8DhsRWbegv+5kLVy1YXLaSW2KZsVzBd\n6TBd6YhUl9eS4UEDvGoxPXiRI2J84xCZMQUuK1qGYRiRosqFIlzg63rmwRuGYUSEz9hZyjRJwzAM\no6QBPsL8VsB0pcV0pcN0pSNWXT4pZYA3DMMwzIM3DMOICvPgDcMwjJZkDvAiMlVEHhSRR0TkLJ+i\nOiVWb810pcN0pcN0pSNWXT7JFOBFZBhJRcSpJCvEHyciO/oU1iGx1noxXekwXekwXemIVZc3svbg\n9wUeVdV+VV0FXAlM9yerYzYsWkADTFc6TFc6TFc6YtXljawBfgtgadX2k26fYRiGEQlZA3yha4i2\nQV/RAhrQV7SABvQVLaABfUULaEBf0QIa0Fe0gAb0FS2gAX1FCwhNpjRJEdkfOFdVp7rts4F3VfXC\nqmNi/xAwDMOIEl9pklkD/NokVc8+CDwF3Akcp6oP+BBlGIZhdE6mapKqulpE/g64BhgGXGbB3TAM\nIy6CzWQ1DMMwiqUrZrKKyAwRWS4ii6v27S4it4vIvSIyR0TWd/uPF5F7qn7eEZHd3Gs3uclZldfe\nk6OudUXkCrf/fhH5StU5e4vIYjdprOPl/DzqKrK9RojITLd/oYgcXHVOke3VTJfv9pogIjeKyBIR\nuU9ETnX7NxaR60TkYRG5VkQ2rDrnbNcuD4rI4VX7vbWZZ13e2iytLrf/RhF5RUR+WHOtwtqrha50\n7aWq0f8ABwF7Aour9t0FHOR+/yxwXp3zdgEeqdq+EdirCF3AicAV7vf1gMeArdz2ncC+7vergamR\n6CqyvU4msf4AxgILqs4psr2a6fLdXpsDe7jfR5OMe+0IfBM40+0/C7jA/b4TsBAYTpIh8ihrvqV7\nazPPury1WQZdI4EDgJOAH9Zcq8j2aqYrVXt1RQ9eVW8BXqrZPcntB7ge+FidU/+aZBJWNd4KoKXU\n9TQwSpJZwKOAt4GVIjIOWF9V73TH/QdwTNG6qs4rqr12JHmYUdXngBUisk8E7VVP1+Sq83y21zOq\nutD9/irwAMl8k2nA5e6wy1nz/59O8mG9SlX7SQLpfr7bzJeuqkt6abO0ulT1dVW9FXir+jpFt1cj\nXdUS2713VwT4BiwRkcrs2U8AE+occyxwRc2+y91Xm3/KU5eqXkMSOJ8G+oFvqeoKkjf6yarzlxFm\n0lhaXRUKaS9gETBNRIaJyERgb2BLCm6vBrqqn70g7SUifSTfMu4ANlPV5e6l5cBm7vfxDG6bygTE\n2v3e2qwDXdXLeXpvszZ1VagdiAz2jHWoq0Lb7dXNAf5zwJdEZAHJ155Bi2yLyH7A66p6f9Xu41V1\nF5Kv5AeJyKfz0iUinyKxQMYBE4F/cAEiL7LoKqy9gBkkf2QLgO8CtwHvkN8ku7S6IFB7icho4P8B\np6nqK9WvafK9vZBMCU+6vLeZtdcaujbAq+pDqnqEqk4msWH+XHPIJ4Ff1pzzlPv3VffavjnoetS9\n9D7gt6r6jvtqfytJ7+9Jkp5phS1JegxF6prszimivf7s9r+jqn+vqnuq6jEkdUMeJpl3UUR7tdIV\npL1EZDhJUPiZql7ldi8Xkc3d6+OAZ93+ZQz+NrElyfO1DM9t5kHXMvDfZil1NaLo9mpI2vbq2gAv\nImPdv2sB/wRcUvXaWiRfq6+s2jesMuLsGvtoYDGeqaPrUvfSg8Ah7rVRwP7Ag6r6DIkXv5+ICPBp\n4KohF85X1wMFttclbns9pwcROQxYpaoPqurTFNNeTXWFaC/3/7sMuF9Vv1f10hzgBPf7Caz5/88B\nPilJps9EYBJwp+9nzJcu322WQdfAqdUbvp8xX7oytVe7o7FF/pD46E+RfE1eSvL1+VSS0eiHgPNr\njp8C3FazbyTJ1+pFwH0kX68lL13AOsDP3RuyBPhy1Wt7u/2PAj/Is70a6SIZcC2yvfpIPnzuB64F\nJkTSXnV1BWqvA4F3STJQ7nE/U4GNSQZ+H3YaNqw65xzXLg8CR4RoM1+6fLdZRl39wAvAK+693yGS\n9hqiiwwxzCY6GYZhlJSutWgMwzCM5liANwzDKCkW4A3DMEqKBXjDMIySYgHeMAyjpFiANwzDKCkW\n4A3DMEqKBXjDMIyS8j9/dBvANgaM3AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mam.year.value_counts().sort_index().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### What are the 10 most frequent roles that start with the phrase \"Patron in\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Patron in Frisky Rabbit 16\n", + "Patron in the Coffee House 9\n", + "Patron in Chinese Restaurant 9\n", + "Patron in Billiard Parlor 5\n", + "Patron in Bar 4\n", + "Patron in restaurant 3\n", + "Patron in Restaurant 3\n", + "Patron in cabaret 3\n", + "Patron in Club 3\n", + "Patron in booth 2\n", + "dtype: int64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = cast\n", + "c[c.character.str.startswith('Patron in')].character.value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### What are the 10 most frequent roles that start with the word \"Science\"?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# c[c.character.str.startswith('Science')].character.value_counts().head(10)\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the n-values of the roles that Judi Dench has played over her career." + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "judi = cast\n", + "judi = cast[cast.name == 'Judi Dench'].sort('year')" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "judi = judi[judi.n.notnull()]\n", + "judi.plot(x='year', y='n', kind='scatter')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the n-values of Cary Grant's roles through his career." + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cg = cast[cast.name =='Cary Grant'].sort('year')" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cg = cg[cg.n.notnull()]" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cg.plot(x='year', y='n', kind='scatter')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the n-value of the roles that Sidney Poitier has acted over the years." + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sp = cast[cast.name == 'Sidney Poitier'].sort('year')" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sp = sp[sp.n.notnull()]" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Ps3R1icftu+3DJ+jzspfxjrmstrcfp740t7dvvflaPKX6Mt+HY70HVso2qc9Oh5gqiogN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\nQEiSelkgJEm9/j93PT1iJVA2HgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sp.plot(y = 'n', x='year', kind='scatter')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many leading (n=1) roles were available to actors, and how many to actresses, in the 1950s?" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n1 = cast[(cast.year // 10 == 195) & (cast.n == 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n1.type.value_counts().plot(kind='barh')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many supporting (n=2) roles were available to actors, and how many to actresses, in the 1950s?" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n2 = cast[(cast.year // 10 == 195) & (cast.n == 2)]" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cast.type.value_counts().plot(kind='barh')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Jeff - Exercises-3.ipynb b/Jeff - Exercises-3.ipynb new file mode 100644 index 0000000..29d3d53 --- /dev/null +++ b/Jeff - Exercises-3.ipynb @@ -0,0 +1,646 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "css = open('style-table.css').read() + open('style-notebook.css').read()\n", + "HTML(''.format(css))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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titleyear
0Somewhere in the NYC2017
1Des hommes et des dieux2010
2Beau Jest2008
3Girl of the Sea1920
4The Fruit Machine1988
\n", + "
" + ], + "text/plain": [ + " title year\n", + "0 Somewhere in the NYC 2017\n", + "1 Des hommes et des dieux 2010\n", + "2 Beau Jest 2008\n", + "3 Girl of the Sea 1920\n", + "4 The Fruit Machine 1988" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None)\n", + "titles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titleyearnametypecharactern
0Suuri illusioni1985Homo $actorGuests22
1Gangsta Rap: The Glockumentary2007Too $hortactorHimselfNaN
2Menace II Society1993Too $hortactorLew-Loc27
3Porndogs: The Adventures of Sadie2009Too $hortactorBosco3
4Stop Pepper Palmer2014Too $hortactorHimselfNaN
\n", + "
" + ], + "text/plain": [ + " title year name type character n\n", + "0 Suuri illusioni 1985 Homo $ actor Guests 22\n", + "1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", + "2 Menace II Society 1993 Too $hort actor Lew-Loc 27\n", + "3 Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n", + "4 Stop Pepper Palmer 2014 Too $hort actor Himself NaN" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n", + "cast.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Using groupby(), plot the number of films that have been released each decade in the history of cinema." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "decade = titles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "decade.groupby(decade.year // 10 * 10).size().plot(ylim = 0, kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Use groupby() to plot the number of \"Hamlet\" films made each decade." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "decade = decade[decade.title == 'Hamlet']\n", + "decade.groupby(decade.year // 10 * 10).size().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many leading (n=1) roles were available to actors, and how many to actresses, in each year of the 1950s?" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n1 = cast[(cast.year // 10 * 10 == 1950) & (cast.n == 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year type \n", + "1950 actor 604\n", + " actress 268\n", + "1951 actor 632\n", + " actress 272\n", + "1952 actor 589\n", + " actress 285\n", + "1953 actor 632\n", + " actress 289\n", + "1954 actor 623\n", + " actress 297\n", + "1955 actor 606\n", + " actress 264\n", + "1956 actor 608\n", + " actress 288\n", + "1957 actor 706\n", + " actress 283\n", + "1958 actor 691\n", + " actress 275\n", + "1959 actor 676\n", + " actress 285\n", + "dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n1.groupby(['year', 'type']).size()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### In the 1950s decade taken as a whole, how many total roles were available to actors, and how many to actresses, for each \"n\" number 1 through 5?" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n15 = cast[cast.n < 6]\n", + "n15.groupby(['n', 'type'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Use groupby() to determine how many roles are listed for each of the Pink Panther movies." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "title\n", + "The Pink Panther 108\n", + "The Pink Panther 2 82\n", + "The Pink Panther Strikes Again 73\n", + "dtype: int64" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pink = cast[cast.title.str.startswith('The Pink Panther')]\n", + "pink.groupby(['title']).size()\n", + "#pink.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "1963 15\n", + "2006 50\n", + "Name: n, dtype: float64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pink1 = cast[cast.title == 'The Pink Panther']\n", + "pink1.sort('n').groupby(['year'])['n'].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### List, in order by year, each of the films in which Frank Oz has played more than 1 role." + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "oz = cast[cast.name == 'Frank Oz']" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ozg = oz.groupby(['year', 'title']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year title \n", + "1979 The Muppet Movie 8\n", + "1981 An American Werewolf in London 2\n", + " The Great Muppet Caper 6\n", + "1982 The Dark Crystal 2\n", + "1984 The Muppets Take Manhattan 7\n", + "1985 Sesame Street Presents: Follow that Bird 3\n", + "1992 The Muppet Christmas Carol 7\n", + "1996 Muppet Treasure Island 4\n", + "1999 Muppets from Space 4\n", + " The Adventures of Elmo in Grouchland 3\n", + "dtype: int64" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozg[ozg>1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### List each of the characters that Frank Oz has portrayed at least twice." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "twice = cast[cast.name == 'Frank Oz']" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "twc = twice.groupby(['name', 'character']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "name character \n", + "Frank Oz Grover 2\n", + " Bert 3\n", + " Cookie Monster 3\n", + " Fozzie Bear 4\n", + " Sam the Eagle 5\n", + " Yoda 5\n", + " Animal 6\n", + " Miss Piggy 6\n", + "dtype: int64" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "twc[twc>1].order()" + ] + } + ], + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Jeff - Exercises-4.ipynb 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" + ], + "text/plain": [ + " title year name type character n\n", + "0 Suuri illusioni 1985 Homo $ actor Guests 22\n", + "1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", + "2 Menace II Society 1993 Too $hort actor Lew-Loc 27" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n", + "cast.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Define a year as a \"Superman year\" whose films feature more Superman characters than Batman. How many years in film history have been Superman years?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bs = cast[(cast.character == 'Superman') | (cast.character == 'Batman')] # .set_index('year').sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bs = bs.groupby(['year', 'character']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bs = bs.unstack()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bs = bs.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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characterBatmanSuperman
year
193810
194010
194310
194801
194920
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" + ], + "text/plain": [ + "character Batman Superman\n", + "year \n", + "1938 1 0\n", + "1940 1 0\n", + "1943 1 0\n", + "1948 0 1\n", + "1949 2 0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Superman Years\n" + ] + }, + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sup = bs[bs.Superman > bs.Batman]\n", + "print('Superman Years')\n", + "len(sup)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### How many years have been \"Batman years\", with more Batman characters than Superman characters?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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characterBatmanSuperman
year
193810
194010
194310
194801
194920
\n", + "
" + ], + "text/plain": [ + "character Batman Superman\n", + "year \n", + "1938 1 0\n", + "1940 1 0\n", + "1943 1 0\n", + "1948 0 1\n", + "1949 2 0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batman Years\n" + ] + }, + { + "data": { + "text/plain": [ + "24" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bat = bs[bs.Batman > bs.Superman]\n", + "print('Batman Years')\n", + "len(bat)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of actor roles each year and the number of actress roles each year over the history of film." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aa = cast\n", + "aa = aa.groupby(['year', 'type']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aa = aa.unstack()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bC9X+KBatNLUg0WDGhqrqqJKqXFVpwl1VjuP0NBI7A/cC21WSEVcF7Qn8yppt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\nZwlDi6tt69TqrkvnZxR7GKe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aa.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the number of actor roles each year and the number of actress roles each year, but this time as a kind='area' plot." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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zawKQ9CvgLcBaSaPMbG10Q62P+VcDB2Tdvz+hp7GaPafjZ9Iz9xwIvBzdYYNj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\nPZvsuyuG5nObD8d1HKfqicu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aa.plot(kind='area') #this doesn't seem right as actors should be the larger according to above" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the difference between the number of actor roles each year and the number of actress roles each year over the history of film." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dif = aa.actor - aa.actress\n", + "dif.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the fraction of roles that have been 'actor' roles each year in the hitsory of film." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# no" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot the fraction of supporting (n=2) roles that have been 'actor' roles each year in the history of film." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# no" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Build a plot with a line for each rank n=1 through n=3, where the line shows what fraction of that rank's roles were 'actor' roles for each year in the history of film." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3nr2NJxV2U+iptirYKmoZT79J6+OGvyrcFL53DP72wWoZTqNLUZ6ltlD8SLWV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\nfgipBfLh78BJIjSIsAz2wPi0GH11HMc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# copy Brandon's solution\n", + "c = cast\n", + "c = c[c.n <= 3]\n", + "c = c.groupby(['year', 'type', 'n']).size()\n", + "c = c.unstack('type')\n", + "(c.actor / (c.actor + c.actress)).unstack('n').plot(ylim=[0,1]) #second unstack here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/README.md b/README.md index 88b27af..45d3d4b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # American Time Use Analysis - + ## Description Use the U.S. Department of Labor's data on Americans' time use for research and analysis. diff --git a/Starting Point.ipynb b/Starting Point.ipynb index 2e6fffc..c5ae9e3 100644 --- a/Starting Point.ipynb +++ b/Starting Point.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -76,7 +76,7 @@ " dtype='object', 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" ], "text/plain": [ - " weight minutes weighted_minutes\n", - "2 10377056.507734 60 6.226234e+08\n", - "3 7731257.992805 65 5.025318e+08\n", - "4 4725269.227067 90 4.252742e+08\n", - "5 2372791.046351 270 6.406536e+08\n", - "6 5671341.270490 244 1.383807e+09" + " weight minutes\n", + "2 10377056.507734 60\n", + "3 7731257.992805 65\n", + "4 4725269.227067 90\n", + "5 2372791.046351 270\n", + "6 5671341.270490 244" ] }, - "execution_count": 39, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -536,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -547,7 +487,7 @@ "211.67427866070051" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -559,20 +499,22 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/plain": [ - "190.25402840855642" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'weighted_minutes'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Minutes on average spent watching TV (weighted)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweighted_minutes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/atus-analysis/.direnv/python-3.4.3/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2149\u001b[0m raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[0;32m-> 2150\u001b[0;31m (type(self).__name__, name))\n\u001b[0m\u001b[1;32m 2151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2152\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'weighted_minutes'" + ] } ], "source": [ @@ -582,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 19, "metadata": { "collapsed": false }, diff --git a/homework.ipynb b/homework.ipynb new file mode 100644 index 0000000..c9dd60e --- /dev/null +++ b/homework.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 11385 entries, 0 to 11384\n", + "Columns: 413 entries, tucaseid to t500107\n", + "dtypes: float64(1), int64(412)\n", + "memory usage: 36.0 MB\n" + ] + } + ], + "source": [ + "d = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "summary.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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