IMDB Data Analysis 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "    </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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "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": 21,
   "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>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": 21,
     "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": 23,
   "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": 23,
     "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates['date']=pd.to_datetime(dates['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a bar plot of the months in which movies with \"Christmas\" in their title tend to be released in the USA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3c7a7d70>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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7yUVV9Yk+6nZf/0+BW6vqs4vGNwF/WVUbeqlroEualD7vWjsU6/Zd20CXNDFJHq2qU4+U\nun3XPlRfFJXUiEndtTbBu+UmVttAl9S3Sd21Nqm6E6ttoEvq26cY3Nlx7+IVSW5vsO7EansNXZIa\n4YdzSVIjDHRJaoSBLkmNMNAlqREGuiQ14v8BRvp9elBqZ3MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c352f70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=dates\n",
    "d=d[(d.title.str.contains('Christmas')) & (d.country=='USA')]\n",
    "d.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a bar plot of the months in which movies whose titles start with \"The Hobbit\" are released in the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3c7a7970>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD/CAYAAAAKVJb/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c352cf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=dates\n",
    "d=d[(d.title.str.startswith('The Hobbit')) & (d.country=='USA')]\n",
    "d.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a bar plot of the day of the week on which movies with \"Romance\" in their title tend to be released in the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3c90a3d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c9035f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=dates\n",
    "d=d[(d.title.str.contains('Romance')) & (d.country=='USA')]\n",
    "d.date.dt.dayofweek.value_counts().sort_index().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a bar plot of the day of the week on which movies with \"Action\" in their title tend to be released in the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e2fb30>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c56aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=dates\n",
    "d=d[(d.title.str.contains('Action')) & (d.country=='USA')]\n",
    "d.date.dt.dayofweek.value_counts().sort_index().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On which date was each Judi Dench movie from the 1990s released in the USA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>1995</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1995-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jack &amp; Sarah</td>\n",
       "      <td>1995</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>3.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1996-03-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>1996</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Hecuba</td>\n",
       "      <td>12.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1996-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs Brown</td>\n",
       "      <td>1997</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Queen Victoria</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1997-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tomorrow Never Dies</td>\n",
       "      <td>1997</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1997-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shakespeare in Love</td>\n",
       "      <td>1998</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>12.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1999-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tea with Mussolini</td>\n",
       "      <td>1999</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>Arabella</td>\n",
       "      <td>2.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1999-05-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The World Is Not Enough</td>\n",
       "      <td>1999</td>\n",
       "      <td>Judi Dench</td>\n",
       "      <td>actress</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1999-11-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  year        name     type        character     n  \\\n",
       "0                GoldenEye  1995  Judi Dench  actress                M   6.0   \n",
       "2             Jack & Sarah  1995  Judi Dench  actress         Margaret   3.0   \n",
       "1                   Hamlet  1996  Judi Dench  actress           Hecuba  12.0   \n",
       "3                Mrs Brown  1997  Judi Dench  actress   Queen Victoria   1.0   \n",
       "7      Tomorrow Never Dies  1997  Judi Dench  actress                M   9.0   \n",
       "4      Shakespeare in Love  1998  Judi Dench  actress  Queen Elizabeth  12.0   \n",
       "5       Tea with Mussolini  1999  Judi Dench  actress         Arabella   2.0   \n",
       "6  The World Is Not Enough  1999  Judi Dench  actress                M   6.0   \n",
       "\n",
       "  country       date  \n",
       "0     USA 1995-11-17  \n",
       "2     USA 1996-03-22  \n",
       "1     USA 1996-12-25  \n",
       "3     USA 1997-10-03  \n",
       "7     USA 1997-12-19  \n",
       "4     USA 1999-01-08  \n",
       "5     USA 1999-05-14  \n",
       "6     USA 1999-11-19  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa=dates[dates.country=='USA']\n",
    "c=cast\n",
    "c=c[c.name=='Judi Dench']\n",
    "c=c[c.year//10==199]\n",
    "c.merge(usa).sort_values('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In which months do films with Judi Dench tend to be released in the USA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3c558430>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WO2882+IvRSW9CGzpR7S1Pg8MuqStZ0s/oq31eQZd0lbzKUaP9lg3/4Ik1ztvfB5Dl6RG\n+OJcktQIgy5JjTDoktQIgy5JjTDoktSI/wPPA01K2Y9B1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2ca9f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa=dates[dates.country=='USA']\n",
    "c=cast\n",
    "c=c[c.name=='Judi Dench']\n",
    "c=c.merge(usa).sort_values('date')\n",
    "c.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In which months do films with Tom Cruise tend to be released in the USA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2375c0b0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD/CAYAAADhYy38AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZKKMOiSVMT/AjieHera0/ZJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de6ab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa=dates[dates.country=='USA']\n",
    "c=cast\n",
    "c=c[c.name=='Tom Cruise']\n",
    "c=c.merge(usa).sort_values('date')\n",
    "c.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "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",
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