Exercises-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "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": 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></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burgtheater</td>\n",
       "      <td>1936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Main Hoon Angrakshak</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Faded Memories</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cinema Six</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I Want Your Love</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year\n",
       "0           Burgtheater  1936\n",
       "1  Main Hoon Angrakshak  2003\n",
       "2        Faded Memories  2008\n",
       "3            Cinema Six  2012\n",
       "4      I Want Your Love  2012"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('data/titles.csv', index_col=None)\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = pd.read_csv('data/cast.csv', index_col=None)\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### What are the ten most common movie names of all time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hamlet                  20\n",
       "Carmen                  17\n",
       "Macbeth                 16\n",
       "The Outsider            12\n",
       "Maya                    12\n",
       "Temptation              12\n",
       "Freedom                 11\n",
       "Honeymoon               11\n",
       "The Three Musketeers    11\n",
       "Othello                 11\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_common_titles = titles.title.value_counts().head(10)\n",
    "ten_common_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Which three years of the 1930s saw the most films released?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1937    1224\n",
       "1936    1169\n",
       "1938    1163\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release_by_year = titles[titles.year // 10 * 10 == 1930].year.value_counts().head(3)\n",
    "release_by_year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1930\n",
       "1    2000\n",
       "2    2000\n",
       "3    2010\n",
       "4    2010\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles_by_decade = titles.year // 10 * 10\n",
    "titles_by_decade.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26628f94cc0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XIyKiSx1dBCfpxVSF4a9tf7eEH5a0j+2HytDQIyU+COzbsvhc4MESf8ew+DUlPrdN+xrb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\nRERNikNERNSkOERERM3/B4zZgOFOjUkjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2663b0f2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titles_by_decade.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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26629d8ab00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z4bIkD+8hwwHAPwOXjh46GPgccADdnt4HJ51hlOOrSR41z+MFPCnJ53rIsBL4Q7odiI/Q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\nkhpjcUtSYyxuSWrM/wEo0nh2KAqK4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26629d3b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(titles[titles.title == 'Hamlet'].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 \"Rustler\" characters in each decade of the history of film."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26629dacf28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w01X13b2mHwzcUVXrzNTbz653mfqay2Wqc6YvAM+tqq/vNf0IYGZc79NKKfQvzPcNlyTA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\nwkKXpIH4/ydUb5Qz3G9qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26629e11048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(cast[cast.character == 'Rustler'].year // 10 * 10 ).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot the number of \"Hamlet\" characters each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26629c3f470>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAELCAYAAADJF31HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAE8tJREFUeJzt3XuQbWV95vHvI5fEeByEokVK0OM4\n4CVejs6RUMGYgKJoTEEqZhJSBcfE5OhESq0wF4KpEcbEOU4ppFKDFxKIJ4mXWEYDo0YhqElMItoc\nuR8QZZAcQWhHEcUYA/zmj7V6aNpu+rZXr+Y9309VV++99lr9e9febz977XdddqoKSdLD3yPGboAk\naTIMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1Ij9l3PYgcffHBt3rx5PUtK0sPe\nFVdc8Y2qmlpqvnUN9M2bNzM9Pb2eJSXpYS/JV5czn0MuktQIA12SGmGgS1IjDHRJaoSBLkmNMNAl\nqREGuiQ1wkCXpEas64lFktSqzWd8bNXL3rLjZyfSBrfQJakRBrokNcJAl6RGLBnoSX40yeeTXJXk\nuiRn99OflOTyJDcl+fMk+w/fXEnSYpazhf4vwHFV9WxgC3BCkqOBtwLnVtURwLeAVw3XTEnSUpYM\n9Op8t7+7X/9TwHHAh/rpO4GTBmmhJGlZljWGnmSfJFcCdwKXAl8B7qqqe/tZ9gCPX2TZ7Ummk0zP\nzMxMos2SpAUsK9Cr6r6q2gIcBhwFPG2h2RZZ9vyq2lpVW6emlvzCDUnSKq3oKJequgv4DHA08Jgk\nsycmHQbcNtmmSZJWYjlHuUwleUx/+5HAi4DdwKeBV/SzbQMuGqqRkqSlLefU/0OBnUn2oXsD+GBV\nfTTJ9cAHkvwu8EXgggHbKUlawpKBXlVXA89ZYPrNdOPpkqQNwDNFJakRBrokNcJAl6RGGOiS1AgD\nXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAl\nqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjVgy0JMcnuTTSXYnuS7J6/vpZyX5WpIr\n+5+XDd9cSdJi9l3GPPcCp1fVriSPBq5Icmn/2LlV9bbhmidJWq4lA72qbgdu729/J8lu4PFDN0yS\ntDIrGkNPshl4DnB5P+m0JFcnuTDJgYsssz3JdJLpmZmZNTVWkrS4ZQd6kk3AXwBvqKq7gXcCTwa2\n0G3Bv32h5arq/KraWlVbp6amJtBkSdJClhXoSfajC/P3VtWHAarqjqq6r6ruB/4QOGq4ZkqSlrKc\no1wCXADsrqpz5kw/dM5sPw9cO/nmSZKWazlHuRwDnAJck+TKftqZwMlJtgAF3AK8epAWSpKWZTlH\nuXwWyAIPfXzyzZEkrZZnikpSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMM\ndElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCX\npEYY6JLUCANdkhphoEtSI5YM9CSHJ/l0kt1Jrkvy+n76QUkuTXJT//vA4ZsrSVrMcrbQ7wVOr6qn\nAUcDr03ydOAM4LKqOgK4rL8vSRrJkoFeVbdX1a7+9neA3cDjgROBnf1sO4GThmqkJGlpKxpDT7IZ\neA5wOXBIVd0OXegDj11kme1JppNMz8zMrK21kqRFLTvQk2wC/gJ4Q1Xdvdzlqur8qtpaVVunpqZW\n00ZJ0jIsK9CT7EcX5u+tqg/3k+9Icmj/+KHAncM0UZK0HMs5yiXABcDuqjpnzkMXA9v629uAiybf\nPEnScu27jHmOAU4BrklyZT/tTGAH8MEkrwJuBX5xmCZKkpZjyUCvqs8CWeThF062OZKk1fJMUUlq\nxHKGXCTpYeFxn75y6ZkewteP3TKhlozDLXRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhph\noEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6\nJDXCQJekRhjoktSIJQM9yYVJ7kxy7ZxpZyX5WpIr+5+XDdtMSdJSlrOF/h7ghAWmn1tVW/qfj0+2\nWZKklVoy0Kvqb4FvrkNbJElrsJYx9NOSXN0PyRy42ExJtieZTjI9MzOzhnKSpIey2kB/J/BkYAtw\nO/D2xWasqvOramtVbZ2amlplOUnSUlYV6FV1R1XdV1X3A38IHDXZZkmSVmpVgZ7k0Dl3fx64drF5\nJUnrY9+lZkjyfuBngIOT7AHeBPxMki1AAbcArx6wjZKkZVgy0Kvq5AUmXzBAWyRJa+CZopLUCANd\nkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWp\nEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmN2HfsBkhqz2WfevKql33hcV+ZYEv2Lm6h\nS1IjDHRJasSSgZ7kwiR3Jrl2zrSDklya5Kb+94HDNlOStJTlbKG/Bzhh3rQzgMuq6gjgsv6+JGlE\nSwZ6Vf0t8M15k08Edva3dwInTbhdkqQVWu0Y+iFVdTtA//uxk2uSJGk1Bt8pmmR7kukk0zMzM0OX\nk6S91moD/Y4khwL0v+9cbMaqOr+qtlbV1qmpqVWWkyQtZbWBfjGwrb+9DbhoMs2RJK3Wcg5bfD/w\nj8BTkuxJ8ipgB3B8kpuA4/v7kqQRLXnqf1WdvMhDL5xwWyRJa+CZopLUCANdkhphoEtSIwx0SWqE\ngS5JjTDQJakRBrokNcJAl6RG+J2i0sD2nPF3q172sB0/NcGWqHVuoUtSIwx0SWqEgS5JjTDQJakR\nBrokNcJAl6RGeNii9hpv/6WXr3rZ0//8oxNsiTQMt9AlqREGuiQ1wkCXpEYY6JLUCANdkhqxoY5y\n2XzGx1a97C07fnaCLVkfz9z5zDUtf822aybUEkktcAtdkhphoEtSIwx0SWrEmsbQk9wCfAe4D7i3\nqrZOolGSpJWbxE7RY6vqGxP4O5KkNXDIRZIasdZAL+CSJFck2b7QDEm2J5lOMj0zM7PGcpKkxaw1\n0I+pqucCLwVem+QF82eoqvOramtVbZ2amlpjOUnSYtYU6FV1W//7TuAjwFGTaJQkaeVWHehJHpXk\n0bO3gRcD106qYZKklVnLUS6HAB9JMvt33ldVn5hIqyRJK7bqQK+qm4FnT7AtkqQ18LBFSWrEhrra\notp33ms+tablX/uu4ybUEqk9bqFLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGg\nS1IjDHRJaoSBLkmNMNAlqRFenEtq2FlnnTXKshqHW+iS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWp\nER62CHDWAWtY9tuTa8c62v3Up6162afdsHuCLZE0KW6hS1IjDHRJasSaAj3JCUluTPLlJGdMqlGS\npJVbdaAn2Qc4D3gp8HTg5CRPn1TDJEkrs5Yt9KOAL1fVzVX1A+ADwImTaZYkaaVSVatbMHkFcEJV\n/Xp//xTgJ6rqtHnzbQe293efAty4yrYeDHxjlcuu1Vi197a6Y9Z2nfeO2g/XdX5iVU0tNdNaDlvM\nAtN+6N2hqs4Hzl9Dna5YMl1VW9f6dx5Otfe2umPWdp33jtqtr/Nahlz2AIfPuX8YcNvamiNJWq21\nBPoXgCOSPCnJ/sAvAxdPplmSpJVa9ZBLVd2b5DTgk8A+wIVVdd3EWvbD1jxs8zCsvbfVHbO267x3\n1G56nVe9U1SStLF4pqgkNcJAl6RGGOiS1AgDXZIa4fXQJTUryQHACcDj6U58vA34ZFXdNXDdp9Jd\nCmVu3YuratAvE/Aolw1krE4wdm2tj73tNU5yKvAm4BLga/3kw4DjgbOr6k8GqvtfgZPprm+1Z07d\nXwY+UFU7hqgLGzjQk7wEOIkHd76LquoTLdYetROMW3uU19n+BazTa9zXH2Odb6S7vtRd86YfCFxe\nVUcOVPdLwI9X1b/Om74/cF1VHTFEXdiggZ7k94EjgT/hwZ3vVOCmqnp9a7VH7QQj1R7xubZ/PTB9\nPfrXmOv8vKr69rzpBwDTA/brG4CXVNVX501/InBJVT1liLqwcQP9Swu9eyYJ8KWhw22M2qN2gpFq\nj/hc278emL4e/Wusdd4G/De6IZd/6ic/gW7I5c1V9Z6B6p4A/C/gpnl1/x1w2pCfSjbqTtHvJzmq\nqj4/b/rzgO83WvsNwGVJFuwEA9Yds/ZYz7X9q7Ne/WuUda6qnUkuBl5CN9QT4DPAb1fVtwas+4kk\nR9J9Z8Rs3T3AF6rqvqHqwsbdQn8u8E7g0TzwEe1w4G7gN6vqikZrP4IROsFYtcd6rkd+jf898I6R\nao/Sv8Z8vvv6hzBn7L6q7hiy3hJt2VRV3x3s72/EQJ+V5HHM6XxV9fWWa/f/cFTV/f3Y5jOAW6rq\nm0PXXqAtv1lV71inWqO8znth/3oCcHdV3ZVkM7AV2D3wRfXm1l/XdU6yBXgXcADdG0noxu7vonsj\n2TVk/UXadGtVPWGwv7+RA30hSZ5aVTesQ539FtiBdHBVDfJtJ0lOAt4N3A+8BjgTuIduZ9J/rKr/\nPUTdvvZvLTD5TOAtAFV1zlC157VjE9363jzkccL9m+W/Vt/5kxwLPJdu5+DQR9c8q6quHrLGInXP\nAF4N/AvwNuA/AX8PHA1csB6vcZKtdFvm99LtDB30/zjJlcCrq+ryedOPBt5dVc8eqO5C/0/QvaG8\nsaoOGqIuPDzPFL1kyD+e5Ngke4DbklzSb8msR+03Ac8GfhL4U+DUqjoOOKZ/bEhnAz8BbKL7WPxo\nuksiz94eRJJ3zLn9fOB64O3ANUleNlRdumv5P6av+5+B3wMeCZye5H8MWBfgi0m+nOTNWd8vVT+F\n7svcjwHOBX6qql5FNwTza0MWTvLTSaaBHcCFdG8sFyT5TJLDH3rpNXnU/DAHqKrPAY8asO5bgAN5\n4P9n9mcTA2fuhtwpmuQPFnuI/h9xQP+T7miA69J9b+qlSU7pO8FCX7s3MbMfQfuPZTf20746OxQz\noB8HzqHr5GdX1feSbKuqsweue/Sc228GTqqqXUn+LfBB4OMD1d1nzk6xX6ILt39OsgPYBfz2QHUB\nrqYL15OBi5PcA7yf7ljwWwase1+/jj8A/hn4vwBVdU93sMmgfh94cVXNJHkScE5VHZPkeOAC4MUD\n1f2rJB+jO1xydkfw4XSHSw75SWwX8JcL7RtI8usD1t2YgQ78KnA63cfD+U4euPb+s2OKVfWhJLuB\nD/cfWQcdn0ryiKq6nzlbTEn2AfYfsm5V3Qq8IsmJdG9g5w5ZbxH/ZnZMs6pu7td7KHcneUZVXUv3\npb0/Shdy+zL8p9bq674ReGOSo+hO7vm7JP9UVT85UN1dSd5H96Z9GbAzySeA4+g+GQ1pn6qa6W/f\nCjwRoKou7Y9RH0RVvS7JS3ng7NjZHcHnVdVQGwvQ5ddi+70G/U7RDTmGnuRTwO9U1T8s8Nj/qaon\nDVh7Gnj53B02SQ4DPgo8uaoGGYJI8jzgmqr6/rzpm4HnV9WfDVF3gXY8CjiL7gy7Fwxc63vAl+n+\n0TYDT6iqb/WfSK6uqmcMVPdZdMNaV/WTjgH+BngW3dbj+4ao29f+YlU9Z4HpAV5QVX8zUN19gV+k\n2yj5EN0Q28l0AXteVd0zRN2+9oV93cvowvVrVfVbSX4M2FVVTx2q9t5mowb6QcD3q+p7I9R+ETBT\nVVfNm/4Y4LVV9Xvr3aZW9Se1zHV7Vf0gycF04fbhAWvvQ/dR/0i6LfM9rM9Fm35lyDeMjSjJfsBv\n0I3hX0X3dZX3JXkk8Nj5JztNsO4BdMNnJwKP7SffCVwE7BjqtZ5T9yRgar3qwgYN9L1Rf4THfwF+\nge7Qqh8AXwHeNdQZbRuhttbHQ7zG76yqnWO2bShJPgl8Ctg5Z//U44BXAi+squPXue424EVD1YUN\nepRLkk1J/nuS65J8O8lMks8leWXDtd8L3Ex3VtvZwB/Q7Tw7NslbWqw91nNt/3rQa3zc0P1rzjpf\nu87rvLmq3jp3+LSqvl7dhcgGOxb8Ieq+deC6G3MLPclFwEeAvwb+A92OnA8Av0M3/nZma7WTXDX3\nuNgkX6iq5/XjydcPOc44Vu0Rn2v71/r2r7HW+ZK+5s7Zs0PTnTX6SuD4qnpRS3UBqKoN9wNcNe/+\nF/rfjwBuaLE28A90Oz8Bfo5uPHf2sRsHXudRao/4XNu/1rd/jbXOBwJvBW4AvkV35MnuftpBrdWt\nqg172OI9SZ5fVZ9N8nP0hwBVd0r80AfNjlX7NcAfpbuoz7X0hy4mmQLOG7DumLXHeq7tX+vbv0ZZ\n5+qOmPpj4FLgczXnGirprog4yLHoY9WdLb7hfugOH/s83TUXPgsc2U+fAl7Xau2HaNOvjvhaDFZ7\nrOfa/rW+/WvE1/l1wI3AXwK3ACfOeWxXa3WramMG+hJPVpPhtkTdW0dc51Fqj/hc278aWWfgGmBT\nf3szMA28vr//xdbqVtXG3Cn6UDLw1crGqp1ksQs2hW6L5keGqDt27cWM9Trbv9bXwOt8fVU9fc79\nTXQnVV0PHFdVW1qqCxv01P8lOt8hjdY+hO6QsvkX3g/dDq0hjVJ7rOfa/vVDdQftXyOu89eTbKmq\nKwGq6rtJXk53gbBnNlh3YwY6e2G40V1aYNNsJ3hQ4eQzA9Yds/ZYz7X9a27h4fvXWOt8Kt2lev+/\nqroXODXJuxusu2EDfa8Lt+ouZbrYY78yVN2Ra4/1Otu/HvzYoP2L8dZ5z0M89vet1YUNemKRJGnl\nNuSp/5KklTPQJakRBrokNcJAl6RG/D+46Lrunifo7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662ac26668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(cast[cast.character == 'Hamlet'].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": [
    "### What are the 11 most common character names in movie history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Himself        20746\n",
       "Dancer         12477\n",
       "Extra          11948\n",
       "Reporter        8434\n",
       "Student         7773\n",
       "Doctor          7669\n",
       "Party Guest     7245\n",
       "Policeman       7029\n",
       "Nurse           6999\n",
       "Bartender       6802\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Queen Elizabeth II           12\n",
       "Joyce Brothers                9\n",
       "Luisa Horga                   9\n",
       "Mar?a Luisa (V) Mart?n        9\n",
       "Margaret Thatcher             8\n",
       "Hillary Clinton               8\n",
       "Rekha                         6\n",
       "Joan Rivers                   6\n",
       "Mar?a Isabel (III) Mart?n     6\n",
       "Marta Berrocal                6\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character == 'Herself'].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adolf Hitler             99\n",
       "Richard Nixon            44\n",
       "Ronald Reagan            41\n",
       "John F. Kennedy          37\n",
       "George W. Bush           25\n",
       "Winston Churchill        24\n",
       "Martin Luther King       23\n",
       "Ron Jeremy               22\n",
       "Bill Clinton             22\n",
       "Franklin D. Roosevelt    21\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character == 'Himself'].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emmett Vogan       39\n",
       "Sam (II) Harris    33\n",
       "Harold Miller      29\n",
       "Bess Flowers       29\n",
       "Nolan Leary        27\n",
       "Franklyn Farnum    27\n",
       "Frank O'Connor     27\n",
       "Herman Hack        25\n",
       "Larry Steers       25\n",
       "Edmund Cobb        24\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.year == 1945].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eric Roberts           35\n",
       "Lee Nicholas Harris    23\n",
       "Tom Sizemore           23\n",
       "Dennis Nicomede        22\n",
       "Daniel Eghan           20\n",
       "Sergey A.              19\n",
       "Stuart (III) Whelan    18\n",
       "Lloyd Kaufman          17\n",
       "Dawna Lee Heising      16\n",
       "Jackson Kai            15\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.year == 2017].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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mammootty        22\n",
       "Shakti Kapoor    21\n",
       "Sukumari         20\n",
       "Lou Scheimer     15\n",
       "Raj Babbar       15\n",
       "Mohanlal         15\n",
       "Aruna Irani      15\n",
       "Rajesh Khanna    13\n",
       "Om Shivpuri      13\n",
       "Asrani           13\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cast[cast.year == 1985]).name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2662b3db908>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0C1obik/288ZjM0odlPpMuIP4bLbaEzO9tMZM3QpXbS+/qGZ5PvZAo+kNElAt5h4XDO/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\nQljE/wdXsyNety09WQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662b3d3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[cast.name == 'Mammootty'].year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2662c822a58>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W4jfGxIwxO4HVwA0i0jzrkB8CjcaY7cCzwLfmaOdRY8xuY8zu2traVIeoZS651eKOHOnx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662c7f4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[cast.name == 'Amy Poehler'].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": 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></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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patron in Frisky Rabbit         16\n",
       "Patron in Chinese Restaurant     9\n",
       "Patron in the Coffee House       9\n",
       "Patron in Billiard Parlor        5\n",
       "Patron in restaurant             4\n",
       "Patron in Bar                    4\n",
       "Patron in cabaret                3\n",
       "Patron in Restaurant             3\n",
       "Patron in Club                   3\n",
       "Patron in Quiet Bar              2\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science Teacher         60\n",
       "Science Student          9\n",
       "Science Fair Student     9\n",
       "Science Fair Judge       6\n",
       "Science Club Member      5\n",
       "Science Reporter         5\n",
       "Science Fair Kid         5\n",
       "Science Promo Cadet      5\n",
       "Science Kid              5\n",
       "Science                  4\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character.str.startswith('Science')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 26,
   "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>2814255</th>\n",
       "      <td>The Third Secret</td>\n",
       "      <td>1964</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Miss Humphries</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814210</th>\n",
       "      <td>A Study in Terror</td>\n",
       "      <td>1965</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Sally</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814221</th>\n",
       "      <td>He Who Rides a Tiger</td>\n",
       "      <td>1965</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Joanne</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814218</th>\n",
       "      <td>Four in the Morning</td>\n",
       "      <td>1965</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Wife</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814208</th>\n",
       "      <td>A Midsummer Night's Dream</td>\n",
       "      <td>1968</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Titania</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             title  year        name     type       character  \\\n",
       "2814255           The Third Secret  1964  Judi Dench  actress  Miss Humphries   \n",
       "2814210          A Study in Terror  1965  Judi Dench  actress           Sally   \n",
       "2814221       He Who Rides a Tiger  1965  Judi Dench  actress          Joanne   \n",
       "2814218        Four in the Morning  1965  Judi Dench  actress            Wife   \n",
       "2814208  A Midsummer Night's Dream  1968  Judi Dench  actress         Titania   \n",
       "\n",
       "            n  \n",
       "2814255  12.0  \n",
       "2814210   8.0  \n",
       "2814221   2.0  \n",
       "2814218   2.0  \n",
       "2814208  16.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "judi_dench = cast[(cast.name == 'Judi Dench') & (cast.n.notnull())].sort_values(by=['year'])\n",
    "judi_dench.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2662c8db7f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662c8ea080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "judi_dench.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": 29,
   "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>871437</th>\n",
       "      <td>Sinners in the Sun</td>\n",
       "      <td>1932</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Ridgeway</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871418</th>\n",
       "      <td>Madame Butterfly</td>\n",
       "      <td>1932</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Lieutenant B.F. Pinkerton</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871419</th>\n",
       "      <td>Merrily We Go to Hell</td>\n",
       "      <td>1932</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Charlie Baxter</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871399</th>\n",
       "      <td>Devil and the Deep</td>\n",
       "      <td>1932</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Lt. Jaeckel</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871409</th>\n",
       "      <td>Hot Saturday</td>\n",
       "      <td>1932</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Romer Sheffield</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title  year        name   type  \\\n",
       "871437     Sinners in the Sun  1932  Cary Grant  actor   \n",
       "871418       Madame Butterfly  1932  Cary Grant  actor   \n",
       "871419  Merrily We Go to Hell  1932  Cary Grant  actor   \n",
       "871399     Devil and the Deep  1932  Cary Grant  actor   \n",
       "871409           Hot Saturday  1932  Cary Grant  actor   \n",
       "\n",
       "                        character    n  \n",
       "871437                   Ridgeway  8.0  \n",
       "871418  Lieutenant B.F. Pinkerton  2.0  \n",
       "871419             Charlie Baxter  9.0  \n",
       "871399                Lt. Jaeckel  4.0  \n",
       "871409            Romer Sheffield  1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cary_grant = cast[(cast.name == 'Cary Grant') & (cast.n.notnull())].sort_values('year')\n",
    "cary_grant.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2662c8c6a58>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Y4rbftxxxi2faw7V3m/lxK1fbtvF3RZx+53rPnHL47aPdErtegTbh/arb2BY2UxGuShS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662c903c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cary_grant.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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2662c9c1a20>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2662c9b72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[(cast.name == 'Sidney Poitier') & (cast.n.notnull())].sort_values('year').plot(x='year', y='n', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head()"
   ]
  },
  {
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 523 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'actors': 6692, 'actresses': 3040}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "{'actors': len(cast[(cast.type == 'actor') & (cast.n == 1) & (cast.year // 10 * 10 == 1950)]), 'actresses': len(cast[(cast.type == 'actress') & (cast.n == 1) & (cast.year // 10 * 10 == 1950)])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'actors': 4640, 'actresses': 4635}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'actors': len(cast[(cast.type == 'actor') & (cast.n == 2) & (cast.year // 10 * 10 == 1950)]), 'actresses': len(cast[(cast.type == 'actress') & (cast.n == 2) & (cast.year // 10 * 10 == 1950)])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 193 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "c = cast.set_index(['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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