18-01 Recommender Systems with Python.ipynb

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    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Recommender Systems with Python\n",
    "\n",
    "Welcome to the code notebook for Recommender Systems with Python. In this lecture we will develop basic recommendation systems using Python and pandas. There is another notebook: *Advanced Recommender Systems with Python*. That notebook goes into more detail with the same data set.\n",
    "\n",
    "In this notebook, we will focus on providing a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. Keep in mind, this is not a true robust recommendation system, to describe it more accurately,it just tells you what movies/items are most similar to your movie choice.\n",
    "\n",
    "There is no project for this topic, instead you have the option to work through the advanced lecture version of this notebook (totally optional!).\n",
    "\n",
    "Let's get started!\n",
    "\n",
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "df = pd.read_csv('data/u.data', sep='\\t', names=column_names)"
   ]
  },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
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       "   user_id  item_id  rating  timestamp\n",
       "0        0       50       5  881250949\n",
       "1        0      172       5  881250949\n",
       "2        0      133       1  881250949\n",
       "3      196      242       3  881250949\n",
       "4      186      302       3  891717742"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Now let's get the movie titles:"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_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",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
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      "text/plain": [
       "   item_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": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "movie_titles = pd.read_csv(\"data/Movie_Id_Titles\")\n",
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can merge them together:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
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       "      <td>Star Wars (1977)</td>\n",
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       "      <td>Star Wars (1977)</td>\n",
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       "   user_id  item_id  rating  timestamp             title\n",
       "0        0       50       5  881250949  Star Wars (1977)\n",
       "1      290       50       5  880473582  Star Wars (1977)\n",
       "2       79       50       4  891271545  Star Wars (1977)\n",
       "3        2       50       5  888552084  Star Wars (1977)\n",
       "4        8       50       5  879362124  Star Wars (1977)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df = pd.merge(df,movie_titles,on='item_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "Let's explore the data a bit and get a look at some of the best rated movies.\n",
    "\n",
    "## Visualization Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a ratings dataframe with average rating and number of ratings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
       "Prefontaine (1997)                            5.0\n",
       "Santa with Muscles (1996)                     5.0\n",
       "Star Kid (1997)                               5.0\n",
       "Someone Else's America (1995)                 5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Star Wars (1977)             584\n",
       "Contact (1997)               509\n",
       "Fargo (1996)                 508\n",
       "Return of the Jedi (1983)    507\n",
       "Liar Liar (1997)             485\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
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      "text/plain": [
       "                             rating\n",
       "title                              \n",
       "'Til There Was You (1997)  2.333333\n",
       "1-900 (1994)               2.600000\n",
       "101 Dalmatians (1996)      2.908257\n",
       "12 Angry Men (1957)        4.344000\n",
       "187 (1997)                 3.024390"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.DataFrame(df.groupby('title')['rating'].mean())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now set the number of ratings column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>9</td>\n",
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       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
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       "      <th>101 Dalmatians (1996)</th>\n",
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       "      <td>109</td>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
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      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "ratings['num of ratings'] = pd.DataFrame(df.groupby('title')['rating'].count())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a few histograms:"
   ]
  },
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   "execution_count": 11,
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       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10c1c5d310>"
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings['num of ratings'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10a599e910>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings['rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f10a5862250>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rating',y='num of ratings',data=ratings,alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! Now that we have a general idea of what the data looks like, let's move on to creating a simple recommendation system:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommending Similar Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a matrix that has the user ids on one access and the movie title on another axis. Each cell will then consist of the rating the user gave to that movie. Note there will be a lot of NaN values, because most people have not seen most of the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <th>187 (1997)</th>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <th>3 Ninjas: High Noon At Mega Mountain (1998)</th>\n",
       "      <th>39 Steps, The (1935)</th>\n",
       "      <th>...</th>\n",
       "      <th>Yankee Zulu (1994)</th>\n",
       "      <th>Year of the Horse (1997)</th>\n",
       "      <th>You So Crazy (1994)</th>\n",
       "      <th>Young Frankenstein (1974)</th>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1664 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title    'Til There Was You (1997)  1-900 (1994)  101 Dalmatians (1996)  \\\n",
       "user_id                                                                   \n",
       "0                              NaN           NaN                    NaN   \n",
       "1                              NaN           NaN                    2.0   \n",
       "2                              NaN           NaN                    NaN   \n",
       "3                              NaN           NaN                    NaN   \n",
       "4                              NaN           NaN                    NaN   \n",
       "\n",
       "title    12 Angry Men (1957)  187 (1997)  2 Days in the Valley (1996)  \\\n",
       "user_id                                                                 \n",
       "0                        NaN         NaN                          NaN   \n",
       "1                        5.0         NaN                          NaN   \n",
       "2                        NaN         NaN                          NaN   \n",
       "3                        NaN         2.0                          NaN   \n",
       "4                        NaN         NaN                          NaN   \n",
       "\n",
       "title    20,000 Leagues Under the Sea (1954)  2001: A Space Odyssey (1968)  \\\n",
       "user_id                                                                      \n",
       "0                                        NaN                           NaN   \n",
       "1                                        3.0                           4.0   \n",
       "2                                        NaN                           NaN   \n",
       "3                                        NaN                           NaN   \n",
       "4                                        NaN                           NaN   \n",
       "\n",
       "title    3 Ninjas: High Noon At Mega Mountain (1998)  39 Steps, The (1935)  \\\n",
       "user_id                                                                      \n",
       "0                                                NaN                   NaN   \n",
       "1                                                NaN                   NaN   \n",
       "2                                                1.0                   NaN   \n",
       "3                                                NaN                   NaN   \n",
       "4                                                NaN                   NaN   \n",
       "\n",
       "title    ...  Yankee Zulu (1994)  Year of the Horse (1997)  \\\n",
       "user_id  ...                                                 \n",
       "0        ...                 NaN                       NaN   \n",
       "1        ...                 NaN                       NaN   \n",
       "2        ...                 NaN                       NaN   \n",
       "3        ...                 NaN                       NaN   \n",
       "4        ...                 NaN                       NaN   \n",
       "\n",
       "title    You So Crazy (1994)  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
       "user_id                                                                      \n",
       "0                        NaN                        NaN                NaN   \n",
       "1                        NaN                        5.0                3.0   \n",
       "2                        NaN                        NaN                NaN   \n",
       "3                        NaN                        NaN                NaN   \n",
       "4                        NaN                        NaN                NaN   \n",
       "\n",
       "title    Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
       "user_id                                                                \n",
       "0                         NaN                                    NaN   \n",
       "1                         NaN                                    NaN   \n",
       "2                         NaN                                    NaN   \n",
       "3                         NaN                                    NaN   \n",
       "4                         NaN                                    NaN   \n",
       "\n",
       "title    Zeus and Roxanne (1997)  unknown  Á köldum klaka (Cold Fever) (1994)  \n",
       "user_id                                                                        \n",
       "0                            NaN      NaN                                 NaN  \n",
       "1                            NaN      4.0                                 NaN  \n",
       "2                            NaN      NaN                                 NaN  \n",
       "3                            NaN      NaN                                 NaN  \n",
       "4                            NaN      NaN                                 NaN  \n",
       "\n",
       "[5 rows x 1664 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_mat = df.pivot_table(index='user_id',columns='title',values='rating')\n",
    "movie_mat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most rated movie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>4.359589</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact (1997)</th>\n",
       "      <td>3.803536</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fargo (1996)</th>\n",
       "      <td>4.155512</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>4.007890</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>3.156701</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Patient, The (1996)</th>\n",
       "      <td>3.656965</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scream (1996)</th>\n",
       "      <td>3.441423</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <td>3.878319</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air Force One (1997)</th>\n",
       "      <td>3.631090</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independence Day (ID4) (1996)</th>\n",
       "      <td>3.438228</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 rating  num of ratings\n",
       "title                                                  \n",
       "Star Wars (1977)               4.359589             584\n",
       "Contact (1997)                 3.803536             509\n",
       "Fargo (1996)                   4.155512             508\n",
       "Return of the Jedi (1983)      4.007890             507\n",
       "Liar Liar (1997)               3.156701             485\n",
       "English Patient, The (1996)    3.656965             481\n",
       "Scream (1996)                  3.441423             478\n",
       "Toy Story (1995)               3.878319             452\n",
       "Air Force One (1997)           3.631090             431\n",
       "Independence Day (ID4) (1996)  3.438228             429"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('num of ratings',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose two movies: starwars, a sci-fi movie. And Liar Liar, a comedy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</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>2.333333</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's grab the user ratings for those two movies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "0    5.0\n",
       "1    5.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "Name: Star Wars (1977), dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "star_wars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liar_liar_user_ratings = moviemat['Liar Liar (1997)']\n",
    "star_wars_user_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use corrwith() method to get correlations between two pandas series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alonkin/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/home/alonkin/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_star_wars = moviemat.corrwith(star_wars_user_ratings)\n",
    "similar_to_liar_liar = moviemat.corrwith(liar_liar_user_ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean this by removing NaN values and using a DataFrame instead of a series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>0.872872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation\n",
       "title                                 \n",
       "'Til There Was You (1997)     0.872872\n",
       "1-900 (1994)                 -0.645497\n",
       "101 Dalmatians (1996)         0.211132\n",
       "12 Angry Men (1957)           0.184289\n",
       "187 (1997)                    0.027398"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_star_wars = pd.DataFrame(similar_to_star_wars,columns=['Correlation'])\n",
    "corr_star_wars.dropna(inplace=True)\n",
    "corr_star_wars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we sort the dataframe by correlation, we should get the most similar movies, however note that we get some results that don't really make sense. This is because there are a lot of movies only watched once by users who also watched star wars (it was the most popular movie). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hollow Reed (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stripes (1981)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man of the Year (1995)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Safe Passage (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlaw, The (1943)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Line King: Al Hirschfeld, The (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hurricane Streets (1998)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation\n",
       "title                                                          \n",
       "Hollow Reed (1996)                                          1.0\n",
       "Stripes (1981)                                              1.0\n",
       "Star Wars (1977)                                            1.0\n",
       "Man of the Year (1995)                                      1.0\n",
       "Beans of Egypt, Maine, The (1994)                           1.0\n",
       "Safe Passage (1994)                                         1.0\n",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "Outlaw, The (1943)                                          1.0\n",
       "Line King: Al Hirschfeld, The (1996)                        1.0\n",
       "Hurricane Streets (1998)                                    1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_star_wars.sort_values('Correlation',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fix this by filtering out movies that have less than 100 reviews (this value was chosen based off the histogram from earlier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</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>0.872872</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation  num of ratings\n",
       "title                                                 \n",
       "'Til There Was You (1997)     0.872872               9\n",
       "1-900 (1994)                 -0.645497               5\n",
       "101 Dalmatians (1996)         0.211132             109\n",
       "12 Angry Men (1957)           0.184289             125\n",
       "187 (1997)                    0.027398              41"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_star_wars = corr_star_wars.join(ratings['num of ratings'])\n",
    "corr_star_wars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now sort the values and notice how the titles make a lot more sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empire Strikes Back, The (1980)</th>\n",
       "      <td>0.748353</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>0.672556</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (1981)</th>\n",
       "      <td>0.536117</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin Powers: International Man of Mystery (1997)</th>\n",
       "      <td>0.377433</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  \\\n",
       "title                                                             \n",
       "Star Wars (1977)                                       1.000000   \n",
       "Empire Strikes Back, The (1980)                        0.748353   \n",
       "Return of the Jedi (1983)                              0.672556   \n",
       "Raiders of the Lost Ark (1981)                         0.536117   \n",
       "Austin Powers: International Man of Mystery (1997)     0.377433   \n",
       "\n",
       "                                                    num of ratings  \n",
       "title                                                               \n",
       "Star Wars (1977)                                               584  \n",
       "Empire Strikes Back, The (1980)                                368  \n",
       "Return of the Jedi (1983)                                      507  \n",
       "Raiders of the Lost Ark (1981)                                 420  \n",
       "Austin Powers: International Man of Mystery (1997)             130  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_star_wars[corr_star_wars['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the same for the comedy Liar Liar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batman Forever (1995)</th>\n",
       "      <td>0.516968</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mask, The (1994)</th>\n",
       "      <td>0.484650</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Down Periscope (1996)</th>\n",
       "      <td>0.472681</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Con Air (1997)</th>\n",
       "      <td>0.469828</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Correlation  num of ratings\n",
       "title                                             \n",
       "Liar Liar (1997)          1.000000             485\n",
       "Batman Forever (1995)     0.516968             114\n",
       "Mask, The (1994)          0.484650             129\n",
       "Down Periscope (1996)     0.472681             101\n",
       "Con Air (1997)            0.469828             137"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liar_liar = pd.DataFrame(similar_to_liar_liar,columns=['Correlation'])\n",
    "corr_liar_liar.dropna(inplace=True)\n",
    "corr_liar_liar = corr_liar_liar.join(ratings['num of ratings'])\n",
    "corr_liar_liar[corr_liar_liar['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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