attachment_EDA-Python-02 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://shwetkm.github.io/upxlogo.png\"></img>\n",
    "\n",
    "# Exploratory Data Analysis using Python(Part 02) - Data Science Foundation\n",
    "\n",
    "\n",
    "**SET THE WORKING DIRECTORY SO THAT WE DO NOT HAVE TO THINK ABOUT PATH PROBLEMS**\n",
    "\n",
    "Please ensure all your folder names <font color='red'><b>do not have a space & use forward slash (/)</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "your_local_path=\"C:/sandbox/python/Datasets/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Techniques/questions to look at in this module:\n",
    "<b><i>\n",
    "+ How to merge datasets and different merge techniques\n",
    "+ List of movies that are rated most by users\n",
    "+ In ratings, how to address ratings sparsity problem\n",
    "+ Plot distributions\n",
    "+ Identify different age groups and technique of binning\n",
    "+ Subgrouping and unstacking\n",
    "+ Differences in ratings by gender\n",
    "</b></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Pass column names in names for each CSV\n",
    "\n",
    "# Load the users data\n",
    "u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']\n",
    "users = pd.read_csv(your_local_path+'u.user', sep='|', names=u_cols,\n",
    "                    encoding='latin-1')\n",
    "\n",
    "# Load the ratings data\n",
    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
    "ratings = pd.read_csv(your_local_path+'u.data', sep='\\t', names=r_cols,\n",
    "                      encoding='latin-1')\n",
    "\n",
    "# the movies file contains columns indicating the movie's genres\n",
    "# let's only load the first five columns of the file with usecols\n",
    "\n",
    "# Load the movies data\n",
    "m_cols = ['movie_id', 'title', 'release_date', 'video_release_date', 'imdb_url']\n",
    "movies = pd.read_csv(your_local_path+'u.item', sep='|', names=m_cols, usecols=range(5),\n",
    "                     encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 5)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the structure of Users data\n",
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067\n",
       "3        4   24   M  technician    43537\n",
       "4        5   33   F       other    15213"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check few sample values of user data\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 943 entries, 0 to 942\n",
      "Data columns (total 5 columns):\n",
      "user_id       943 non-null int64\n",
      "age           943 non-null int64\n",
      "sex           943 non-null object\n",
      "occupation    943 non-null object\n",
      "zip_code      943 non-null object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 36.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get information on user data\n",
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                int64\n",
       "title                  object\n",
       "release_date           object\n",
       "video_release_date    float64\n",
       "imdb_url               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data type information of movies data\n",
    "movies.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>472.000000</td>\n",
       "      <td>34.051962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>272.364951</td>\n",
       "      <td>12.192740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>236.500000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>472.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>707.500000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id         age\n",
       "count  943.000000  943.000000\n",
       "mean   472.000000   34.051962\n",
       "std    272.364951   12.192740\n",
       "min      1.000000    7.000000\n",
       "25%    236.500000   25.000000\n",
       "50%    472.000000   31.000000\n",
       "75%    707.500000   43.000000\n",
       "max    943.000000   73.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the spread of the users data\n",
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "Name: movie_id, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get sample movie ids\n",
    "movies['movie_id'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id              title\n",
       "0         1   Toy Story (1995)\n",
       "1         2   GoldenEye (1995)\n",
       "2         3  Four Rooms (1995)\n",
       "3         4  Get Shorty (1995)\n",
       "4         5     Copycat (1995)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting a subset of columns from the movies data\n",
    "movies[['movie_id','title']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>938</td>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>technician</td>\n",
       "      <td>55038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>33319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>940</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>941</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>943</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>77841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  age sex     occupation zip_code\n",
       "937      938   38   F     technician    55038\n",
       "938      939   26   F        student    33319\n",
       "939      940   32   M  administrator    02215\n",
       "940      941   20   M        student    97229\n",
       "942      943   22   M        student    77841"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another example of subsetting (Putting a condition)\n",
    "users[users.age<40].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>30329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>06405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  age sex occupation zip_code\n",
       "4         5   33   F      other    15213\n",
       "10       11   39   F      other    30329\n",
       "11       12   28   F      other    06405"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More conditions\n",
    "users[(users.age < 40) & (users.sex == 'F')].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067\n",
       "3        4   24   M  technician    43537\n",
       "4        5   33   F       other    15213"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the index for the users dataset. Can run only once, do not run more than once, else you shall get an error.\n",
    "users.set_index('user_id', inplace=False)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>85711</td>\n",
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       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067\n",
       "3        4   24   M  technician    43537\n",
       "4        5   33   F       other    15213"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you wish then please reset the index\n",
    "users.reset_index(inplace=False)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1682, 5)\n",
      "8410\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id              title release_date  video_release_date  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3         4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4         5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  \n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...  \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...  \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...  \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...  \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge datasets - Movies, ratings & users\n",
    "print(movies.shape)\n",
    "print(movies.size)\n",
    "movies.head()    # movie_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 4)\n",
      "400000\n"
     ]
    }
   ],
   "source": [
    "ratings.head()\n",
    "print(ratings.shape)\n",
    "print(ratings.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 5)\n",
      "4715\n"
     ]
    }
   ],
   "source": [
    "users.head()\n",
    "print(users.shape)\n",
    "print(users.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736532</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737890</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887739608</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Twelve Monkeys (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Twelve%20Monk...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887738847</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Babe (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Babe%20(1995)</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887736696</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Dead%20Man%20...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887737194</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>Seven (Se7en) (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Se7en%20(1995)</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737837</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>14-Aug-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Usual%20Suspe...</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737243</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>Mr. Holland's Opus (1995)</td>\n",
       "      <td>29-Jan-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Mr.%20Holland...</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887739426</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17</td>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>05-Feb-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?From%20Dusk%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887739056</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>Antonia's Line (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Antonia%20(1995)</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887737383</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21</td>\n",
       "      <td>Muppet Treasure Island (1996)</td>\n",
       "      <td>16-Feb-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Muppet%20Trea...</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887740729</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>16-Feb-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Braveheart%20...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887737647</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23</td>\n",
       "      <td>Taxi Driver (1976)</td>\n",
       "      <td>16-Feb-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Taxi%20Driver...</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737293</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24</td>\n",
       "      <td>Rumble in the Bronx (1995)</td>\n",
       "      <td>23-Feb-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Hong%20Faan%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887738057</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Birdcage, The (1996)</td>\n",
       "      <td>08-Mar-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Birdcage,%20T...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887740649</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Apollo%2013%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887737036</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>Belle de jour (1967)</td>\n",
       "      <td>01-Jan-1967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Belle%20de%20...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887738933</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31</td>\n",
       "      <td>Crimson Tide (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Crimson%20Tid...</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887739472</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>32</td>\n",
       "      <td>Crumb (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Crumb%20(1994)</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737432</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42</td>\n",
       "      <td>Clerks (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Clerks%20(1994)</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887738191</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44</td>\n",
       "      <td>Dolores Claiborne (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Dolores%20Cla...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887740451</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>45</td>\n",
       "      <td>Eat Drink Man Woman (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Yinshi%20Nan%...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736843</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47</td>\n",
       "      <td>Ed Wood (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Ed%20Wood%20(...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887738933</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48</td>\n",
       "      <td>Hoop Dreams (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Hoop%20Dreams...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736880</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>49</td>\n",
       "      <td>I.Q. (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?I.Q.%20(1994)</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887740833</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "      <td>01-Jan-1977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Star%20Wars%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887737431</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>Outbreak (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Outbreak%20(1...</td>\n",
       "      <td>308</td>\n",
       "      <td>2</td>\n",
       "      <td>887740254</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>55</td>\n",
       "      <td>Professional, The (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?L%E9on+(1994)</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
       "      <td>887738760</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>56</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Pulp%20Fictio...</td>\n",
       "      <td>308</td>\n",
       "      <td>5</td>\n",
       "      <td>887736924</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99970</th>\n",
       "      <td>332</td>\n",
       "      <td>Kiss the Girls (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Kiss+the+Girl...</td>\n",
       "      <td>631</td>\n",
       "      <td>3</td>\n",
       "      <td>888465180</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99971</th>\n",
       "      <td>334</td>\n",
       "      <td>U Turn (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?U+Turn+(1997)</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888464941</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99972</th>\n",
       "      <td>338</td>\n",
       "      <td>Bean (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Bean+(1997)</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888465299</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99973</th>\n",
       "      <td>346</td>\n",
       "      <td>Jackie Brown (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</td>\n",
       "      <td>631</td>\n",
       "      <td>4</td>\n",
       "      <td>888465004</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99974</th>\n",
       "      <td>682</td>\n",
       "      <td>I Know What You Did Last Summer (1997)</td>\n",
       "      <td>17-Oct-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?I+Know+What+Y...</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888465247</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99975</th>\n",
       "      <td>873</td>\n",
       "      <td>Picture Perfect (1997)</td>\n",
       "      <td>01-Aug-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Picture+Perfe...</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888465084</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99976</th>\n",
       "      <td>877</td>\n",
       "      <td>Excess Baggage (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Excess+Baggag...</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888465131</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99977</th>\n",
       "      <td>886</td>\n",
       "      <td>Life Less Ordinary, A (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Life+Less+Ord...</td>\n",
       "      <td>631</td>\n",
       "      <td>4</td>\n",
       "      <td>888465216</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99978</th>\n",
       "      <td>1527</td>\n",
       "      <td>Senseless (1998)</td>\n",
       "      <td>09-Jan-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-12...</td>\n",
       "      <td>631</td>\n",
       "      <td>2</td>\n",
       "      <td>888465351</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>38866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99979</th>\n",
       "      <td>272</td>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99980</th>\n",
       "      <td>288</td>\n",
       "      <td>Scream (1996)</td>\n",
       "      <td>20-Dec-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Scream%20(1996)</td>\n",
       "      <td>729</td>\n",
       "      <td>2</td>\n",
       "      <td>893286261</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99981</th>\n",
       "      <td>294</td>\n",
       "      <td>Liar Liar (1997)</td>\n",
       "      <td>21-Mar-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?Liar+Liar+(1997)</td>\n",
       "      <td>729</td>\n",
       "      <td>2</td>\n",
       "      <td>893286338</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99982</th>\n",
       "      <td>300</td>\n",
       "      <td>Air Force One (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Air+Force+One...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99983</th>\n",
       "      <td>310</td>\n",
       "      <td>Rainmaker, The (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Rainmaker,+Th...</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>893286204</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99984</th>\n",
       "      <td>313</td>\n",
       "      <td>Titanic (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-12...</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99985</th>\n",
       "      <td>322</td>\n",
       "      <td>Murder at 1600 (1997)</td>\n",
       "      <td>18-Apr-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Murder%20at%2...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286637</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>328</td>\n",
       "      <td>Conspiracy Theory (1997)</td>\n",
       "      <td>08-Aug-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Conspiracy+Th...</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99987</th>\n",
       "      <td>333</td>\n",
       "      <td>Game, The (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Game%2C+The+(...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>338</td>\n",
       "      <td>Bean (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Bean+(1997)</td>\n",
       "      <td>729</td>\n",
       "      <td>1</td>\n",
       "      <td>893286373</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99989</th>\n",
       "      <td>346</td>\n",
       "      <td>Jackie Brown (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</td>\n",
       "      <td>729</td>\n",
       "      <td>1</td>\n",
       "      <td>893286168</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99990</th>\n",
       "      <td>354</td>\n",
       "      <td>Wedding Singer, The (1998)</td>\n",
       "      <td>13-Feb-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Wedding+Singe...</td>\n",
       "      <td>729</td>\n",
       "      <td>5</td>\n",
       "      <td>893286637</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99991</th>\n",
       "      <td>362</td>\n",
       "      <td>Blues Brothers 2000 (1998)</td>\n",
       "      <td>06-Feb-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Blues+Brother...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286637</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99992</th>\n",
       "      <td>683</td>\n",
       "      <td>Rocket Man (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Rocket+Man+(1...</td>\n",
       "      <td>729</td>\n",
       "      <td>2</td>\n",
       "      <td>893286511</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99993</th>\n",
       "      <td>689</td>\n",
       "      <td>Jackal, The (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Jackal%2C+The...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>690</td>\n",
       "      <td>Seven Years in Tibet (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Seven+Years+i...</td>\n",
       "      <td>729</td>\n",
       "      <td>2</td>\n",
       "      <td>893286149</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>748</td>\n",
       "      <td>Saint, The (1997)</td>\n",
       "      <td>14-Mar-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Saint%2C%20Th...</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>893286638</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>751</td>\n",
       "      <td>Tomorrow Never Dies (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-12...</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>893286338</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>879</td>\n",
       "      <td>Peacemaker, The (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Peacemaker%2C...</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>893286299</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>894</td>\n",
       "      <td>Home Alone 3 (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</td>\n",
       "      <td>729</td>\n",
       "      <td>1</td>\n",
       "      <td>893286511</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>901</td>\n",
       "      <td>Mr. Magoo (1997)</td>\n",
       "      <td>25-Dec-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</td>\n",
       "      <td>729</td>\n",
       "      <td>1</td>\n",
       "      <td>893286491</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>56567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id                                   title release_date  \\\n",
       "0             1                        Toy Story (1995)  01-Jan-1995   \n",
       "1             4                       Get Shorty (1995)  01-Jan-1995   \n",
       "2             5                          Copycat (1995)  01-Jan-1995   \n",
       "3             7                   Twelve Monkeys (1995)  01-Jan-1995   \n",
       "4             8                             Babe (1995)  01-Jan-1995   \n",
       "5             9                 Dead Man Walking (1995)  01-Jan-1995   \n",
       "6            11                    Seven (Se7en) (1995)  01-Jan-1995   \n",
       "7            12              Usual Suspects, The (1995)  14-Aug-1995   \n",
       "8            15               Mr. Holland's Opus (1995)  29-Jan-1996   \n",
       "9            17              From Dusk Till Dawn (1996)  05-Feb-1996   \n",
       "10           19                   Antonia's Line (1995)  01-Jan-1995   \n",
       "11           21           Muppet Treasure Island (1996)  16-Feb-1996   \n",
       "12           22                       Braveheart (1995)  16-Feb-1996   \n",
       "13           23                      Taxi Driver (1976)  16-Feb-1996   \n",
       "14           24              Rumble in the Bronx (1995)  23-Feb-1996   \n",
       "15           25                    Birdcage, The (1996)  08-Mar-1996   \n",
       "16           28                        Apollo 13 (1995)  01-Jan-1995   \n",
       "17           30                    Belle de jour (1967)  01-Jan-1967   \n",
       "18           31                     Crimson Tide (1995)  01-Jan-1995   \n",
       "19           32                            Crumb (1994)  01-Jan-1994   \n",
       "20           42                           Clerks (1994)  01-Jan-1994   \n",
       "21           44                Dolores Claiborne (1994)  01-Jan-1994   \n",
       "22           45              Eat Drink Man Woman (1994)  01-Jan-1994   \n",
       "23           47                          Ed Wood (1994)  01-Jan-1994   \n",
       "24           48                      Hoop Dreams (1994)  01-Jan-1994   \n",
       "25           49                             I.Q. (1994)  01-Jan-1994   \n",
       "26           50                        Star Wars (1977)  01-Jan-1977   \n",
       "27           54                         Outbreak (1995)  01-Jan-1995   \n",
       "28           55                Professional, The (1994)  01-Jan-1994   \n",
       "29           56                     Pulp Fiction (1994)  01-Jan-1994   \n",
       "...         ...                                     ...          ...   \n",
       "99970       332                   Kiss the Girls (1997)  01-Jan-1997   \n",
       "99971       334                           U Turn (1997)  01-Jan-1997   \n",
       "99972       338                             Bean (1997)  01-Jan-1997   \n",
       "99973       346                     Jackie Brown (1997)  01-Jan-1997   \n",
       "99974       682  I Know What You Did Last Summer (1997)  17-Oct-1997   \n",
       "99975       873                  Picture Perfect (1997)  01-Aug-1997   \n",
       "99976       877                   Excess Baggage (1997)  01-Jan-1997   \n",
       "99977       886            Life Less Ordinary, A (1997)  01-Jan-1997   \n",
       "99978      1527                        Senseless (1998)  09-Jan-1998   \n",
       "99979       272                Good Will Hunting (1997)  01-Jan-1997   \n",
       "99980       288                           Scream (1996)  20-Dec-1996   \n",
       "99981       294                        Liar Liar (1997)  21-Mar-1997   \n",
       "99982       300                    Air Force One (1997)  01-Jan-1997   \n",
       "99983       310                   Rainmaker, The (1997)  01-Jan-1997   \n",
       "99984       313                          Titanic (1997)  01-Jan-1997   \n",
       "99985       322                   Murder at 1600 (1997)  18-Apr-1997   \n",
       "99986       328                Conspiracy Theory (1997)  08-Aug-1997   \n",
       "99987       333                        Game, The (1997)  01-Jan-1997   \n",
       "99988       338                             Bean (1997)  01-Jan-1997   \n",
       "99989       346                     Jackie Brown (1997)  01-Jan-1997   \n",
       "99990       354              Wedding Singer, The (1998)  13-Feb-1998   \n",
       "99991       362              Blues Brothers 2000 (1998)  06-Feb-1998   \n",
       "99992       683                       Rocket Man (1997)  01-Jan-1997   \n",
       "99993       689                      Jackal, The (1997)  01-Jan-1997   \n",
       "99994       690             Seven Years in Tibet (1997)  01-Jan-1997   \n",
       "99995       748                       Saint, The (1997)  14-Mar-1997   \n",
       "99996       751              Tomorrow Never Dies (1997)  01-Jan-1997   \n",
       "99997       879                  Peacemaker, The (1997)  01-Jan-1997   \n",
       "99998       894                     Home Alone 3 (1997)  01-Jan-1997   \n",
       "99999       901                        Mr. Magoo (1997)  25-Dec-1997   \n",
       "\n",
       "       video_release_date                                           imdb_url  \\\n",
       "0                     NaN  http://us.imdb.com/M/title-exact?Toy%20Story%2...   \n",
       "1                     NaN  http://us.imdb.com/M/title-exact?Get%20Shorty%...   \n",
       "2                     NaN  http://us.imdb.com/M/title-exact?Copycat%20(1995)   \n",
       "3                     NaN  http://us.imdb.com/M/title-exact?Twelve%20Monk...   \n",
       "4                     NaN     http://us.imdb.com/M/title-exact?Babe%20(1995)   \n",
       "5                     NaN  http://us.imdb.com/M/title-exact?Dead%20Man%20...   \n",
       "6                     NaN    http://us.imdb.com/M/title-exact?Se7en%20(1995)   \n",
       "7                     NaN  http://us.imdb.com/M/title-exact?Usual%20Suspe...   \n",
       "8                     NaN  http://us.imdb.com/M/title-exact?Mr.%20Holland...   \n",
       "9                     NaN  http://us.imdb.com/M/title-exact?From%20Dusk%2...   \n",
       "10                    NaN  http://us.imdb.com/M/title-exact?Antonia%20(1995)   \n",
       "11                    NaN  http://us.imdb.com/M/title-exact?Muppet%20Trea...   \n",
       "12                    NaN  http://us.imdb.com/M/title-exact?Braveheart%20...   \n",
       "13                    NaN  http://us.imdb.com/M/title-exact?Taxi%20Driver...   \n",
       "14                    NaN  http://us.imdb.com/M/title-exact?Hong%20Faan%2...   \n",
       "15                    NaN  http://us.imdb.com/M/title-exact?Birdcage,%20T...   \n",
       "16                    NaN  http://us.imdb.com/M/title-exact?Apollo%2013%2...   \n",
       "17                    NaN  http://us.imdb.com/M/title-exact?Belle%20de%20...   \n",
       "18                    NaN  http://us.imdb.com/M/title-exact?Crimson%20Tid...   \n",
       "19                    NaN    http://us.imdb.com/M/title-exact?Crumb%20(1994)   \n",
       "20                    NaN   http://us.imdb.com/M/title-exact?Clerks%20(1994)   \n",
       "21                    NaN  http://us.imdb.com/M/title-exact?Dolores%20Cla...   \n",
       "22                    NaN  http://us.imdb.com/M/title-exact?Yinshi%20Nan%...   \n",
       "23                    NaN  http://us.imdb.com/M/title-exact?Ed%20Wood%20(...   \n",
       "24                    NaN  http://us.imdb.com/M/title-exact?Hoop%20Dreams...   \n",
       "25                    NaN     http://us.imdb.com/M/title-exact?I.Q.%20(1994)   \n",
       "26                    NaN  http://us.imdb.com/M/title-exact?Star%20Wars%2...   \n",
       "27                    NaN  http://us.imdb.com/M/title-exact?Outbreak%20(1...   \n",
       "28                    NaN             http://us.imdb.com/Title?L%E9on+(1994)   \n",
       "29                    NaN  http://us.imdb.com/M/title-exact?Pulp%20Fictio...   \n",
       "...                   ...                                                ...   \n",
       "99970                 NaN  http://us.imdb.com/M/title-exact?Kiss+the+Girl...   \n",
       "99971                 NaN             http://us.imdb.com/Title?U+Turn+(1997)   \n",
       "99972                 NaN       http://us.imdb.com/M/title-exact?Bean+(1997)   \n",
       "99973                 NaN  http://us.imdb.com/M/title-exact?imdb-title-11...   \n",
       "99974                 NaN  http://us.imdb.com/M/title-exact?I+Know+What+Y...   \n",
       "99975                 NaN  http://us.imdb.com/M/title-exact?Picture+Perfe...   \n",
       "99976                 NaN  http://us.imdb.com/M/title-exact?Excess+Baggag...   \n",
       "99977                 NaN  http://us.imdb.com/M/title-exact?Life+Less+Ord...   \n",
       "99978                 NaN  http://us.imdb.com/M/title-exact?imdb-title-12...   \n",
       "99979                 NaN  http://us.imdb.com/M/title-exact?imdb-title-11...   \n",
       "99980                 NaN   http://us.imdb.com/M/title-exact?Scream%20(1996)   \n",
       "99981                 NaN          http://us.imdb.com/Title?Liar+Liar+(1997)   \n",
       "99982                 NaN  http://us.imdb.com/M/title-exact?Air+Force+One...   \n",
       "99983                 NaN  http://us.imdb.com/M/title-exact?Rainmaker,+Th...   \n",
       "99984                 NaN  http://us.imdb.com/M/title-exact?imdb-title-12...   \n",
       "99985                 NaN  http://us.imdb.com/M/title-exact?Murder%20at%2...   \n",
       "99986                 NaN  http://us.imdb.com/M/title-exact?Conspiracy+Th...   \n",
       "99987                 NaN  http://us.imdb.com/M/title-exact?Game%2C+The+(...   \n",
       "99988                 NaN       http://us.imdb.com/M/title-exact?Bean+(1997)   \n",
       "99989                 NaN  http://us.imdb.com/M/title-exact?imdb-title-11...   \n",
       "99990                 NaN  http://us.imdb.com/M/title-exact?Wedding+Singe...   \n",
       "99991                 NaN  http://us.imdb.com/M/title-exact?Blues+Brother...   \n",
       "99992                 NaN  http://us.imdb.com/M/title-exact?Rocket+Man+(1...   \n",
       "99993                 NaN  http://us.imdb.com/M/title-exact?Jackal%2C+The...   \n",
       "99994                 NaN  http://us.imdb.com/M/title-exact?Seven+Years+i...   \n",
       "99995                 NaN  http://us.imdb.com/M/title-exact?Saint%2C%20Th...   \n",
       "99996                 NaN  http://us.imdb.com/M/title-exact?imdb-title-12...   \n",
       "99997                 NaN  http://us.imdb.com/M/title-exact?Peacemaker%2C...   \n",
       "99998                 NaN  http://us.imdb.com/M/title-exact?imdb-title-11...   \n",
       "99999                 NaN  http://us.imdb.com/M/title-exact?imdb-title-11...   \n",
       "\n",
       "       user_id  rating  unix_timestamp  age sex occupation zip_code  \n",
       "0          308       4       887736532   60   M    retired    95076  \n",
       "1          308       5       887737890   60   M    retired    95076  \n",
       "2          308       4       887739608   60   M    retired    95076  \n",
       "3          308       4       887738847   60   M    retired    95076  \n",
       "4          308       5       887736696   60   M    retired    95076  \n",
       "5          308       4       887737194   60   M    retired    95076  \n",
       "6          308       5       887737837   60   M    retired    95076  \n",
       "7          308       5       887737243   60   M    retired    95076  \n",
       "8          308       3       887739426   60   M    retired    95076  \n",
       "9          308       4       887739056   60   M    retired    95076  \n",
       "10         308       3       887737383   60   M    retired    95076  \n",
       "11         308       3       887740729   60   M    retired    95076  \n",
       "12         308       4       887737647   60   M    retired    95076  \n",
       "13         308       5       887737293   60   M    retired    95076  \n",
       "14         308       4       887738057   60   M    retired    95076  \n",
       "15         308       4       887740649   60   M    retired    95076  \n",
       "16         308       3       887737036   60   M    retired    95076  \n",
       "17         308       4       887738933   60   M    retired    95076  \n",
       "18         308       3       887739472   60   M    retired    95076  \n",
       "19         308       5       887737432   60   M    retired    95076  \n",
       "20         308       4       887738191   60   M    retired    95076  \n",
       "21         308       4       887740451   60   M    retired    95076  \n",
       "22         308       4       887736843   60   M    retired    95076  \n",
       "23         308       4       887738933   60   M    retired    95076  \n",
       "24         308       4       887736880   60   M    retired    95076  \n",
       "25         308       3       887740833   60   M    retired    95076  \n",
       "26         308       5       887737431   60   M    retired    95076  \n",
       "27         308       2       887740254   60   M    retired    95076  \n",
       "28         308       3       887738760   60   M    retired    95076  \n",
       "29         308       5       887736924   60   M    retired    95076  \n",
       "...        ...     ...             ...  ...  ..        ...      ...  \n",
       "99970      631       3       888465180   18   F    student    38866  \n",
       "99971      631       2       888464941   18   F    student    38866  \n",
       "99972      631       2       888465299   18   F    student    38866  \n",
       "99973      631       4       888465004   18   F    student    38866  \n",
       "99974      631       2       888465247   18   F    student    38866  \n",
       "99975      631       2       888465084   18   F    student    38866  \n",
       "99976      631       2       888465131   18   F    student    38866  \n",
       "99977      631       4       888465216   18   F    student    38866  \n",
       "99978      631       2       888465351   18   F    student    38866  \n",
       "99979      729       4       893286638   19   M    student    56567  \n",
       "99980      729       2       893286261   19   M    student    56567  \n",
       "99981      729       2       893286338   19   M    student    56567  \n",
       "99982      729       4       893286638   19   M    student    56567  \n",
       "99983      729       3       893286204   19   M    student    56567  \n",
       "99984      729       3       893286638   19   M    student    56567  \n",
       "99985      729       4       893286637   19   M    student    56567  \n",
       "99986      729       3       893286638   19   M    student    56567  \n",
       "99987      729       4       893286638   19   M    student    56567  \n",
       "99988      729       1       893286373   19   M    student    56567  \n",
       "99989      729       1       893286168   19   M    student    56567  \n",
       "99990      729       5       893286637   19   M    student    56567  \n",
       "99991      729       4       893286637   19   M    student    56567  \n",
       "99992      729       2       893286511   19   M    student    56567  \n",
       "99993      729       4       893286638   19   M    student    56567  \n",
       "99994      729       2       893286149   19   M    student    56567  \n",
       "99995      729       4       893286638   19   M    student    56567  \n",
       "99996      729       3       893286338   19   M    student    56567  \n",
       "99997      729       3       893286299   19   M    student    56567  \n",
       "99998      729       1       893286511   19   M    student    56567  \n",
       "99999      729       1       893286491   19   M    student    56567  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge datasets - Movies, ratings & users\n",
    "movies.head()    # movie_id\n",
    "ratings.head()   # movie_id & user_id\n",
    "#users.head()     # user_id\n",
    "\n",
    "movie_ratings = pd.merge(movies, ratings)\n",
    "lens = pd.merge(movie_ratings, users)\n",
    "lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   movie_id                  title release_date  video_release_date  \\\n",
      "0         1       Toy Story (1995)  01-Jan-1995                 NaN   \n",
      "1         4      Get Shorty (1995)  01-Jan-1995                 NaN   \n",
      "2         5         Copycat (1995)  01-Jan-1995                 NaN   \n",
      "3         7  Twelve Monkeys (1995)  01-Jan-1995                 NaN   \n",
      "4         8            Babe (1995)  01-Jan-1995                 NaN   \n",
      "\n",
      "                                            imdb_url  user_id  rating  \\\n",
      "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...      308       4   \n",
      "1  http://us.imdb.com/M/title-exact?Get%20Shorty%...      308       5   \n",
      "2  http://us.imdb.com/M/title-exact?Copycat%20(1995)      308       4   \n",
      "3  http://us.imdb.com/M/title-exact?Twelve%20Monk...      308       4   \n",
      "4     http://us.imdb.com/M/title-exact?Babe%20(1995)      308       5   \n",
      "\n",
      "   unix_timestamp  age sex occupation zip_code  \n",
      "0       887736532   60   M    retired    95076  \n",
      "1       887737890   60   M    retired    95076  \n",
      "2       887739608   60   M    retired    95076  \n",
      "3       887738847   60   M    retired    95076  \n",
      "4       887736696   60   M    retired    95076  \n",
      "(1682, 5)\n",
      "(100000, 4)\n",
      "(943, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(100000, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(lens.head())\n",
    "print(movies.shape)\n",
    "movies.head()\n",
    "print(ratings.shape)\n",
    "print(users.shape)\n",
    "lens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Star Wars (1977)    583\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us answer some simple questions now. \n",
    "# Most rated movies\n",
    "#print(lens.groupby('title').size())\n",
    "most_rated = lens.groupby('title').size().sort_values(ascending=False)\n",
    "most_rated.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Star Wars (1977)                    583\n",
       "Contact (1997)                      509\n",
       "Fargo (1996)                        508\n",
       "Return of the Jedi (1983)           507\n",
       "Liar Liar (1997)                    485\n",
       "English Patient, The (1996)         481\n",
       "Scream (1996)                       478\n",
       "Toy Story (1995)                    452\n",
       "Air Force One (1997)                431\n",
       "Independence Day (ID4) (1996)       429\n",
       "Raiders of the Lost Ark (1981)      420\n",
       "Godfather, The (1972)               413\n",
       "Pulp Fiction (1994)                 394\n",
       "Twelve Monkeys (1995)               392\n",
       "Silence of the Lambs, The (1991)    390\n",
       "Jerry Maguire (1996)                384\n",
       "Chasing Amy (1997)                  379\n",
       "Rock, The (1996)                    378\n",
       "Empire Strikes Back, The (1980)     367\n",
       "Star Trek: First Contact (1996)     365\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to get the mentions. value_counts: The resulting object will be in descending order so that the first element is the most frequently-occurring \n",
    "lens.title.value_counts()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>9</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>5</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>109</td>\n",
       "      <td>2.908257</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>125</td>\n",
       "      <td>4.344000</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>41</td>\n",
       "      <td>3.024390</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          rating                    \n",
       "                            size      mean amax amin\n",
       "title                                               \n",
       "'Til There Was You (1997)      9  2.333333    4    1\n",
       "1-900 (1994)                   5  2.600000    4    1\n",
       "101 Dalmatians (1996)        109  2.908257    5    1\n",
       "12 Angry Men (1957)          125  4.344000    5    2\n",
       "187 (1997)                    41  3.024390    5    1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Highest rated movies\n",
    "highest_ratings = lens.groupby('title').agg({'rating':[np.size,np.mean,np.max,np.min]})\n",
    "highest_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>They Made Me a Criminal (1939)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marlene Dietrich: Shadow and Light (1996)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint of Fort Washington, The (1993)</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Someone Else's America (1995)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Kid (1997)</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           rating               \n",
       "                                             size mean amax amin\n",
       "title                                                           \n",
       "They Made Me a Criminal (1939)                  1  5.0    5    5\n",
       "Marlene Dietrich: Shadow and Light (1996)       1  5.0    5    5\n",
       "Saint of Fort Washington, The (1993)            2  5.0    5    5\n",
       "Someone Else's America (1995)                   1  5.0    5    5\n",
       "Star Kid (1997)                                 3  5.0    5    5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us sort the output and see what we get\n",
    "highest_ratings.sort_values([('rating', 'mean')], ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>112</td>\n",
       "      <td>4.491071</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>298</td>\n",
       "      <td>4.466443</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>118</td>\n",
       "      <td>4.466102</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casablanca (1942)</th>\n",
       "      <td>243</td>\n",
       "      <td>4.456790</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>283</td>\n",
       "      <td>4.445230</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rear Window (1954)</th>\n",
       "      <td>209</td>\n",
       "      <td>4.387560</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>267</td>\n",
       "      <td>4.385768</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>583</td>\n",
       "      <td>4.358491</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>125</td>\n",
       "      <td>4.344000</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Citizen Kane (1941)</th>\n",
       "      <td>198</td>\n",
       "      <td>4.292929</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>219</td>\n",
       "      <td>4.292237</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One Flew Over the Cuckoo's Nest (1975)</th>\n",
       "      <td>264</td>\n",
       "      <td>4.291667</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silence of the Lambs, The (1991)</th>\n",
       "      <td>390</td>\n",
       "      <td>4.289744</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North by Northwest (1959)</th>\n",
       "      <td>179</td>\n",
       "      <td>4.284916</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>413</td>\n",
       "      <td>4.283293</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       rating                    \n",
       "                                         size      mean amax amin\n",
       "title                                                            \n",
       "Close Shave, A (1995)                     112  4.491071    5    1\n",
       "Schindler's List (1993)                   298  4.466443    5    1\n",
       "Wrong Trousers, The (1993)                118  4.466102    5    1\n",
       "Casablanca (1942)                         243  4.456790    5    1\n",
       "Shawshank Redemption, The (1994)          283  4.445230    5    1\n",
       "Rear Window (1954)                        209  4.387560    5    2\n",
       "Usual Suspects, The (1995)                267  4.385768    5    1\n",
       "Star Wars (1977)                          583  4.358491    5    1\n",
       "12 Angry Men (1957)                       125  4.344000    5    2\n",
       "Citizen Kane (1941)                       198  4.292929    5    2\n",
       "To Kill a Mockingbird (1962)              219  4.292237    5    1\n",
       "One Flew Over the Cuckoo's Nest (1975)    264  4.291667    5    1\n",
       "Silence of the Lambs, The (1991)          390  4.289744    5    1\n",
       "North by Northwest (1959)                 179  4.284916    5    2\n",
       "Godfather, The (1972)                     413  4.283293    5    1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We shall consider movies that have been rated more than 100 times\n",
    "atleast_100 = highest_ratings['rating']['size'] >= 100\n",
    "highest_ratings[atleast_100].sort_values([('rating', 'mean')], ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1f6bc4f97b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6bb4feeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "plt.plot([10,20],[10,20],'ro')\n",
    "plt.title(\"Distribution of users' ages\") \n",
    "plt.ylabel('count of users')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  unix_timestamp\n",
       "0      196       242       3       881250949"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IJTctRtfJTYvdpBfbBM3etNhMMrkb+Kztu8r7E4G/tf2ulsQ5SpJMohslmXSTXmwTtOwO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\nbvh9bKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6bc5830b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings.rating.plot.hist(bins=5) \n",
    "plt.title(\"Ratings\") \n",
    "plt.ylabel('movies count') \n",
    "plt.xlabel('rating') \n",
    "plt.axis([0,5,0,35000])\n",
    "ratings.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##### Lets plot the user age and count\n",
    "users.age.plot.hist(bins=30)\n",
    "plt.title(\"Distribution of users' ages\")\n",
    "plt.ylabel('count of users')\n",
    "plt.xlabel('age');\n",
    "\n",
    "ratings.rating.plot.hist(bins=5)\n",
    "plt.title(\"Ratings\")\n",
    "plt.ylabel('movies count')\n",
    "plt.xlabel('rating')\n",
    "ratings.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to get the 397 th user's age\n",
    "lens['age'][397]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736532</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "      <td>60-69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id             title release_date  video_release_date  \\\n",
       "0         1  Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  user_id  rating  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...      308       4   \n",
       "\n",
       "   unix_timestamp  age sex occupation zip_code age_group  \n",
       "0       887736532   60   M    retired    95076     60-69  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us create buckets age-wise\n",
    "labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79']\n",
    "lens['age_group'] = pd.cut(lens.age, range(0, 81, 10), right=False, labels=labels)\n",
    "lens.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736532</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>retired</td>\n",
       "      <td>95076</td>\n",
       "      <td>60-69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id             title release_date  video_release_date  \\\n",
       "0         1  Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  user_id  rating  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...      308       4   \n",
       "\n",
       "   unix_timestamp  age sex occupation zip_code age_group  \n",
       "0       887736532   60   M    retired    95076     60-69  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79']\n",
    "lens['age_group'] = pd.cut(lens.age, range(0, 81, 10), right=False, labels=labels)\n",
    "lens.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>7479</td>\n",
       "      <td>3.635646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>2308</td>\n",
       "      <td>3.653380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>540</td>\n",
       "      <td>3.688889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>9442</td>\n",
       "      <td>3.670621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>8175</td>\n",
       "      <td>3.541407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>2095</td>\n",
       "      <td>3.441050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>3403</td>\n",
       "      <td>3.349104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>2804</td>\n",
       "      <td>2.896220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>299</td>\n",
       "      <td>3.301003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>1345</td>\n",
       "      <td>3.735316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>5273</td>\n",
       "      <td>3.560781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>1950</td>\n",
       "      <td>3.485641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>901</td>\n",
       "      <td>3.779134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>10663</td>\n",
       "      <td>3.552377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>7801</td>\n",
       "      <td>3.568260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>1609</td>\n",
       "      <td>3.466750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>856</td>\n",
       "      <td>3.582944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>2058</td>\n",
       "      <td>3.611273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>21957</td>\n",
       "      <td>3.515143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>3506</td>\n",
       "      <td>3.532230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>5536</td>\n",
       "      <td>3.375723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rating          \n",
       "                size      mean\n",
       "occupation                    \n",
       "administrator   7479  3.635646\n",
       "artist          2308  3.653380\n",
       "doctor           540  3.688889\n",
       "educator        9442  3.670621\n",
       "engineer        8175  3.541407\n",
       "entertainment   2095  3.441050\n",
       "executive       3403  3.349104\n",
       "healthcare      2804  2.896220\n",
       "homemaker        299  3.301003\n",
       "lawyer          1345  3.735316\n",
       "librarian       5273  3.560781\n",
       "marketing       1950  3.485641\n",
       "none             901  3.779134\n",
       "other          10663  3.552377\n",
       "programmer      7801  3.568260\n",
       "retired         1609  3.466750\n",
       "salesman         856  3.582944\n",
       "scientist       2058  3.611273\n",
       "student        21957  3.515143\n",
       "technician      3506  3.532230\n",
       "writer          5536  3.375723"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group these age buckets and check the rating behaviour. Check who has given max & min number of ratings\n",
    "lens.groupby('occupation').agg({'rating': [np.size, np.mean]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get the top 100 most mentions\n",
    "most_100 = lens.groupby('movie_id').size().sort_values(ascending=False)[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Lets set the movie_id as the index\n",
    "lens.set_index('movie_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                         age_group\n",
       "2001: A Space Odyssey (1968)               5.000000\n",
       "                              10-19        4.100000\n",
       "                              20-29        3.924731\n",
       "                              30-39        3.887500\n",
       "                              40-49        4.093023\n",
       "                              50-59        4.000000\n",
       "                              60-69        4.285714\n",
       "Air Force One (1997)          10-19        3.647059\n",
       "                              20-29        3.666667\n",
       "                              30-39        3.570000\n",
       "                              40-49        3.555556\n",
       "                              50-59        3.750000\n",
       "                              60-69        3.666667\n",
       "                              70-79        3.666667\n",
       "Aladdin (1992)                10-19        3.840000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets split the observations by title and age group\n",
    "by_age = lens.loc[most_100.index].groupby(['title', 'age_group'])\n",
    "by_age.rating.mean().head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th>10-19</th>\n",
       "      <th>20-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60-69</th>\n",
       "      <th>70-79</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Birdcage, The (1996)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.217391</td>\n",
       "      <td>3.413223</td>\n",
       "      <td>3.514286</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.346154</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade Runner (1982)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>4.134454</td>\n",
       "      <td>4.211765</td>\n",
       "      <td>4.147059</td>\n",
       "      <td>4.136364</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blues Brothers, The (1980)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.214286</td>\n",
       "      <td>4.050505</td>\n",
       "      <td>3.637681</td>\n",
       "      <td>3.795455</td>\n",
       "      <td>3.450000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braveheart (1995)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.384615</td>\n",
       "      <td>4.194915</td>\n",
       "      <td>4.156627</td>\n",
       "      <td>4.051282</td>\n",
       "      <td>3.956522</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broken Arrow (1996)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.363636</td>\n",
       "      <td>3.036697</td>\n",
       "      <td>2.938462</td>\n",
       "      <td>3.058824</td>\n",
       "      <td>3.052632</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casablanca (1942)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.636364</td>\n",
       "      <td>4.447368</td>\n",
       "      <td>4.442857</td>\n",
       "      <td>4.530612</td>\n",
       "      <td>4.344828</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chasing Amy (1997)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.130435</td>\n",
       "      <td>3.808333</td>\n",
       "      <td>3.679245</td>\n",
       "      <td>3.878788</td>\n",
       "      <td>3.789474</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clockwork Orange, A (1971)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.042105</td>\n",
       "      <td>3.688525</td>\n",
       "      <td>3.612903</td>\n",
       "      <td>4.176471</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conspiracy Theory (1997)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.565217</td>\n",
       "      <td>3.382114</td>\n",
       "      <td>3.419355</td>\n",
       "      <td>3.465116</td>\n",
       "      <td>3.538462</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact (1997)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.693878</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>3.847458</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.739130</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age_group                           10-19     20-29     30-39     40-49  \\\n",
       "title                                                                     \n",
       "Birdcage, The (1996)        0.0  3.217391  3.413223  3.514286  3.500000   \n",
       "Blade Runner (1982)         0.0  3.400000  4.134454  4.211765  4.147059   \n",
       "Blues Brothers, The (1980)  0.0  4.214286  4.050505  3.637681  3.795455   \n",
       "Braveheart (1995)           0.0  4.384615  4.194915  4.156627  4.051282   \n",
       "Broken Arrow (1996)         0.0  3.363636  3.036697  2.938462  3.058824   \n",
       "Casablanca (1942)           0.0  4.636364  4.447368  4.442857  4.530612   \n",
       "Chasing Amy (1997)          0.0  4.130435  3.808333  3.679245  3.878788   \n",
       "Clockwork Orange, A (1971)  0.0  4.600000  4.042105  3.688525  3.612903   \n",
       "Conspiracy Theory (1997)    0.0  3.565217  3.382114  3.419355  3.465116   \n",
       "Contact (1997)              5.0  3.693878  3.785714  3.847458  3.866667   \n",
       "\n",
       "age_group                      50-59     60-69  70-79  \n",
       "title                                                  \n",
       "Birdcage, The (1996)        3.346154  3.888889    0.0  \n",
       "Blade Runner (1982)         4.136364  4.400000    0.0  \n",
       "Blues Brothers, The (1980)  3.450000  3.200000    0.0  \n",
       "Braveheart (1995)           3.956522  3.714286    4.0  \n",
       "Broken Arrow (1996)         3.052632  2.400000    0.0  \n",
       "Casablanca (1942)           4.344828  4.285714    5.0  \n",
       "Chasing Amy (1997)          3.789474  2.857143    0.0  \n",
       "Clockwork Orange, A (1971)  4.176471  3.714286    0.0  \n",
       "Conspiracy Theory (1997)    3.538462  2.600000    0.0  \n",
       "Contact (1997)              3.739130  3.777778    0.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make it more presentable using unstack\n",
    "by_age.rating.mean().unstack(1).fillna(0)[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Reset movie_id as index\n",
    "lens.reset_index('movie_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <td>3.789916</td>\n",
       "      <td>3.909910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <td>3.368421</td>\n",
       "      <td>3.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Four Rooms (1995)</th>\n",
       "      <td>2.687500</td>\n",
       "      <td>3.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>Get Shorty (1995)</th>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.591463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>Copycat (1995)</th>\n",
       "      <td>3.772727</td>\n",
       "      <td>3.140625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                                F         M\n",
       "movie_id title                                \n",
       "1        Toy Story (1995)   3.789916  3.909910\n",
       "2        GoldenEye (1995)   3.368421  3.178571\n",
       "3        Four Rooms (1995)  2.687500  3.108108\n",
       "4        Get Shorty (1995)  3.400000  3.591463\n",
       "5        Copycat (1995)     3.772727  3.140625"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us pivot the data and split observations betweem male and female ratings\n",
    "pivoted = lens.pivot_table(index=['movie_id', 'title'],\n",
    "                           columns=['sex'],\n",
    "                           values='rating',\n",
    "                           fill_value=0)\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <td>3.789916</td>\n",
       "      <td>3.909910</td>\n",
       "      <td>0.119994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <td>3.368421</td>\n",
       "      <td>3.178571</td>\n",
       "      <td>-0.189850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Four Rooms (1995)</th>\n",
       "      <td>2.687500</td>\n",
       "      <td>3.108108</td>\n",
       "      <td>0.420608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>Get Shorty (1995)</th>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.591463</td>\n",
       "      <td>0.191463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>Copycat (1995)</th>\n",
       "      <td>3.772727</td>\n",
       "      <td>3.140625</td>\n",
       "      <td>-0.632102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                                F         M      diff\n",
       "movie_id title                                          \n",
       "1        Toy Story (1995)   3.789916  3.909910  0.119994\n",
       "2        GoldenEye (1995)   3.368421  3.178571 -0.189850\n",
       "3        Four Rooms (1995)  2.687500  3.108108  0.420608\n",
       "4        Get Shorty (1995)  3.400000  3.591463  0.191463\n",
       "5        Copycat (1995)     3.772727  3.140625 -0.632102"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try to find the rating behaviour difference between male and female\n",
    "pivoted['diff'] = pivoted.M - pivoted.F\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>title</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.789916</td>\n",
       "      <td>3.909910</td>\n",
       "      <td>0.119994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.368421</td>\n",
       "      <td>3.178571</td>\n",
       "      <td>-0.189850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.687500</td>\n",
       "      <td>3.108108</td>\n",
       "      <td>0.420608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>4</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.591463</td>\n",
       "      <td>0.191463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>5</td>\n",
       "      <td>3.772727</td>\n",
       "      <td>3.140625</td>\n",
       "      <td>-0.632102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shanghai Triad (Yao a yao yao dao waipo qiao) ...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>-0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Twelve Monkeys (1995)</td>\n",
       "      <td>7</td>\n",
       "      <td>3.560976</td>\n",
       "      <td>3.861290</td>\n",
       "      <td>0.300315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Babe (1995)</td>\n",
       "      <td>8</td>\n",
       "      <td>4.050000</td>\n",
       "      <td>3.974843</td>\n",
       "      <td>-0.075157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "      <td>9</td>\n",
       "      <td>3.927711</td>\n",
       "      <td>3.884259</td>\n",
       "      <td>-0.043452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard III (1995)</td>\n",
       "      <td>10</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.869565</td>\n",
       "      <td>0.169565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Seven (Se7en) (1995)</td>\n",
       "      <td>11</td>\n",
       "      <td>3.773585</td>\n",
       "      <td>3.868852</td>\n",
       "      <td>0.095268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>12</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>4.399061</td>\n",
       "      <td>0.065728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mighty Aphrodite (1995)</td>\n",
       "      <td>13</td>\n",
       "      <td>3.269231</td>\n",
       "      <td>3.477273</td>\n",
       "      <td>0.208042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Postino, Il (1994)</td>\n",
       "      <td>14</td>\n",
       "      <td>3.890909</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mr. Holland's Opus (1995)</td>\n",
       "      <td>15</td>\n",
       "      <td>3.945652</td>\n",
       "      <td>3.701493</td>\n",
       "      <td>-0.244160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>French Twist (Gazon maudit) (1995)</td>\n",
       "      <td>16</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.193548</td>\n",
       "      <td>-0.056452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>17</td>\n",
       "      <td>2.846154</td>\n",
       "      <td>3.164557</td>\n",
       "      <td>0.318403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>White Balloon, The (1995)</td>\n",
       "      <td>18</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Antonia's Line (1995)</td>\n",
       "      <td>19</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>3.731707</td>\n",
       "      <td>-0.554007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Angels and Insects (1995)</td>\n",
       "      <td>20</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Muppet Treasure Island (1996)</td>\n",
       "      <td>21</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>2.612903</td>\n",
       "      <td>-0.568915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>22</td>\n",
       "      <td>4.126984</td>\n",
       "      <td>4.158120</td>\n",
       "      <td>0.031136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Taxi Driver (1976)</td>\n",
       "      <td>23</td>\n",
       "      <td>4.172414</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>-0.061303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rumble in the Bronx (1995)</td>\n",
       "      <td>24</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.486301</td>\n",
       "      <td>0.236301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Birdcage, The (1996)</td>\n",
       "      <td>25</td>\n",
       "      <td>3.670455</td>\n",
       "      <td>3.346341</td>\n",
       "      <td>-0.324113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brothers McMullen, The (1995)</td>\n",
       "      <td>26</td>\n",
       "      <td>3.230769</td>\n",
       "      <td>3.574468</td>\n",
       "      <td>0.343699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Bad Boys (1995)</td>\n",
       "      <td>27</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>0.267857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>28</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.909953</td>\n",
       "      <td>-0.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Batman Forever (1995)</td>\n",
       "      <td>29</td>\n",
       "      <td>3.173913</td>\n",
       "      <td>2.538462</td>\n",
       "      <td>-0.635452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Belle de jour (1967)</td>\n",
       "      <td>30</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>Entertaining Angels: The Dorothy Day Story (1996)</td>\n",
       "      <td>1653</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>Chairman of the Board (1998)</td>\n",
       "      <td>1654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>Favor, The (1994)</td>\n",
       "      <td>1655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>Little City (1998)</td>\n",
       "      <td>1656</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>Target (1995)</td>\n",
       "      <td>1657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Substance of Fire, The (1996)</td>\n",
       "      <td>1658</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>Getting Away With Murder (1996)</td>\n",
       "      <td>1659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>Small Faces (1995)</td>\n",
       "      <td>1660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>New Age, The (1994)</td>\n",
       "      <td>1661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>Rough Magic (1995)</td>\n",
       "      <td>1662</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>Nothing Personal (1995)</td>\n",
       "      <td>1663</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>8 Heads in a Duffel Bag (1997)</td>\n",
       "      <td>1664</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>Brother's Kiss, A (1997)</td>\n",
       "      <td>1665</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>Ripe (1996)</td>\n",
       "      <td>1666</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>Next Step, The (1995)</td>\n",
       "      <td>1667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>Wedding Bell Blues (1996)</td>\n",
       "      <td>1668</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>MURDER and murder (1996)</td>\n",
       "      <td>1669</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>Tainted (1998)</td>\n",
       "      <td>1670</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>Further Gesture, A (1996)</td>\n",
       "      <td>1671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Kika (1993)</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>Mirage (1995)</td>\n",
       "      <td>1673</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>Mamma Roma (1962)</td>\n",
       "      <td>1674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Sunchaser, The (1996)</td>\n",
       "      <td>1675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>War at Home, The (1996)</td>\n",
       "      <td>1676</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Sweet Nothing (1995)</td>\n",
       "      <td>1677</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Mat' i syn (1997)</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>B. Monkey (1998)</td>\n",
       "      <td>1679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Sliding Doors (1998)</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>You So Crazy (1994)</td>\n",
       "      <td>1681</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Scream of Stone (Schrei aus Stein) (1991)</td>\n",
       "      <td>1682</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1682 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                                               title  movie_id         F  \\\n",
       "0                                      Toy Story (1995)         1  3.789916   \n",
       "1                                      GoldenEye (1995)         2  3.368421   \n",
       "2                                     Four Rooms (1995)         3  2.687500   \n",
       "3                                     Get Shorty (1995)         4  3.400000   \n",
       "4                                        Copycat (1995)         5  3.772727   \n",
       "5     Shanghai Triad (Yao a yao yao dao waipo qiao) ...         6  3.600000   \n",
       "6                                 Twelve Monkeys (1995)         7  3.560976   \n",
       "7                                           Babe (1995)         8  4.050000   \n",
       "8                               Dead Man Walking (1995)         9  3.927711   \n",
       "9                                    Richard III (1995)        10  3.700000   \n",
       "10                                 Seven (Se7en) (1995)        11  3.773585   \n",
       "11                           Usual Suspects, The (1995)        12  4.333333   \n",
       "12                              Mighty Aphrodite (1995)        13  3.269231   \n",
       "13                                   Postino, Il (1994)        14  3.890909   \n",
       "14                            Mr. Holland's Opus (1995)        15  3.945652   \n",
       "15                   French Twist (Gazon maudit) (1995)        16  3.250000   \n",
       "16                           From Dusk Till Dawn (1996)        17  2.846154   \n",
       "17                            White Balloon, The (1995)        18  2.000000   \n",
       "18                                Antonia's Line (1995)        19  4.285714   \n",
       "19                            Angels and Insects (1995)        20  3.583333   \n",
       "20                        Muppet Treasure Island (1996)        21  3.181818   \n",
       "21                                    Braveheart (1995)        22  4.126984   \n",
       "22                                   Taxi Driver (1976)        23  4.172414   \n",
       "23                           Rumble in the Bronx (1995)        24  3.250000   \n",
       "24                                 Birdcage, The (1996)        25  3.670455   \n",
       "25                        Brothers McMullen, The (1995)        26  3.230769   \n",
       "26                                      Bad Boys (1995)        27  2.875000   \n",
       "27                                     Apollo 13 (1995)        28  4.000000   \n",
       "28                                Batman Forever (1995)        29  3.173913   \n",
       "29                                 Belle de jour (1967)        30  3.714286   \n",
       "...                                                 ...       ...       ...   \n",
       "1652  Entertaining Angels: The Dorothy Day Story (1996)      1653  0.000000   \n",
       "1653                       Chairman of the Board (1998)      1654  0.000000   \n",
       "1654                                  Favor, The (1994)      1655  0.000000   \n",
       "1655                                 Little City (1998)      1656  2.000000   \n",
       "1656                                      Target (1995)      1657  0.000000   \n",
       "1657                      Substance of Fire, The (1996)      1658  2.500000   \n",
       "1658                    Getting Away With Murder (1996)      1659  0.000000   \n",
       "1659                                 Small Faces (1995)      1660  0.000000   \n",
       "1660                                New Age, The (1994)      1661  1.000000   \n",
       "1661                                 Rough Magic (1995)      1662  4.000000   \n",
       "1662                            Nothing Personal (1995)      1663  2.000000   \n",
       "1663                     8 Heads in a Duffel Bag (1997)      1664  2.500000   \n",
       "1664                           Brother's Kiss, A (1997)      1665  2.000000   \n",
       "1665                                        Ripe (1996)      1666  2.000000   \n",
       "1666                              Next Step, The (1995)      1667  3.000000   \n",
       "1667                          Wedding Bell Blues (1996)      1668  3.000000   \n",
       "1668                           MURDER and murder (1996)      1669  2.000000   \n",
       "1669                                     Tainted (1998)      1670  3.000000   \n",
       "1670                          Further Gesture, A (1996)      1671  1.000000   \n",
       "1671                                        Kika (1993)      1672  0.000000   \n",
       "1672                                      Mirage (1995)      1673  3.000000   \n",
       "1673                                  Mamma Roma (1962)      1674  0.000000   \n",
       "1674                              Sunchaser, The (1996)      1675  0.000000   \n",
       "1675                            War at Home, The (1996)      1676  0.000000   \n",
       "1676                               Sweet Nothing (1995)      1677  3.000000   \n",
       "1677                                  Mat' i syn (1997)      1678  0.000000   \n",
       "1678                                   B. Monkey (1998)      1679  0.000000   \n",
       "1679                               Sliding Doors (1998)      1680  0.000000   \n",
       "1680                                You So Crazy (1994)      1681  0.000000   \n",
       "1681          Scream of Stone (Schrei aus Stein) (1991)      1682  0.000000   \n",
       "\n",
       "sex          M      diff  \n",
       "0     3.909910  0.119994  \n",
       "1     3.178571 -0.189850  \n",
       "2     3.108108  0.420608  \n",
       "3     3.591463  0.191463  \n",
       "4     3.140625 -0.632102  \n",
       "5     3.571429 -0.028571  \n",
       "6     3.861290  0.300315  \n",
       "7     3.974843 -0.075157  \n",
       "8     3.884259 -0.043452  \n",
       "9     3.869565  0.169565  \n",
       "10    3.868852  0.095268  \n",
       "11    4.399061  0.065728  \n",
       "12    3.477273  0.208042  \n",
       "13    4.000000  0.109091  \n",
       "14    3.701493 -0.244160  \n",
       "15    3.193548 -0.056452  \n",
       "16    3.164557  0.318403  \n",
       "17    3.000000  1.000000  \n",
       "18    3.731707 -0.554007  \n",
       "19    3.333333 -0.250000  \n",
       "20    2.612903 -0.568915  \n",
       "21    4.158120  0.031136  \n",
       "22    4.111111 -0.061303  \n",
       "23    3.486301  0.236301  \n",
       "24    3.346341 -0.324113  \n",
       "25    3.574468  0.343699  \n",
       "26    3.142857  0.267857  \n",
       "27    3.909953 -0.090047  \n",
       "28    2.538462 -0.635452  \n",
       "29    4.000000  0.285714  \n",
       "...        ...       ...  \n",
       "1652  5.000000  5.000000  \n",
       "1653  1.000000  1.000000  \n",
       "1654  2.000000  2.000000  \n",
       "1655  5.000000  3.000000  \n",
       "1656  3.000000  3.000000  \n",
       "1657  4.000000  1.500000  \n",
       "1658  1.000000  1.000000  \n",
       "1659  2.000000  2.000000  \n",
       "1660  0.000000 -1.000000  \n",
       "1661  1.000000 -3.000000  \n",
       "1662  0.000000 -2.000000  \n",
       "1663  4.000000  1.500000  \n",
       "1664  0.000000 -2.000000  \n",
       "1665  0.000000 -2.000000  \n",
       "1666  0.000000 -3.000000  \n",
       "1667  0.000000 -3.000000  \n",
       "1668  0.000000 -2.000000  \n",
       "1669  0.000000 -3.000000  \n",
       "1670  0.000000 -1.000000  \n",
       "1671  2.000000  2.000000  \n",
       "1672  0.000000 -3.000000  \n",
       "1673  4.000000  4.000000  \n",
       "1674  3.000000  3.000000  \n",
       "1675  2.000000  2.000000  \n",
       "1676  0.000000 -3.000000  \n",
       "1677  1.000000  1.000000  \n",
       "1678  3.000000  3.000000  \n",
       "1679  2.000000  2.000000  \n",
       "1680  3.000000  3.000000  \n",
       "1681  3.000000  3.000000  \n",
       "\n",
       "[1682 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset movie_id as index again\n",
    "pivoted.reset_index('movie_id', inplace=True)\n",
    "pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZxsGywJqla7WMlgE0GFe9SuW9gT1iYo6khSTNE+cPhytVQ+P+Pmczs0XNrLOZ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m6c/AMGA2Hu5UeC6cSH2Z07IXw/ZAnyrjSZKkEdLPIEmSRYX0QUiSCiRdCoyIyfn87GdN4Hoz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4SZgJ/Q5oNwOZFf0UfSrIRx3MOl7rzE5qE6bUiSJEmSJGlHbhCmfWZuyEH4fSlvpcBzBLCx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\nS3pU0sYze6xdQavzU+rOUYTpmmmB9ChamRuFYvpd5Xdml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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6bc59cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us plot the rating differences between male and female and check for ourselves for patterns\n",
    "disagreements = pivoted[pivoted.movie_id.isin(most_100.index)]['diff']\n",
    "disagreements.sort_values().plot(kind='barh', figsize=[9, 15])\n",
    "plt.title('Male vs. Female Avg. Ratings\\n(Difference > 0 = Favored by Men)')\n",
    "plt.ylabel('Title')\n",
    "plt.xlabel('Average Rating Difference');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}