Exercises-2_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open(r'D:\\Data Visualization Project\\style-table.css').read() + open(r'D:\\Data Visualization Project\\style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Captain of His Soul</td>\n",
       "      <td>1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qing chun xing</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tangled Web (II)</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Shepherd of the Hills</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anna mit'n Flimmerfimmel</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       title  year\n",
       "0        Captain of His Soul  1918\n",
       "1             Qing chun xing  1975\n",
       "2           Tangled Web (II)  2008\n",
       "3  The Shepherd of the Hills  1919\n",
       "4   Anna mit'n Flimmerfimmel  1919"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv(r'D:\\Data Visualization Project\\titles.csv', index_col=None, encoding= 'utf-8')\n",
    "titles.head()"
   ]
  },
  {
   "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",
       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = pd.read_csv(r'D:\\Data Visualization Project\\cast.csv')\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            11\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles.title.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Which three years of the 1930s saw the most films released?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1937    1217\n",
       "1938    1161\n",
       "1936    1153\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = titles\n",
    "t = t[t.year // 10 == 193]\n",
    "t.year.value_counts().head(3)"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9429418>"
      ]
     },
     "execution_count": 14,
     "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": [
    "t = titles\n",
    "(t.year // 10 * 10).value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot the number of \"Hamlet\" films made each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a1d610>"
      ]
     },
     "execution_count": 16,
     "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": [
    "t = titles\n",
    "t = t[t.title == 'Hamlet']\n",
    "(t.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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb280d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.character == 'Rustler']\n",
    "(c.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 \"Hamlet\" characters each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa179e20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.character == 'Hamlet']\n",
    "(c.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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Himself        19637\n",
       "Dancer         12019\n",
       "Extra          10848\n",
       "Reporter        7818\n",
       "Doctor          6963\n",
       "Party Guest     6786\n",
       "Student         6631\n",
       "Policeman       6424\n",
       "Bartender       6290\n",
       "Herself         6269\n",
       "Minor Role      6203\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.character.value_counts().head(11)"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Queen Elizabeth II        11\n",
       "María Luisa (V) Martín     9\n",
       "Luisa Horga                9\n",
       "Margaret Thatcher          8\n",
       "Joyce Brothers             8\n",
       "Hillary Clinton            8\n",
       "Inés J. Southern           6\n",
       "Marta Berrocal             6\n",
       "Sumie Sakai                6\n",
       "Joan Rivers                6\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c[c.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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adolf Hitler          91\n",
       "Richard Nixon         43\n",
       "Ronald Reagan         37\n",
       "John F. Kennedy       35\n",
       "George W. Bush        24\n",
       "Winston Churchill     24\n",
       "Martin Luther King    23\n",
       "Bill Clinton          22\n",
       "Benito Mussolini      21\n",
       "Ron Jeremy            20\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c[c.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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emmett Vogan       37\n",
       "Sam (II) Harris    31\n",
       "Harold Miller      28\n",
       "Bess Flowers       28\n",
       "Franklyn Farnum    27\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.year == 1945].name.value_counts().head()"
   ]
  },
  {
   "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 1985?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mammootty        22\n",
       "Sukumari         20\n",
       "Shakti Kapoor    19\n",
       "Lou Scheimer     15\n",
       "Raj Babbar       15\n",
       "Mohanlal         15\n",
       "Om Shivpuri      13\n",
       "Asrani           13\n",
       "Rajesh Khanna    13\n",
       "Aruna Irani      13\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 27,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaad92c8>"
      ]
     },
     "execution_count": 32,
     "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": [
    "cast[cast.name == 'Mammootty'].year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patron in Frisky Rabbit         16\n",
       "Patron in Chinese Restaurant     9\n",
       "Patron in the Coffee House       8\n",
       "Patron in Billiard Parlor        5\n",
       "Patron in Restaurant             3\n",
       "Patron in restaurant             3\n",
       "Patron in cabaret                3\n",
       "Patron in Club                   3\n",
       "Patron in Bar                    3\n",
       "Patron in Audience               2\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c[c.character.str.startswith('Patron in ')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science Teacher         57\n",
       "Science Fair Student     9\n",
       "Science Fair Judge       6\n",
       "Science Student          6\n",
       "Science Reporter         5\n",
       "Science Fair Kid         5\n",
       "Science Kid              5\n",
       "Science                  4\n",
       "Science Officer          3\n",
       "Science Fair Teacher     3\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c[c.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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad14628>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.name == 'Judi Dench']\n",
    "c.plot(x='year', y='n', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot the n-values of Cary Grant's roles through his career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad37970>"
      ]
     },
     "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": [
    "c = cast\n",
    "c = c[c.name == 'Cary Grant'].sort_values('year')\n",
    "c = c[c.n.notnull()]\n",
    "c.plot(x='year', y='n', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad79b38>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.name == 'Sidney Poitier'].sort_values('year')\n",
    "c = c[c.n.notnull()]\n",
    "c.plot(x='year', y='n', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      6482\n",
       "actress    2985\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year // 10 == 195]\n",
    "c = c[c.n == 1]\n",
    "c.type.value_counts()"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      4520\n",
       "actress    4496\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year // 10 == 195]\n",
    "c = c[c.n == 2]\n",
    "c.type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}