IMDB analysis 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>The Rising Son</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Thousand Plane Raid</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Spider and the Fly</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gai shi dao wang</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  year\n",
       "0           The Rising Son  1990\n",
       "1  The Thousand Plane Raid  1969\n",
       "2   The Spider and the Fly  1949\n",
       "3                  Country  2000\n",
       "4         Gai shi dao wang  1969"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles=pd.DataFrame.from_csv(r'C:\\Users\\abhij\\Desktop\\pycon-pandas-tutorial-master\\data\\titles.csv',index_col=None)\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast=pd.DataFrame.from_csv(r'C:\\Users\\abhij\\Desktop\\pycon-pandas-tutorial-master\\data\\cast.csv',index_col=None)\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>country</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#73, Shaanthi Nivaasa</td>\n",
       "      <td>2007</td>\n",
       "      <td>India</td>\n",
       "      <td>2007-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#Beings</td>\n",
       "      <td>2015</td>\n",
       "      <td>Romania</td>\n",
       "      <td>2015-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#Ewankosau saranghaeyo</td>\n",
       "      <td>2015</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2015-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#Horror</td>\n",
       "      <td>2015</td>\n",
       "      <td>USA</td>\n",
       "      <td>2015-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#Horror</td>\n",
       "      <td>2015</td>\n",
       "      <td>UK</td>\n",
       "      <td>2016-05-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title  year      country        date\n",
       "0   #73, Shaanthi Nivaasa  2007        India  2007-06-15\n",
       "1                 #Beings  2015      Romania  2015-01-29\n",
       "2  #Ewankosau saranghaeyo  2015  Philippines  2015-01-21\n",
       "3                 #Horror  2015          USA  2015-11-20\n",
       "4                 #Horror  2015           UK  2016-05-16"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates=pd.DataFrame.from_csv(r'C:\\Users\\abhij\\Desktop\\pycon-pandas-tutorial-master\\data\\release_dates.csv',index_col=None)\n",
    "dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the ten most common movie names of all time?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hamlet                  20\n",
       "Carmen                  17\n",
       "Macbeth                 15\n",
       "Maya                    12\n",
       "The Outsider            12\n",
       "Freedom                 11\n",
       "Othello                 11\n",
       "The Three Musketeers    11\n",
       "Paradise                10\n",
       "Vengeance               10\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles.title.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Which three years of the 1930s saw the most films released?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1=titles[titles.year//10==193]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1937    1202\n",
       "1936    1143\n",
       "1938    1139\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.year.value_counts().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the number of films that have been released each decade over the history of cinema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23189630>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iymopHCS9jCoYPmf7C6X8tKTZZf5s4GCp7wfmNaw+t9T2l+nm+gnrSJoOnAEcah6H7Ztt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\np3dkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d15cdb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(t.year//10*10).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Plot the number of \"Rustler\" characters in each decade of the history of film"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "rust=cast[cast.character=='Rustler']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2f7a50>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nMnhD676qenhMGc4EHjv5P1qSVwEvBu4e85kb31NVnxrXzz+VJN8OPFJVDyfZCswAh0b8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\n1WpNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d303710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(rust.year//10*10).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the number of \"Hamlet\" characters each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham=cast[cast.character=='Hamlet'].sort_values('n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x237ff2d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sqtrM4EN7HwP8nMHvQ9/3dXW5HwI+lGQNg0/7uqL7d/+dnnJ3dH+0fslgrj/uBnP/YJm5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x237ffa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ham.year//10*10).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the number of \"Hamlet\" films made each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham1=cast[cast.title=='Hamlet'].sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23918a50>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z5mX5W8wbx6+qI+Padh9y9LrQj+vDX1IfMvQlRx8y9CVHHzL0JcekM3Tj1Bfz/Qdmv1A9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\nRljoktQIC12SGvH/ARY1frwhHrTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2382ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ham1.year//10*10).value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the 11 most common character names in movie history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Himself        20727\n",
       "Dancer         12257\n",
       "Extra          11650\n",
       "Reporter        8338\n",
       "Doctor          7588\n",
       "Student         7568\n",
       "Party Guest     7063\n",
       "Policeman       6987\n",
       "Nurse           6820\n",
       "Bartender       6744\n",
       "Herself         6549\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cast.character).value_counts().head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who are the 10 people most often credited as \"Herself\" in film history?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "herself=cast[cast.character=='Herself'].name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Queen Elizabeth II    12\n",
       "Margaret Thatcher      9\n",
       "Laura (II) M?gica      9\n",
       "Mary Jo Pehl           9\n",
       "Joyce Brothers         9\n",
       "Marina Revilla         8\n",
       "Marta Berrocal         8\n",
       "Estrella Borja         8\n",
       "In?s J. Southern       8\n",
       "Luisa Horga            7\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "herself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who are the 10 people most often credited as \"Himself\" in film history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "himself=cast[cast.character=='Himself'].name.value_counts().head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adolf Hitler          99\n",
       "Richard Nixon         46\n",
       "Ronald Reagan         40\n",
       "John F. Kennedy       36\n",
       "George W. Bush        24\n",
       "Winston Churchill     23\n",
       "Ron Jeremy            23\n",
       "Bill Clinton          22\n",
       "Benito Mussolini      22\n",
       "Martin Luther King    22\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "himself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which actors or actresses appeared in the most movies in the year 1945?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "most=cast[((cast.type=='actor') | (cast.type=='actress')) & (cast.year==1945)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emmett Vogan    39\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most.name.value_counts().head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which actors or actresses appeared in the most movies in the year 1985?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mammootty    22\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most=cast[((cast.type=='actor') | (cast.type=='actress')) & (cast.year==1985)]\n",
    "most.name.value_counts().head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot how many roles Mammootty has played in each year of his career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c50510>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lIx7XkRW5Zmpaf/itE+l24JBF50WjugDaihW2vWTRgW6MedsYc2Hycp4x5ie2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23d971d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[cast.name=='Mammootty'].year.value_counts().sort_index().plot(c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the 10 most frequent roles that start with the phrase \"Patron in\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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 Restaurant             3\n",
       "Patron in cabaret                3\n",
       "Patron in Club                   3\n",
       "Patron in Coffee Shop            2\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character.str.startswith('Patron in')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the 10 most frequent roles that start with the word \"Science\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science Teacher         60\n",
       "Science Student          9\n",
       "Science Fair Student     9\n",
       "Science Promo Cadet      5\n",
       "Science Fair Judge       5\n",
       "Science Club Member      5\n",
       "Science Kid              5\n",
       "Science Reporter         5\n",
       "Science Fair Kid         4\n",
       "Science                  4\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character.str.startswith('Science')].character.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the n-values of the roles that Judi Dench has played over her career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "judi=cast[cast.name=='Judi Dench'].sort_values('year')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2459c5f0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23acc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "judi=judi[judi.n.notnull()]\n",
    "judi.plot(x='year',y='n',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the n-value of the roles that Sidney Poitier has acted over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VXHnpMgaPn6J7yQKHhZm1hSLOkloO3C3p7Pv/SUTcJ+nDABGxE9gLbAQGgJeB3yigzpqW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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x455fc090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sid=cast[cast.name=='Sidney Poitier'].sort_values('year')\n",
    "sid=sid[sid.n.notnull()]\n",
    "sid=sid.plot(x='year',y='n',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many leading (n=1) roles were available to actors, and how many to actresses, in the 1950s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=cast\n",
    "c=c[c.year//10 ==195]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=c[c.n==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      6693\n",
       "actress    3004\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many supporting (n=2) roles were available to actors, and how many to actresses, in the 1950s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=cast\n",
    "c=c[c.year//10==195]\n",
    "c=c[c.n==2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      4621\n",
       "actress    4606\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}