Play with MovieLens data sets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movies: User Rating analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**u.data structure**: user id | item id | rating | timestamp\n",
    "\n",
    "**u.user structure**: user id | age | gender | occupation | zip code\n",
    "\n",
    "**u.item structure**: movie id | movie title | release date | video release date |IMDb URL | unknown | Action | Adventure | Animation | Children's | Comedy | Crime | Documentary | Drama | Fantasy |Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |Thriller | War | Western |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Standard deviation of ratings by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- coding: utf-8 -*-\n",
    "# three time modules won't be used that much, nice to have.\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1.170951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1.109556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rating\n",
       "gender          \n",
       "F       1.170951\n",
       "M       1.109556"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame from table: u.data\n",
    "data_names = ['userid','itemid','rating','timestamp']\n",
    "udata = pd.read_table(r'C:\\Users\\Zishi\\Downloads\\ml-100k\\u.data',sep='\\t',names=data_names)\n",
    "# create a DataFrame from table: u.user\n",
    "user_names = ['userid','age','gender','occupation','zipcode']\n",
    "uuser = pd.read_table(r'C:\\Users\\Zishi\\Downloads\\ml-100k\\u.user',sep='|',names=user_names)\n",
    "# merge both dataframes on key \"userid\"\n",
    "userdata = pd.merge(uuser,udata,how='inner',on='userid')\n",
    "# create a new dataframe containing only \"gender\" and \"rating\"\n",
    "genderdata = userdata[['gender','rating']]\n",
    "# use pivot table to culculate standard deviation\n",
    "pivot = pd.pivot_table(genderdata,index=['gender'],values=['rating'],aggfunc='std')\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import movie categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_names = ['itemid','movietitle','release date','videoreleasedate','IMDbURL']\n",
    "# for latin1, use: encoding='ISO-8859-1'\n",
    "uitem = pd.read_table(r'C:\\Users\\Zishi\\Downloads\\ml-100k\\u.item',sep='|',names=item_names,\n",
    "                      usecols = range(5),encoding='ISO-8859-1')\n",
    "movie_ratings = pd.merge(uitem,udata)\n",
    "lens = pd.merge(movie_ratings,uuser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movietitle\n",
       "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",
       "Back to the Future (1985)                    350\n",
       "Titanic (1997)                               350\n",
       "Mission: Impossible (1996)                   344\n",
       "Fugitive, The (1993)                         336\n",
       "Indiana Jones and the Last Crusade (1989)    331\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated = lens.groupby('movietitle').size().sort_values(ascending=False)[:25]\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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",
       "Titanic (1997)                               350\n",
       "Back to the Future (1985)                    350\n",
       "Mission: Impossible (1996)                   344\n",
       "Fugitive, The (1993)                         336\n",
       "Indiana Jones and the Last Crusade (1989)    331\n",
       "Name: movietitle, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lens.movietitle.value_counts()[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>movietitle</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>5</td>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>109</td>\n",
       "      <td>2.908257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>125</td>\n",
       "      <td>4.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>41</td>\n",
       "      <td>3.024390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          rating          \n",
       "                            size      mean\n",
       "movietitle                                \n",
       "'Til There Was You (1997)      9  2.333333\n",
       "1-900 (1994)                   5  2.600000\n",
       "101 Dalmatians (1996)        109  2.908257\n",
       "12 Angry Men (1957)          125  4.344000\n",
       "187 (1997)                    41  3.024390"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_stats = lens.groupby('movietitle').agg({'rating': [np.size, np.mean]})\n",
    "movie_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>movietitle</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marlene Dietrich: Shadow and Light (1996)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint of Fort Washington, The (1993)</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Someone Else's America (1995)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Kid (1997)</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           rating     \n",
       "                                             size mean\n",
       "movietitle                                            \n",
       "They Made Me a Criminal (1939)                  1  5.0\n",
       "Marlene Dietrich: Shadow and Light (1996)       1  5.0\n",
       "Saint of Fort Washington, The (1993)            2  5.0\n",
       "Someone Else's America (1995)                   1  5.0\n",
       "Star Kid (1997)                                 3  5.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_stats.sort_values([('rating','mean')],ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>movietitle</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>298</td>\n",
       "      <td>4.466443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>118</td>\n",
       "      <td>4.466102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casablanca (1942)</th>\n",
       "      <td>243</td>\n",
       "      <td>4.456790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>283</td>\n",
       "      <td>4.445230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rear Window (1954)</th>\n",
       "      <td>209</td>\n",
       "      <td>4.387560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>267</td>\n",
       "      <td>4.385768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>583</td>\n",
       "      <td>4.358491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>125</td>\n",
       "      <td>4.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Citizen Kane (1941)</th>\n",
       "      <td>198</td>\n",
       "      <td>4.292929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>219</td>\n",
       "      <td>4.292237</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silence of the Lambs, The (1991)</th>\n",
       "      <td>390</td>\n",
       "      <td>4.289744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North by Northwest (1959)</th>\n",
       "      <td>179</td>\n",
       "      <td>4.284916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>413</td>\n",
       "      <td>4.283293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       rating          \n",
       "                                         size      mean\n",
       "movietitle                                             \n",
       "Close Shave, A (1995)                     112  4.491071\n",
       "Schindler's List (1993)                   298  4.466443\n",
       "Wrong Trousers, The (1993)                118  4.466102\n",
       "Casablanca (1942)                         243  4.456790\n",
       "Shawshank Redemption, The (1994)          283  4.445230\n",
       "Rear Window (1954)                        209  4.387560\n",
       "Usual Suspects, The (1995)                267  4.385768\n",
       "Star Wars (1977)                          583  4.358491\n",
       "12 Angry Men (1957)                       125  4.344000\n",
       "Citizen Kane (1941)                       198  4.292929\n",
       "To Kill a Mockingbird (1962)              219  4.292237\n",
       "One Flew Over the Cuckoo's Nest (1975)    264  4.291667\n",
       "Silence of the Lambs, The (1991)          390  4.289744\n",
       "North by Northwest (1959)                 179  4.284916\n",
       "Godfather, The (1972)                     413  4.283293"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atleast_100 = movie_stats['rating']['size'] >= 100\n",
    "movie_stats[atleast_100].sort_values([('rating','mean')],ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f841bfdda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uuser.age.plot.hist(bins=30)\n",
    "plt.title(\"Distribution of users' ages\")\n",
    "plt.ylabel('count of users')\n",
    "plt.xlabel('age');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}