Source_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the 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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('u.data', sep='\\t', names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_titles = pd.read_csv('Movie_Id_Titles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge them together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, movie_titles, on='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<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",
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       "  </tbody>\n",
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      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import vizualisation libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### create a ratings dataframe with average rating and number of ratings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings =pd.DataFrame(df.groupby('title')['rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Set the number of ratings column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings['rating_numbers'] = pd.DataFrame(df.groupby('title')['rating'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_numbers</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  rating_numbers\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": [
    "##### Number of ratings histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x202e4daf668>"
      ]
     },
     "execution_count": 17,
     "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_numbers'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average rating per movie histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x202e4ee86d8>"
      ]
     },
     "execution_count": 18,
     "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'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Relationship between the average rating and the actual number of ratings\n",
    "###### The larger the number of ratings, the more likely the rating of a movie is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x202e4d3f860>"
      ]
     },
     "execution_count": 19,
     "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": [
    "sns.jointplot(x='rating', y='rating_numbers', data=ratings, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommending Similar Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. The NaN values are due to most people not having seen most of the movies."
   ]
  },
  {
   "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>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)  \\\n",
       "user_id                 ...                                       \n",
       "0                       ...                                 NaN   \n",
       "1                       ...                                 NaN   \n",
       "2                       ...                                 NaN   \n",
       "3                       ...                                 NaN   \n",
       "4                       ...                                 NaN   \n",
       "\n",
       "title    Year of the Horse (1997)  You So Crazy (1994)  \\\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    Young Frankenstein (1974)  Young Guns (1988)  Young Guns II (1990)  \\\n",
       "user_id                                                                       \n",
       "0                              NaN                NaN                   NaN   \n",
       "1                              5.0                3.0                   NaN   \n",
       "2                              NaN                NaN                   NaN   \n",
       "3                              NaN                NaN                   NaN   \n",
       "4                              NaN                NaN                   NaN   \n",
       "\n",
       "title    Young Poisoner's Handbook, The (1995)  Zeus and Roxanne (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    unknown  Á köldum klaka (Cold Fever) (1994)  \n",
       "user_id                                               \n",
       "0            NaN                                 NaN  \n",
       "1            4.0                                 NaN  \n",
       "2            NaN                                 NaN  \n",
       "3            NaN                                 NaN  \n",
       "4            NaN                                 NaN  \n",
       "\n",
       "[5 rows x 1664 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat = df.pivot_table(index='user_id', columns='title', values='rating')\n",
    "moviemat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Most rated movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <th>rating_numbers</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  rating_numbers\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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('rating_numbers', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's choose two movies for our system: Starwars, a sci-fi movie. And Liar Liar, a comedy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the user ratings for those two movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "starwars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liar_liar_user_ratings =moviemat['Liar Liar (1997)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "Name: Star Wars (1977), dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starwars_user_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### correlation of every other movie to that specific user behaviour on the StarWars movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Anaconda\\lib\\site-packages\\numpy\\lib\\function_base.py:2392: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "G:\\Anaconda\\lib\\site-packages\\numpy\\lib\\function_base.py:2326: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_starwars = moviemat.corrwith(starwars_user_ratings)\n",
    "similar_to_starwars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### correlation of every other movie to that specific user behaviour on the Liar Liar movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "'Til There Was You (1997)    0.118913\n",
       "1-900 (1994)                      NaN\n",
       "101 Dalmatians (1996)        0.469765\n",
       "12 Angry Men (1957)          0.066272\n",
       "187 (1997)                   0.175145\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_liarliar = moviemat.corrwith(liar_liar_user_ratings)\n",
    "similar_to_liarliar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### remove the NaN values and use a DF instead of Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars, columns=['Correlation'])\n",
    "corr_starwars.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perfectly correlated movies with StarWars? \n",
    "##### most likely these movies happen to have been seen only by one person who also happend to rate StarWars 5 stars"
   ]
  },
  {
   "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",
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>Man of the Year (1995)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hollow Reed (1996)</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>Good Man in Africa, A (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",
       "Commandments (1997)                                         1.0\n",
       "Cosi (1996)                                                 1.0\n",
       "No Escape (1994)                                            1.0\n",
       "Stripes (1981)                                              1.0\n",
       "Man of the Year (1995)                                      1.0\n",
       "Hollow Reed (1996)                                          1.0\n",
       "Beans of Egypt, Maine, The (1994)                           1.0\n",
       "Good Man in Africa, A (1994)                                1.0\n",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "Outlaw, The (1943)                                          1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values('Correlation', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set a threshold for the number of ratings necessary and filter out movies that have less than a certain number of reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "join the 'number of ratings' column to our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>rating_numbers</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_numbers\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = corr_starwars.join(ratings['rating_numbers'], how='left', lsuffix='_left', rsuffix='_right')\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "filter out movies that have less than 100 reviews (this value was chosen based off the ratings histogram from earlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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_numbers</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",
       "                                                    rating_numbers  \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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['rating_numbers']>100].sort_values('Correlation', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perfectly correlated movies with Liar Liar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>0.118913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.469765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.066272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.175145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation\n",
       "title                                 \n",
       "'Til There Was You (1997)     0.118913\n",
       "1-900 (1994)                       NaN\n",
       "101 Dalmatians (1996)         0.469765\n",
       "12 Angry Men (1957)           0.066272\n",
       "187 (1997)                    0.175145"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar, columns=['Correlation'])\n",
    "corr_liarliar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### remove the NaN values and use a DF instead of Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_liarliar.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>rating_numbers</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>0.118913</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.469765</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.066272</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.175145</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td>0.040739</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Correlation  rating_numbers\n",
       "title                                                   \n",
       "'Til There Was You (1997)       0.118913               9\n",
       "101 Dalmatians (1996)           0.469765             109\n",
       "12 Angry Men (1957)             0.066272             125\n",
       "187 (1997)                      0.175145              41\n",
       "2 Days in the Valley (1996)     0.040739              93"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar = corr_liarliar.join(ratings['rating_numbers'], how='left')\n",
    "corr_liarliar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "filter out movies that have less than 100 reviews (this value was chosen randomly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>rating_numbers</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  rating_numbers\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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar[corr_liarliar['rating_numbers']>100].sort_values('Correlation', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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