Solutions-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
       "    border-right: 1px dashed #888;\n",
       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>Tomorrow Ends at Dawn</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brothers of the West</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nemo</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pereezd</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bad for Business</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title  year\n",
       "0  Tomorrow Ends at Dawn  2002\n",
       "1   Brothers of the West  1937\n",
       "2                   Nemo  1984\n",
       "3                Pereezd  2014\n",
       "4       Bad for Business  2007"
      ]
     },
     "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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gangsta Rap: The Glockumentary</td>\n",
       "      <td>2007</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Himself</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Menace II Society</td>\n",
       "      <td>1993</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Lew-Loc</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Porndogs: The Adventures of Sadie</td>\n",
       "      <td>2009</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bosco</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stop Pepper Palmer</td>\n",
       "      <td>2014</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Himself</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  year       name   type character   n\n",
       "0                    Suuri illusioni  1985     Homo $  actor    Guests  22\n",
       "1     Gangsta Rap: The Glockumentary  2007  Too $hort  actor   Himself NaN\n",
       "2                  Menace II Society  1993  Too $hort  actor   Lew-Loc  27\n",
       "3  Porndogs: The Adventures of Sadie  2009  Too $hort  actor     Bosco   3\n",
       "4                 Stop Pepper Palmer  2014  Too $hort  actor   Himself NaN"
      ]
     },
     "execution_count": 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": [
    {
     "data": {
      "text/plain": [
       "Hamlet                  19\n",
       "Carmen                  13\n",
       "Macbeth                 12\n",
       "The Three Musketeers    12\n",
       "She                     11\n",
       "Maya                    11\n",
       "The Outsider            11\n",
       "Karma                   10\n",
       "Anna Karenina           10\n",
       "Temptation              10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the ten most common movie names of all time?\n",
    "\n",
    "titles.title.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1937    1180\n",
       "1936    1116\n",
       "1938    1115\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which three years of the 1930s saw the most films released?\n",
    "\n",
    "t = titles\n",
    "t = t[t.year // 10 == 193]\n",
    "t.year.value_counts().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275b321160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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g75I/uPw6zz35I5G/DviPVK8fTI4dC1wG3D3X82tf+wC7Jf2apAup3oz7Dtj7\nBpKeXpAqfiDpV0vm+cAuAJd3bSZ/oPl1nnvyh59/EdV7FP5S0jOSnqF6P8fPARfO+fxe/3oM+0T1\nCYDjVO9yPInqKeTzwNeAf9aH/H8KfJWqZ/sK8E/K+DHApckfXH6d55784eeXrNOBtwFHTBlfMtfz\ne57cKJ+A9ww4/73JH05+neee/IOTD1xK9Y7Y24HHgAtaLuvHu9vrnT/If8Bhn4BtyW9mfp3nnvyD\nkw98HXhF2T6Z6iM2PlTO92NxrnV+7d/hq33v0GxnQfLrm1/nuSd/+PlURzN+H8D2RHkd8DZJJ9Gf\nj9eodX7tF3/g54ElVMfDTvU3ya91fp3nnvzh5z8l6fUu7263/X1Jvw5cD7xuruc3YfH/EtVTowen\nXiDpL5Nf6/w6zz35w8//V8B+/4eF7d2SLgb+ZK7n1/5NXhERMXtNOM4/IiJmKYt/RMQclMU/ImIO\nyuIfETEHZfGPiJiD/j+z6rMgx/PIsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27642ee080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of films that have been released each decade\n",
    "# over the history of cinema.\n",
    "\n",
    "t = titles\n",
    "(t.year // 10 * 10).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275ce86e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hfa8/6TFwgaRmt1+PL6De8X3f32s/3pMdz+D+Zv3n5/KEPcykp/XzM//jUZjHfnFiy9rr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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275ce916a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of \"Hamlet\" films made each decade.\n",
    "\n",
    "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275ce0f710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KBxiQca6cqZuMmSBnrknNtGTBlnSUpH3ALLAnIu4DNkTEbLPKLLBhhBnNzIwOJ+GNiKeB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\nYJuZ9YQLtplZT/x/cAQs4JeasCYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cddeb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of \"Rustler\" characters\n",
    "# in each decade of the history of film.\n",
    "\n",
    "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275cda26d8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEOCAYAAACtoy3oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW5ImwoEtSRPhwJakifh/yZxN/5sWA68AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cd736d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of \"Hamlet\" characters each decade.\n",
    "\n",
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Himself        18630\n",
       "Dancer         10864\n",
       "Extra           8474\n",
       "Reporter        7483\n",
       "Doctor          6643\n",
       "Policeman       6399\n",
       "Student         6158\n",
       "Nurse           6039\n",
       "Bartender       5986\n",
       "Party Guest     5620\n",
       "Zombie          5573\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the 11 most common character names in movie history?\n",
    "\n",
    "cast.character.value_counts().head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Joyce Brothers         14\n",
       "Queen Elizabeth II     10\n",
       "Margaret Thatcher       8\n",
       "Joan Rivers             7\n",
       "Mary Jo Pehl            7\n",
       "Kareena Kapoor          5\n",
       "Chris Evert             5\n",
       "Sally Jessy Raphael     5\n",
       "Caroline Rhea           5\n",
       "Marilyn Monroe          5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who are the 10 people most often credited as \"Herself\" in film history?\n",
    "\n",
    "c = cast\n",
    "c[c.character == 'Herself'].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adolf Hitler             91\n",
       "Richard Nixon            38\n",
       "Ronald Reagan            28\n",
       "John F. Kennedy          26\n",
       "Ron Jeremy               22\n",
       "George W. Bush           20\n",
       "Franklin D. Roosevelt    20\n",
       "Bill Clinton             20\n",
       "Winston Churchill        20\n",
       "Martin Luther King       19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who are the 10 people most often credited as \"Himself\" in film history?\n",
    "\n",
    "c = cast\n",
    "c[c.character == 'Himself'].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emmett Vogan       39\n",
       "Sam (II) Harris    30\n",
       "Bess Flowers       28\n",
       "Harold Miller      28\n",
       "Nolan Leary        27\n",
       "Frank O'Connor     26\n",
       "Franklyn Farnum    24\n",
       "Edmund Cobb        24\n",
       "Tom London         24\n",
       "Pierre Watkin      24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which actors or actresses appeared in the most movies in the year 1945?\n",
    "\n",
    "cast[cast.year == 1945].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shakti Kapoor       19\n",
       "Mammootty           17\n",
       "Sukumari            16\n",
       "Lou Scheimer        15\n",
       "Aruna Irani         14\n",
       "Rajesh Khanna       13\n",
       "Mohanlal            13\n",
       "Deven Verma         13\n",
       "Raj Babbar          13\n",
       "Satyendra Kapoor    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which actors or actresses appeared in the most movies in the year 1985?\n",
    "\n",
    "cast[cast.year == 1985].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275ccfb240>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aLsiAG8CMpQcPq2vE57oaU8lEvQpUpgAvIusDB6rqxQCq+o6qRhOAYvJI6+nS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\nwzCMimIB3jAMo6JYgDcMw6go/wObLdtwLadeBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cc8c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot how many roles Mammootty has played in each year of his career.\n",
    "\n",
    "cast[cast.name == 'Mammootty'].year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patron in Frisky Rabbit         16\n",
       "Patron in the Coffee House       9\n",
       "Patron in Chinese Restaurant     9\n",
       "Patron in Billiard Parlor        5\n",
       "Patron in Bar                    4\n",
       "Patron in cabaret                3\n",
       "Patron in restaurant             3\n",
       "Patron in Restaurant             3\n",
       "Patron in Club                   3\n",
       "Patron in Coffee Shop            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the 10 most frequent roles that start with the phrase \"Patron in\"?\n",
    "\n",
    "c = cast\n",
    "c[c.character.str.startswith('Patron in ')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science Teacher         53\n",
       "Science Fair Student     8\n",
       "Science Student          7\n",
       "Science Fair Judge       6\n",
       "Science Club Member      5\n",
       "Science Reporter         5\n",
       "Science Kid              4\n",
       "Science Promo Cadet      4\n",
       "Science Officer          3\n",
       "Science teacher          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the 10 most frequent roles that start with the word \"Science\"?\n",
    "\n",
    "c = cast\n",
    "c[c.character.str.startswith('Science')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275cbfc048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cc04d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the n-values of the roles that Judi Dench has played over her career.\n",
    "\n",
    "c = cast\n",
    "c = c[c.name == 'Judi Dench']\n",
    "c.plot(x='year', y='n', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275cc36160>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cd58a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the n-values of Cary Grant's roles through his career.\n",
    "\n",
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f275cbac908>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ps3R1icftu+3DJ+jzspfxjrmstrcfp740t7dvvflaPKX6Mt+HY70HVso2qc9Oh5gqiogN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\nQEiSelkgJEm9/j93PT1iJVA2HgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275cb3a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the n-value of the roles that Sidney Poitier has acted\n",
    "# over the years.\n",
    "\n",
    "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      6349\n",
       "actress    2789\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many leading (n=1) roles were available to actors,\n",
    "# and how many to actresses, in the 1950s?\n",
    "\n",
    "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actress    4375\n",
       "actor      4354\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many supporting (n=2) roles were available to actors,\n",
    "# and how many to actresses, in the 1950s?\n",
    "\n",
    "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}