01-Recommender Systems with Python.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender Systems with Python\n",
    "\n",
    "Welcome to the code notebook for Recommender Systems with Python. In this lecture we will develop basic recommendation systems using Python and pandas. There is another notebook: *Advanced Recommender Systems with Python*. That notebook goes into more detail with the same data set.\n",
    "\n",
    "In this notebook, we will focus on providing a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. Keep in mind, this is not a true robust recommendation system, to describe it more accurately,it just tells you what movies/items are most similar to your movie choice.\n",
    "\n",
    "There is no project for this topic, instead you have the option to work through the advanced lecture version of this notebook (totally optional!).\n",
    "\n",
    "Let's get started!\n",
    "\n",
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "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": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the movie titles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles = pd.read_csv(\"Movie_Id_Titles\")\n",
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can merge them together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,movie_titles,on='item_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "Let's explore the data a bit and get a look at some of the best rated movies.\n",
    "\n",
    "## Visualization Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a ratings dataframe with average rating and number of ratings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.DataFrame(df.groupby('title')['rating'].mean())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now set the number of ratings column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['num of ratings'] = pd.DataFrame(df.groupby('title')['rating'].count())\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a few histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1258f8780>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VJEmai07awAXNlwzH9j5dcTWSJGmu8rYQkiRJhRm4JEmSCjuplxRLaXUPL/A+XpIknUwM\nXDPQziccb/vCv017Dy/wPl6SJJ1sDFwz0O4nHL2HlyRJAgPXjPkJR0mS1C43zUuSJBXmDFcPtNoD\nBm6qlyRpLjFw9UCrPWBuqpckaW4xcPXITB+M7S0nJEnqPwauWajZkqO3nJAkqf8YuGahZkuO3nJC\nkqT+Y+CapY635OgtJyRJ6j8GLnWN+8skSZqegeskMz4+zo4dO5qeM9NQtGvXLq695V73l0mSdAwD\n1xzT6h5fjz76KHf/4NkZhaJWM1i7d++e8acvS3P2TZLUSwauOab1cx6z6ab7Tj4h+cKG/tnI2TdJ\nUi8ZuOagTp7z2MknJGf7hv5mfWk1M+jslySpEwYuvUSvPiHZatmvWehpZ7mzmWZB09kvSVKnDFyq\nTKtZpGZLlr/e8xR//1d/yFlnnXXC10J7y52zcf+Ze88kaW7oauCKiHnAZ4HzgUPAX2bmE918D/Wv\n1vvLjr9kObb36UagOvFrX7i+lFah6IknnmDp0qVNQ9HxQlMv957N5rDXqrYnn3ySN77xjQZRSbNG\nt2e4/gRYkplviYg3A3c0jklAZ/vLOrm2E53MzEE9DA4M/veMQ1Mne89g5kuxs/kxUu0E0ZUrV7oM\nLGnW6Hbguhj4F4DM/I+I+P0uv75UuU5m5qAeBmcamjrZewadLcW2+nOVDHutroXZuQQsScfT7cC1\nDNg75fujETE/Mye6/D5SpUrOrrXzydBOapvpUmwnn2iFzsJeO9c20yoMjo+PA0wb6JqNVTHeSUjt\n5L07rbvVMm6z2jt9b5j58nbppfNO/tyt3rvVjaxPxv2ds3krRLcD1z5gcMr3zcLWAoCJPY9zdMH+\naU94fs8IBw7A0UP7XjI29qv/Zfzwr6cdKz3ue/ve3R5fcsqpHD30speMTR49yP7RJ4q8dqvX7/S1\nD+75BTff/lWWLD112vH9z/4Pg6edNe317V87fW17nvopN99ea3r94qXLph1vNlZ6/PmxPdyy+nLO\nPPPMaa/9+c9/zvo7Hyzy3p3+uZ4f28OhQ4dmVHs33rtZ35pp1dNOXrvV63f659qyZQv//ODOGf0s\nzVXt/P/87N9df9x/zDXz1FNPvfDljNLavMnJyZlcN62I+FPgnZl5fUT8AXBrZv7xcc69GNjStTeX\nJEkq75LM3HqiF3V7huubwOUR8cPG9+9vcu424BLgl8B4l+uQJEnqpgXAq6nnlxPW1RkuSZIkvdT8\nXhcgSZI01xm4JEmSCjNwSZIkFWbgkiRJKqwnD6/2mYvlNR6tdHtmvjUizgY2ARPAcGauaZyzGrgB\nOAKsy8zNvaq3n0XEQuCLwOuBxcA64CfY82IiYj5wJxDUe/wB4HnseXERcRrwGPB26p8w34Q9LyYi\narx4Q/HdwMex50VFxEeBdwOLqGeVR+hCz3s1w/X/z1wEbqH+zEV1SUR8hPovoyWNQ3cAazNzFTA/\nIq6OiBXATcBFwJXA+ohY1JOC+981wLOZeSn1Xn4Ge17au4DJzLwYuJX6LyF7XljjHxefB8Yah+x5\nQRGxBCAz39b47y+w50VFxCrgokY+uQz4HbrU814Frt945iLgMxe766fAe6Z8P5SZL9xk9n7gcuBC\nYGtmHs3MfcBO4Lxqy5wzvkb9lz7U79NyFLjAnpeTmd+m/i9LgNcBv8KeV+GTwOeAXwDzsOelnQ+c\nEhEPRMS/NlYu7HlZVwDDEfEt4DvAd+lSz3sVuKZ95mKPaplzMvOb1H/pv2DelK/3U+//IL/5/+AA\nsLx8dXNPZo5l5q8jYhD4OvAx7HlxmTkREZuADcC92POiIuI64JnMfJAXez3172173n1jwCcy8wrg\nRuAe/Dkv7ZXAEPBnvNjzrvyc9yrknMgzF9W5qb0dBPZQ/3+wbJrjmoGIOBN4CPhyZn4Ve16JzLwO\nOAe4CxiYMmTPu+/91J8k8gPqMy9fAV41Zdyed98O6r/wycydwHPAiinj9rz7ngMeaMxc7aC+z3xq\nkJpxz3sVuH4I/BFA45mL/9WjOk4WP4qISxtfX0X9GZbbgIsjYnFELAfOBYZ7VWA/a6zlPwD8TWZ+\nuXH4x/a8nIi4prGxFep/IY4DjzX2X4A977rMXJWZb83MtwL/CVwL3O/PeVHXA/8IEBGvof4L/nv+\nnBe1lfqerBd6fgrw/W70vCefUuTEnrmozt0M3NnY0LcduC8zJyNiA/UfrnnUNwQe7mWRfewW4FTg\n1oi4DZgEPgR82p4X8w3gSxHxMPW/xz4IPA7cZc8r5d8tZW2k/nO+hfqs+XXUZ2D8OS8kMzdHxCUR\n8Sj1Xt4I/Iwu9NxnKUqSJBXmRnVJkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFL\nkiSpMAOXJElSYf8HpRtYumRv1GQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125a06ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings['num of ratings'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125d12908>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x125d12080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings['rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x126005320>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZLK9BiIxRE9nPf/5zbrrppuzPF5rEAJ566ikUReEXv/gFe/fu5fvf/z62bbNp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\nek/RsQXKgzjSc8xqkWRsskoiQ7JpF8cW7OovPecEaMPCxDREGxY9R8M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dymHz4x//mI6O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12554f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rating',y='num of ratings',data=ratings,alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! Now that we have a general idea of what the data looks like, let's move on to creating a simple recommendation system:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommending Similar Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a matrix that has the user ids on one access and the movie title on another axis. Each cell will then consist of the rating the user gave to that movie. Note there will be a lot of NaN values, because most people have not seen most of the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 149,
     "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 movie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>4.359589</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact (1997)</th>\n",
       "      <td>3.803536</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fargo (1996)</th>\n",
       "      <td>4.155512</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>4.007890</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>3.156701</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Patient, The (1996)</th>\n",
       "      <td>3.656965</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scream (1996)</th>\n",
       "      <td>3.441423</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <td>3.878319</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air Force One (1997)</th>\n",
       "      <td>3.631090</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independence Day (ID4) (1996)</th>\n",
       "      <td>3.438228</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 rating  num of ratings\n",
       "title                                                  \n",
       "Star Wars (1977)               4.359589             584\n",
       "Contact (1997)                 3.803536             509\n",
       "Fargo (1996)                   4.155512             508\n",
       "Return of the Jedi (1983)      4.007890             507\n",
       "Liar Liar (1997)               3.156701             485\n",
       "English Patient, The (1996)    3.656965             481\n",
       "Scream (1996)                  3.441423             478\n",
       "Toy Story (1995)               3.878319             452\n",
       "Air Force One (1997)           3.631090             431\n",
       "Independence Day (ID4) (1996)  3.438228             429"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('num of ratings',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose two movies: starwars, a sci-fi movie. And Liar Liar, a comedy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rating  num of ratings\n",
       "title                                              \n",
       "'Til There Was You (1997)  2.333333               9\n",
       "1-900 (1994)               2.600000               5\n",
       "101 Dalmatians (1996)      2.908257             109\n",
       "12 Angry Men (1957)        4.344000             125\n",
       "187 (1997)                 3.024390              41"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's grab the user ratings for those two movies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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": 162,
     "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.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use corrwith() method to get correlations between two pandas series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marci/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:2487: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "similar_to_starwars = moviemat.corrwith(starwars_user_ratings)\n",
    "similar_to_liarliar = moviemat.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean this by removing NaN values and using a DataFrame instead of a series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars,columns=['Correlation'])\n",
    "corr_starwars.dropna(inplace=True)\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we sort the dataframe by correlation, we should get the most similar movies, however note that we get some results that don't really make sense. This is because there are a lot of movies only watched once by users who also watched star wars (it was the most popular movie). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values('Correlation',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fix this by filtering out movies that have less than 100 reviews (this value was chosen based off the histogram from earlier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>0.872872</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation  num of ratings\n",
       "title                                                 \n",
       "'Til There Was You (1997)     0.872872               9\n",
       "1-900 (1994)                 -0.645497               5\n",
       "101 Dalmatians (1996)         0.211132             109\n",
       "12 Angry Men (1957)           0.184289             125\n",
       "187 (1997)                    0.027398              41"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = corr_starwars.join(ratings['num of ratings'])\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now sort the values and notice how the titles make a lot more sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empire Strikes Back, The (1980)</th>\n",
       "      <td>0.748353</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
       "      <td>0.672556</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (1981)</th>\n",
       "      <td>0.536117</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin Powers: International Man of Mystery (1997)</th>\n",
       "      <td>0.377433</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  \\\n",
       "title                                                             \n",
       "Star Wars (1977)                                       1.000000   \n",
       "Empire Strikes Back, The (1980)                        0.748353   \n",
       "Return of the Jedi (1983)                              0.672556   \n",
       "Raiders of the Lost Ark (1981)                         0.536117   \n",
       "Austin Powers: International Man of Mystery (1997)     0.377433   \n",
       "\n",
       "                                                    num of ratings  \n",
       "title                                                               \n",
       "Star Wars (1977)                                               584  \n",
       "Empire Strikes Back, The (1980)                                368  \n",
       "Return of the Jedi (1983)                                      507  \n",
       "Raiders of the Lost Ark (1981)                                 420  \n",
       "Austin Powers: International Man of Mystery (1997)             130  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the same for the comedy Liar Liar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>num of ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batman Forever (1995)</th>\n",
       "      <td>0.516968</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mask, The (1994)</th>\n",
       "      <td>0.484650</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Down Periscope (1996)</th>\n",
       "      <td>0.472681</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Con Air (1997)</th>\n",
       "      <td>0.469828</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Correlation  num of ratings\n",
       "title                                             \n",
       "Liar Liar (1997)          1.000000             485\n",
       "Batman Forever (1995)     0.516968             114\n",
       "Mask, The (1994)          0.484650             129\n",
       "Down Periscope (1996)     0.472681             101\n",
       "Con Air (1997)            0.469828             137"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar,columns=['Correlation'])\n",
    "corr_liarliar.dropna(inplace=True)\n",
    "corr_liarliar = corr_liarliar.join(ratings['num of ratings'])\n",
    "corr_liarliar[corr_liarliar['num of ratings']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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