s23-Recommender-Systems-Notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recommended Reading: Recommender system by J and Z\n",
    "\n",
    "# Fully developed and deployed recommender systems are extremly complex and resource intensive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two types of recommender system:\n",
    "\n",
    "\n",
    "#### Content-Based:\n",
    "    * Focus on the attribute and give the similarity between items.\n",
    "    \n",
    "    \n",
    "#### Collaborative-Filtering:\n",
    "    * Produces recommendations based on the knowledge from other users\n",
    "    * More commonly used.\n",
    "    \n",
    "    * two sub cases:\n",
    "        * Memory based \n",
    "        \n",
    "        * Model based\n",
    "\n",
    "   \n",
    "<!-- #### Then there will be a dataset  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Explore the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recommendation system for movies! A beginer dataset for recommendation system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "df = pd.read_csv('u.data', sep='\\t', names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "\n",
    "# item_id is the numeric representation of movie titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_titles = pd.read_csv('Movie_Id_Titles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge\n",
    "df = pd.merge(df, movie_titles, on='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>880473582</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>891271545</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>888552084</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>879362124</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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",
       "Entertaining Angels: The Dorothy Day Story (1996)    5.0\n",
       "Saint of Fort Washington, The (1993)                 5.0\n",
       "Great Day in Harlem, A (1994)                        5.0\n",
       "They Made Me a Criminal (1939)                       5.0\n",
       "Aiqing wansui (1994)                                 5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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",
       "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",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.DataFrame(df.groupby('title')['rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>rating</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nratings = pd.DataFrame(df.groupby('title')['rating'].count().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.merge(ratings, nratings, on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>rating_x</th>\n",
       "      <th>rating_y</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.772727</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
       "      <td>3.341463</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <td>2.166667</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>3.444444</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1664 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       rating_x  rating_y\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\n",
       "...                                         ...       ...\n",
       "Young Guns II (1990)                   2.772727        44\n",
       "Young Poisoner's Handbook, The (1995)  3.341463        41\n",
       "Zeus and Roxanne (1997)                2.166667         6\n",
       "unknown                                3.444444         9\n",
       "Á köldum klaka (Cold Fever) (1994)     3.000000         1\n",
       "\n",
       "[1664 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings\n",
    "\n",
    "# rating_y is just the number of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9069acf9d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings['rating_y'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9069c493a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings['rating_x'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f9069dddc10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# When there are some data points overlapping each other, it's best to ise alpha so the graph will be clearly shown!\n",
    "\n",
    "sns.jointplot(x='rating_x', y='rating_y', data=ratings, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Train the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviemat = df.pivot_table(index='user_id', columns='title', values='rating' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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",
       "    <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",
       "      <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>939</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>940</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>941</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>942</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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</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>943</th>\n",
       "      <td>NaN</td>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</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>944 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",
       "939                            NaN           NaN                    NaN   \n",
       "940                            NaN           NaN                    NaN   \n",
       "941                            NaN           NaN                    NaN   \n",
       "942                            NaN           NaN                    NaN   \n",
       "943                            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",
       "939                      NaN         NaN                          NaN   \n",
       "940                      NaN         NaN                          NaN   \n",
       "941                      NaN         NaN                          NaN   \n",
       "942                      NaN         NaN                          NaN   \n",
       "943                      NaN         NaN                          2.0   \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",
       "939                                      NaN                           NaN   \n",
       "940                                      NaN                           NaN   \n",
       "941                                      NaN                           NaN   \n",
       "942                                      NaN                           3.0   \n",
       "943                                      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",
       "939                                              NaN                   NaN   \n",
       "940                                              NaN                   NaN   \n",
       "941                                              NaN                   NaN   \n",
       "942                                              NaN                   3.0   \n",
       "943                                              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",
       "939      ...                 NaN                       NaN   \n",
       "940      ...                 NaN                       NaN   \n",
       "941      ...                 NaN                       NaN   \n",
       "942      ...                 NaN                       NaN   \n",
       "943      ...                 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",
       "939                      NaN                        NaN                NaN   \n",
       "940                      NaN                        NaN                NaN   \n",
       "941                      NaN                        NaN                NaN   \n",
       "942                      NaN                        NaN                NaN   \n",
       "943                      NaN                        NaN                4.0   \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",
       "939                       NaN                                    NaN   \n",
       "940                       NaN                                    NaN   \n",
       "941                       NaN                                    NaN   \n",
       "942                       NaN                                    NaN   \n",
       "943                       3.0                                    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",
       "939                          NaN      NaN                                 NaN  \n",
       "940                          NaN      NaN                                 NaN  \n",
       "941                          NaN      NaN                                 NaN  \n",
       "942                          NaN      NaN                                 NaN  \n",
       "943                          NaN      NaN                                 NaN  \n",
       "\n",
       "[944 rows x 1664 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>rating_x</th>\n",
       "      <th>rating_y</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_x  rating_y\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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('rating_y',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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",
       "      ... \n",
       "939    NaN\n",
       "940    4.0\n",
       "941    NaN\n",
       "942    5.0\n",
       "943    4.0\n",
       "Name: Star Wars (1977), Length: 944, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starwars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liarliar_user_ratings = moviemat['Liar Liar (1997)']\n",
    "starwars_user_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/lib/function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/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_starwars = moviemat.corrwith(starwars_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/lib/function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/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_liarliar = moviemat.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars, columns=['Correlation'])\n",
    "corr_starwars.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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",
       "    </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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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",
       "    </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>Commandments (1997)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cosi (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Escape (1994)</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation\n",
       "title                                                          \n",
       "Hollow Reed (1996)                                          1.0\n",
       "Commandments (1997)                                         1.0\n",
       "Cosi (1996)                                                 1.0\n",
       "No Escape (1994)                                            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",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "Outlaw, The (1943)                                          1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values('Correlation', ascending=False).head(10)\n",
    "# It's weriod since some movie may only have a handful number of people\n",
    "#  who watched it, so we need to filter out the movies with handful watchers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = corr_starwars.join(ratings['rating_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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>rating_y</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  rating_y\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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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>rating_y</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",
       "    <tr>\n",
       "      <th>Sting, The (1973)</th>\n",
       "      <td>0.367538</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana Jones and the Last Crusade (1989)</th>\n",
       "      <td>0.350107</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pinocchio (1940)</th>\n",
       "      <td>0.347868</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frighteners, The (1996)</th>\n",
       "      <td>0.332729</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L.A. Confidential (1997)</th>\n",
       "      <td>0.319065</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  rating_y\n",
       "title                                                                    \n",
       "Star Wars (1977)                                       1.000000       584\n",
       "Empire Strikes Back, The (1980)                        0.748353       368\n",
       "Return of the Jedi (1983)                              0.672556       507\n",
       "Raiders of the Lost Ark (1981)                         0.536117       420\n",
       "Austin Powers: International Man of Mystery (1997)     0.377433       130\n",
       "Sting, The (1973)                                      0.367538       241\n",
       "Indiana Jones and the Last Crusade (1989)              0.350107       331\n",
       "Pinocchio (1940)                                       0.347868       101\n",
       "Frighteners, The (1996)                                0.332729       115\n",
       "L.A. Confidential (1997)                               0.319065       297"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['rating_y']>100].sort_values('Correlation', ascending=False).head(10)\n",
    "\n",
    "# Some of them are highly popular!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar, columns=['Correlation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar = corr_liarliar.join(ratings['rating_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": 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>rating_y</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>Bean (1997)</th>\n",
       "      <td>0.504689</td>\n",
       "      <td>91</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bullets Over Broadway (1994)</th>\n",
       "      <td>-0.201614</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)</th>\n",
       "      <td>-0.238092</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welcome to the Dollhouse (1995)</th>\n",
       "      <td>-0.254231</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raging Bull (1980)</th>\n",
       "      <td>-0.308129</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Three Colors: Red (1994)</th>\n",
       "      <td>-0.385208</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  rating_y\n",
       "title                                                                    \n",
       "Liar Liar (1997)                                       1.000000       485\n",
       "Batman Forever (1995)                                  0.516968       114\n",
       "Bean (1997)                                            0.504689        91\n",
       "Mask, The (1994)                                       0.484650       129\n",
       "Down Periscope (1996)                                  0.472681       101\n",
       "...                                                         ...       ...\n",
       "Bullets Over Broadway (1994)                          -0.201614        86\n",
       "Dr. Strangelove or: How I Learned to Stop Worry...    -0.238092       194\n",
       "Welcome to the Dollhouse (1995)                       -0.254231       112\n",
       "Raging Bull (1980)                                    -0.308129       116\n",
       "Three Colors: Red (1994)                              -0.385208        83\n",
       "\n",
       "[416 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# ALWAYS, rating_y is the number of watchers\n",
    "\n",
    "\n",
    "corr_liarliar[corr_liarliar['rating_y']>80].sort_values('Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}