Exercises-BL-4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "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('style-table.css').read() + open('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",
       "      <td>0</td>\n",
       "      <td>Kanzashi</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>24 Hours</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Red Mantis</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Ruby Skye P.I.: The Spam Scam</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>County General</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           title  year\n",
       "0                       Kanzashi  1941\n",
       "1                       24 Hours  1931\n",
       "2                     Red Mantis  2009\n",
       "3  Ruby Skye P.I.: The Spam Scam  2010\n",
       "4                 County General  2005"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('data/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",
       "      <td>0</td>\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",
       "      <td>1</td>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$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('data/cast.csv')\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = titles\n",
    "c = cast"
   ]
  },
  {
   "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": {},
   "outputs": [],
   "source": [
    "bs = c[(c.character == 'Batman')|(c.character == 'Superman')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  character\n",
       "1938  Batman       1\n",
       "1940  Batman       1\n",
       "1943  Batman       1\n",
       "1948  Superman     1\n",
       "1949  Batman       2\n",
       "                  ..\n",
       "2016  Superman     4\n",
       "2017  Batman       9\n",
       "      Superman     5\n",
       "2018  Batman       4\n",
       "      Superman     2\n",
       "Length: 68, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs.groupby(['year', 'character']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>SvsB</th>\n",
       "      <th>SupermanYear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1938</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1949</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "character  Batman  Superman  SvsB  SupermanYear\n",
       "year                                           \n",
       "1938          1.0       0.0  -1.0             0\n",
       "1940          1.0       0.0  -1.0             0\n",
       "1943          1.0       0.0  -1.0             0\n",
       "1948          0.0       1.0   1.0             1\n",
       "1949          2.0       0.0  -2.0             0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_year = bs.groupby(['year', 'character']).size().unstack().fillna(0)\n",
    "s_year['SvsB'] = s_year.Superman - s_year.Batman\n",
    "s_year['SupermanYear'] = np.where(s_year.SvsB > 0, 1, 0)\n",
    "s_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_year.SupermanYear.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We don't need to do applymap\n",
    "\n",
    "s_year[s_year.SvsB > 0].shape"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = c[(c.character == 'Batman')|(c.character == 'Superman')]\n",
    "\n",
    "# Do a small test here\n",
    "(bs.groupby([c.character, c.year]).size()).equals(bs.groupby([bs.character, bs.year]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>By</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1938</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1949</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1953</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1957</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1967</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1989</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1993</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "character  Batman  Superman   By\n",
       "year                            \n",
       "1938          1.0       0.0  1.0\n",
       "1940          1.0       0.0  1.0\n",
       "1943          1.0       0.0  1.0\n",
       "1948          0.0       1.0 -1.0\n",
       "1949          2.0       0.0  2.0\n",
       "1950          0.0       1.0 -1.0\n",
       "1951          0.0       2.0 -2.0\n",
       "1953          2.0       0.0  2.0\n",
       "1954          0.0       5.0 -5.0\n",
       "1955          0.0       1.0 -1.0\n",
       "1957          2.0       0.0  2.0\n",
       "1965          1.0       0.0  1.0\n",
       "1966          1.0       0.0  1.0\n",
       "1967          1.0       0.0  1.0\n",
       "1973          1.0       2.0 -1.0\n",
       "1978          0.0       2.0 -2.0\n",
       "1980          0.0       2.0 -2.0\n",
       "1981          1.0       1.0  0.0\n",
       "1983          0.0       2.0 -2.0\n",
       "1985          2.0       2.0  0.0\n",
       "1987          0.0       1.0 -1.0\n",
       "1988          1.0       0.0  1.0\n",
       "1989          1.0       0.0  1.0\n",
       "1991          1.0       1.0  0.0\n",
       "1992          1.0       1.0  0.0\n",
       "1993          2.0       0.0  2.0\n",
       "1994          1.0       1.0  0.0\n",
       "1995          1.0       1.0  0.0\n",
       "1997          1.0       1.0  0.0\n",
       "1998          1.0       0.0  1.0\n",
       "1999          1.0       0.0  1.0\n",
       "2002          1.0       1.0  0.0\n",
       "2003          1.0       1.0  0.0\n",
       "2004          1.0       0.0  1.0\n",
       "2005          1.0       0.0  1.0\n",
       "2006          0.0       1.0 -1.0\n",
       "2008          3.0       2.0  1.0\n",
       "2009          3.0       1.0  2.0\n",
       "2010          3.0       1.0  2.0\n",
       "2011          4.0       2.0  2.0\n",
       "2012          2.0       1.0  1.0\n",
       "2013          5.0       1.0  4.0\n",
       "2014          5.0       1.0  4.0\n",
       "2015          4.0       1.0  3.0\n",
       "2016         12.0       4.0  8.0\n",
       "2017          9.0       5.0  4.0\n",
       "2018          4.0       2.0  2.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by = bs.groupby([c.character, c.year]).size().unstack(0).fillna(0)\n",
    "by['By'] = by.Batman - by.Superman\n",
    "by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by[by.By > 0].shape"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = c.groupby([c.type, c.year]).size().unstack(0).fillna(0)\n",
    "\n",
    "aa = aa.iloc[:len(aa)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21ddc8708>"
      ]
     },
     "execution_count": 38,
     "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": [
    "aa.reset_index().plot(x = 'year', y = ['actor', 'actress'], kind = 'area', color = ['red', 'green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21912ea08>"
      ]
     },
     "execution_count": 26,
     "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": [
    "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e041b70c48>"
      ]
     },
     "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": [
    "aa = c.groupby([c.type, c.year]).size().unstack(0).fillna(0)\n",
    "aa = aa.iloc[:len(aa) - 1]\n",
    "aa.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": 23,
   "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>type</th>\n",
       "      <th>actor</th>\n",
       "      <th>actress</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1894</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1906</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1907</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2022</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2024</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2025</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type  actor  actress  difference\n",
       "year                            \n",
       "1894    2.0      1.0         1.0\n",
       "1900    2.0      0.0         2.0\n",
       "1905    1.0      0.0         1.0\n",
       "1906   14.0      3.0        11.0\n",
       "1907    5.0      0.0         5.0\n",
       "...     ...      ...         ...\n",
       "2021   14.0     21.0        -7.0\n",
       "2022   12.0      4.0         8.0\n",
       "2023    2.0      0.0         2.0\n",
       "2024    9.0      6.0         3.0\n",
       "2025    5.0      4.0         1.0\n",
       "\n",
       "[123 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "an = c.groupby([c.type, c.year]).size().unstack(0).fillna(0)\n",
    "an = an.iloc[:len(an) - 1]\n",
    "an['difference'] = an['actor'] - an['actress'] \n",
    "an"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e04298d448>"
      ]
     },
     "execution_count": 24,
     "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": [
    "an.reset_index().plot(x = 'year', y = 'difference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e04d6215c8>"
      ]
     },
     "execution_count": 29,
     "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": [
    "# Using Brandon's method\n",
    "\n",
    "(an.actor - an.actress).plot(ylim = [0, 40000])"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3786173, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = c[c.year < 2100]\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x236b798fc88>"
      ]
     },
     "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": [
    "cf = c.groupby(['year', 'type']).size().unstack(1)#.fillna(0)\n",
    "cf = cf.assign(total = cf.sum(axis = 1)).reset_index()\n",
    "cf['fraction'] = cf.actor/cf.total\n",
    "\n",
    "cf.plot(x = 'year', y = 'fraction', ylim = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>type</th>\n",
       "      <th>year</th>\n",
       "      <th>actor</th>\n",
       "      <th>actress</th>\n",
       "      <th>total</th>\n",
       "      <th>fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1906</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1907</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>2021</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2022</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2024</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2025</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type  year  actor  actress  total  fraction\n",
       "0     1894    2.0      1.0    3.0  0.666667\n",
       "1     1900    2.0      0.0    2.0  1.000000\n",
       "2     1905    1.0      0.0    1.0  1.000000\n",
       "3     1906   14.0      3.0   17.0  0.823529\n",
       "4     1907    5.0      0.0    5.0  1.000000\n",
       "..     ...    ...      ...    ...       ...\n",
       "118   2021   14.0     21.0   35.0  0.400000\n",
       "119   2022   12.0      4.0   16.0  0.750000\n",
       "120   2023    2.0      0.0    2.0  1.000000\n",
       "121   2024    9.0      6.0   15.0  0.600000\n",
       "122   2025    5.0      4.0    9.0  0.555556\n",
       "\n",
       "[123 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>type</th>\n",
       "      <th>actor</th>\n",
       "      <th>actress</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1894</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1906</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1907</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2022</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2024</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2025</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2115</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type  actor  actress\n",
       "year                \n",
       "1894    2.0      1.0\n",
       "1900    2.0      NaN\n",
       "1905    1.0      NaN\n",
       "1906   14.0      3.0\n",
       "1907    5.0      NaN\n",
       "...     ...      ...\n",
       "2022   12.0      4.0\n",
       "2023    2.0      NaN\n",
       "2024    9.0      6.0\n",
       "2025    5.0      4.0\n",
       "2115    2.0      1.0\n",
       "\n",
       "[124 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.groupby(['year', 'type']).size().unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24f882efa48>"
      ]
     },
     "execution_count": 15,
     "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.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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2369060cdc8>"
      ]
     },
     "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": [
    "cf.plot()"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143720, 6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x236944f9f48>"
      ]
     },
     "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",
    "\n",
    "ct = c[c.n == 2]\n",
    "ct.shape\n",
    "\n",
    "ct = ct.groupby(['year', 'type']).size().unstack()\n",
    "(ct.actor/(ct.actor + ct.actress)).plot(kind = 'line', ylim = [0, 1])"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fa58662948>"
      ]
     },
     "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": [
    "df = c[c.n < 4]\n",
    "df = df.groupby([df.type, df.year, df.n]).size().unstack(0)\n",
    "(df.actor/(df.actor + df.actress)).unstack().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  order   ex\n",
       "0     a  300\n",
       "1     b  500\n",
       "2     b  200\n",
       "3     c  600"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "  order  vol\n",
       "0     a  200\n",
       "1     b  400\n",
       "2     c  700"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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       "  order   ex  vol  try\n",
       "0     a  300  200  200\n",
       "1     b  500  400  400\n",
       "2     b  200  400  200\n",
       "3     c  600  700  600"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = pd.DataFrame({'order':['a', 'b', 'b', 'c'], 'ex':[300, 500, 200, 600]})\n",
    "o\n",
    "\n",
    "pb = pd.DataFrame({'order':['a', 'b', 'c'], 'vol':[200, 400, 700]})\n",
    "pb\n",
    "\n",
    "m = pd.merge(o, pb, on = 'order', how = 'left')\n",
    "m['try'] = m[['ex', 'vol']].min(axis = 1)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   type  value_x  value_y\n",
       "0     a       10      100\n",
       "1     a       50      100\n",
       "2     a       45      100\n",
       "3     a       10      100\n",
       "4     b       45       50\n",
       "5     b       10       50\n",
       "6     b        5       50\n",
       "7     c        6       30\n",
       "8     c        6       30\n",
       "9     c        8       30\n",
       "10    c       12       30\n",
       "11    c       10       30"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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      ],
      "text/plain": [
       "   type  value_x  value_y  cumsum\n",
       "0     a       10      100      10\n",
       "1     a       50      100      60\n",
       "2     a       45      100     105\n",
       "3     a       10      100     115\n",
       "4     b       45       50      45\n",
       "5     b       10       50      55\n",
       "6     b        5       50      60\n",
       "7     c        6       30       6\n",
       "8     c        6       30      12\n",
       "9     c        8       30      20\n",
       "10    c       12       30      32\n",
       "11    c       10       30      42"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.DataFrame({\"type\":['a','b','c'], \"value\":[100, 50, 30]})\n",
    "B = pd.DataFrame({\"type\": ['a','a','a','a','b','b','b','c','c','c','c','c'], \"value\": [10,50,45,10,45,10,5,6,6,8,12,10]})\n",
    "\n",
    "df = pd.merge(B, A, on = 'type')\n",
    "df \n",
    "\n",
    "df['cumsum'] = df.groupby('type')['value_x'].cumsum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   type  value\n",
       "0     a     10\n",
       "1     a     50\n",
       "2     a     45\n",
       "4     b     45\n",
       "5     b     10\n",
       "7     c      6\n",
       "8     c      6\n",
       "9     c      8\n",
       "10    c     12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "      value\n",
       "type       \n",
       "a       105\n",
       "b        55\n",
       "c        32"
      ]
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     "execution_count": 24,
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   "source": [
    "B[(df.groupby('type')['cumsum'].shift().fillna(0) < df['value_y'])]#.groupby('type').count()\n",
    "B[(df.groupby('type')['cumsum'].shift().fillna(0) < df['value_y'])].groupby('type').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>a1</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>a2</td>\n",
       "      <td>200</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>a3</td>\n",
       "      <td>1000</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  OrderID  Execution_Size  Quote_Size\n",
       "0      a1              20         300\n",
       "1      a1              75         300\n",
       "2      a1             500         300\n",
       "3      a2             200         500\n",
       "4      a3            1000         600"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "q =pd.DataFrame({'OrderID':['a1', 'a1','a1','a2', 'a3'], \n",
    "              'Execution_Size': [20, 75, 500, 200, 1000],\n",
    "              'Quote_Size': [300, 300, 300, 500, 600] })\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     595\n",
       "1     595\n",
       "2     595\n",
       "3     200\n",
       "4    1000\n",
       "Name: Execution_Size, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.groupby(q.OrderID)['Execution_Size'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>OrderID</th>\n",
       "      <th>Execution_Size</th>\n",
       "      <th>Quote_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>a1</td>\n",
       "      <td>20</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>a1</td>\n",
       "      <td>75</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>a1</td>\n",
       "      <td>205</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>a2</td>\n",
       "      <td>200</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>a3</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  OrderID  Execution_Size  Quote_Size\n",
       "0      a1              20         300\n",
       "1      a1              75         300\n",
       "2      a1             205         300\n",
       "3      a2             200         500\n",
       "4      a3             600         600"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Desired result\n",
    "\n",
    "d =pd.DataFrame({'OrderID':['a1', 'a1','a1','a2', 'a3'], \n",
    "              'Execution_Size': [20, 75, 205, 200, 600],\n",
    "              'Quote_Size': [300, 300, 300, 500, 600] })\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    jozefeg@gmail.com\n",
      "1    congrat to decode\n",
      "2      happy coding!  \n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "d = {0: {50: 16, 23: 7, 22: 7, 5: 1, 31: 10, 47: 15, 2: 0, 44: 14, 39: 13, 21: 7, 11: 3, 30: 10, 16: 5, 42: 14, 26: 8, 1: 0, 40: 13, 32: 10, 34: 11, 46: 15, 49: 16, 15: 5, 37: 12, 9: 3, 12: 4, 41: 13, 24: 8, 10: 3, 18: 6, 8: 2, 33: 11, 35: 11, 0: 0, 36: 12, 27: 9, 48: 16, 38: 12, 7: 2, 28: 9, 3: 1, 29: 9, 43: 14, 4: 1, 45: 15, 17: 5, 14: 4, 13: 4, 19: 6, 25: 8, 20: 6, 6: 2}, 1: {50: 2, 23: 2, 22: 1, 5: 2, 31: 1, 47: 2, 2: 2, 44: 2, 39: 0, 21: 0, 11: 2, 30: 0, 16: 1, 42: 0, 26: 2, 1: 1, 40: 1, 32: 2, 34: 1, 46: 1, 49: 1, 15: 0, 37: 1, 9: 0, 12: 0, 41: 2, 24: 0, 10: 1, 18: 0, 8: 2, 33: 0, 35: 2, 0: 0, 36: 0, 27: 0, 48: 0, 38: 2, 7: 1, 28: 1, 3: 0, 29: 2, 43: 1, 4: 1, 45: 0, 17: 2, 14: 2, 13: 1, 19: 1, 25: 1, 20: 2, 6: 0}, 2: {50: 32, 23: 32, 22: 32, 5: 32, 31: 32, 47: 32, 2: 32, 44: 33, 39: 46, 21: 64, 11: 97, 30: 97, 16: 97, 42: 99, 26: 99, 1: 99, 40: 99, 32: 100, 34: 100, 46: 100, 49: 101, 15: 101, 37: 101, 9: 101, 12: 102, 41: 103, 24: 103, 10: 103, 18: 103, 8: 104, 33: 105, 35: 105, 0: 106, 36: 108, 27: 109, 48: 109, 38: 110, 7: 110, 28: 111, 3: 111, 29: 111, 43: 111, 4: 111, 45: 111, 17: 112, 14: 112, 13: 114, 19: 116, 25: 116, 20: 121, 6: 122}}\n",
    "print (pd.DataFrame(d).pivot(0,1,2).applymap(chr).agg(''.join))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8774.3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "279.3 + 1699*5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11250"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1182"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10068"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "48000 - 36750\n",
    "197 * 2 * 3\n",
    "11250 - 1182"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a000fa148>"
      ]
     },
     "execution_count": 22,
     "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": [
    "n13 = c[c.n <= 3]\n",
    "\n",
    "na = n13.groupby([n13.type, n13.year, n13.n]).size().unstack([0])\n",
    "\n",
    "(na.actor/(na.actor + na.actress)).unstack(-1).plot(ylim = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a001e3088>"
      ]
     },
     "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": [
    "# Brandon's method\n",
    "\n",
    "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])"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}