Exercises-4_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
       "    border-right: 1px dashed #888;\n",
       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('D:/Data Visualization Project/style-table.css').read() + open('D:/Data Visualization Project/style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Captain of His Soul</td>\n",
       "      <td>1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qing chun xing</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tangled Web (II)</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Shepherd of the Hills</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anna mit'n Flimmerfimmel</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       title  year\n",
       "0        Captain of His Soul  1918\n",
       "1             Qing chun xing  1975\n",
       "2           Tangled Web (II)  2008\n",
       "3  The Shepherd of the Hills  1919\n",
       "4   Anna mit'n Flimmerfimmel  1919"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('D:/Data Visualization Project/titles.csv')\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closet Monster</td>\n",
       "      <td>2015</td>\n",
       "      <td>Buffy #1</td>\n",
       "      <td>actor</td>\n",
       "      <td>Buffy 4</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year      name   type                character     n\n",
       "0        Closet Monster  2015  Buffy #1  actor                  Buffy 4  31.0\n",
       "1       Suuri illusioni  1985    Homo $  actor                   Guests  22.0\n",
       "2   Battle of the Sexes  2017   $hutter  actor          Bobby Riggs Fan  10.0\n",
       "3  Secret in Their Eyes  2015   $hutter  actor          2002 Dodger Fan   NaN\n",
       "4            Steve Jobs  2015   $hutter  actor  1988 Opera House Patron   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = pd.read_csv('D:/Data Visualization Project/cast.csv')\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>character</th>\n",
       "      <th>Batman</th>\n",
       "      <th>Superman</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "character  Batman  Superman\n",
       "year                       \n",
       "1938          1.0       0.0\n",
       "1940          1.0       0.0\n",
       "1943          1.0       0.0\n",
       "1948          0.0       1.0\n",
       "1949          2.0       0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[(c.character == 'Superman') | (c.character == 'Batman')]\n",
    "c = c.groupby(['year', 'character']).size()\n",
    "c = c.unstack()\n",
    "c = c.fillna(0)\n",
    "c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Superman years:\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "diff = c.Superman - c.Batman\n",
    "print('Superman years:')\n",
    "print(len(diff[diff > 0.0]))"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batman years:\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "diff = c.Batman - c.Superman\n",
    "print('Batman years:')\n",
    "print(len(diff[diff > 0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e9fd78>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year < 2020]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "c.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x917a3d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year < 2020]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "c.plot(kind = 'area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x91c3850>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year < 2020]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor - c.actress).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot the fraction of roles that have been 'actor' roles each year in the history of film."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9982370>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year < 2019]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor/(c.actor + c.actress)).plot(ylim=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x99828c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.n == 2]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor/(c.actor + c.actress)).plot(ylim = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9f13478>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.n <= 3]\n",
    "c = c.groupby(['year', 'type', 'n']).size()\n",
    "c = c.unstack('type').fillna(0)\n",
    "r = c.actor / (c.actor + c.actress)\n",
    "r = r.unstack('n')\n",
    "r.plot(ylim=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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