MLdatasettest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import re\n",
    "from scipy.sparse import csr_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from surprise import Reader, Dataset, SVD, evaluate\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 1 shape: (100836, 3)\n",
      "-Dataset examples-\n",
      "        Cust_Id  Rating  Movie_Id\n",
      "0             1     4.0         1\n",
      "1             1     4.0         3\n",
      "2             1     4.0         6\n",
      "3             1     5.0        47\n",
      "4             1     5.0        50\n",
      "5             1     3.0        70\n",
      "6             1     5.0       101\n",
      "7             1     4.0       110\n",
      "8             1     5.0       151\n",
      "9             1     5.0       157\n",
      "10            1     5.0       163\n",
      "11            1     5.0       216\n",
      "12            1     3.0       223\n",
      "13            1     5.0       231\n",
      "14            1     4.0       235\n",
      "15            1     5.0       260\n",
      "16            1     3.0       296\n",
      "17            1     3.0       316\n",
      "18            1     5.0       333\n",
      "19            1     4.0       349\n",
      "20            1     4.0       356\n",
      "21            1     5.0       362\n",
      "22            1     4.0       367\n",
      "23            1     3.0       423\n",
      "24            1     4.0       441\n",
      "25            1     5.0       457\n",
      "26            1     4.0       480\n",
      "27            1     3.0       500\n",
      "28            1     5.0       527\n",
      "29            1     4.0       543\n",
      "...         ...     ...       ...\n",
      "100806      610     3.0    150401\n",
      "100807      610     4.0    152077\n",
      "100808      610     4.0    152081\n",
      "100809      610     3.5    152372\n",
      "100810      610     3.5    155064\n",
      "100811      610     5.0    156371\n",
      "100812      610     4.5    156726\n",
      "100813      610     4.0    157296\n",
      "100814      610     5.0    158238\n",
      "100815      610     3.5    158721\n",
      "100816      610     3.5    158872\n",
      "100817      610     3.0    158956\n",
      "100818      610     3.0    159093\n",
      "100819      610     3.0    160080\n",
      "100820      610     2.5    160341\n",
      "100821      610     4.5    160527\n",
      "100822      610     3.0    160571\n",
      "100823      610     3.0    160836\n",
      "100824      610     4.0    161582\n",
      "100825      610     4.0    161634\n",
      "100826      610     3.5    162350\n",
      "100827      610     3.5    163937\n",
      "100828      610     3.5    163981\n",
      "100829      610     5.0    164179\n",
      "100830      610     4.0    166528\n",
      "100831      610     4.0    166534\n",
      "100832      610     5.0    168248\n",
      "100833      610     5.0    168250\n",
      "100834      610     5.0    168252\n",
      "100835      610     3.0    170875\n",
      "\n",
      "[100836 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Skip date\n",
    "\n",
    "df1 = pd.read_csv(r'C:\\Users\\HP\\Desktop\\BE data\\ml-latest-small\\ratings.csv', header = None, names = ['Cust_Id','Movie_Id','Rating'],usecols = [0,1,2])\n",
    "df1 = df1[['Cust_Id','Rating','Movie_Id']]\n",
    "#df1.columns = df1.columns.str.strip()\n",
    "\n",
    "print('Dataset 1 shape: {}'.format(df1.shape))\n",
    "print('-Dataset examples-')\n",
    "print(df1.iloc[::1, :])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full dataset shape: (100836, 3)\n",
      "-Dataset examples-\n",
      "        Cust_Id  Rating  Movie_Id\n",
      "0             1     4.0         1\n",
      "1             1     4.0         3\n",
      "2             1     4.0         6\n",
      "3             1     5.0        47\n",
      "4             1     5.0        50\n",
      "5             1     3.0        70\n",
      "6             1     5.0       101\n",
      "7             1     4.0       110\n",
      "8             1     5.0       151\n",
      "9             1     5.0       157\n",
      "10            1     5.0       163\n",
      "11            1     5.0       216\n",
      "12            1     3.0       223\n",
      "13            1     5.0       231\n",
      "14            1     4.0       235\n",
      "15            1     5.0       260\n",
      "16            1     3.0       296\n",
      "17            1     3.0       316\n",
      "18            1     5.0       333\n",
      "19            1     4.0       349\n",
      "20            1     4.0       356\n",
      "21            1     5.0       362\n",
      "22            1     4.0       367\n",
      "23            1     3.0       423\n",
      "24            1     4.0       441\n",
      "25            1     5.0       457\n",
      "26            1     4.0       480\n",
      "27            1     3.0       500\n",
      "28            1     5.0       527\n",
      "29            1     4.0       543\n",
      "...         ...     ...       ...\n",
      "100806      610     3.0    150401\n",
      "100807      610     4.0    152077\n",
      "100808      610     4.0    152081\n",
      "100809      610     3.5    152372\n",
      "100810      610     3.5    155064\n",
      "100811      610     5.0    156371\n",
      "100812      610     4.5    156726\n",
      "100813      610     4.0    157296\n",
      "100814      610     5.0    158238\n",
      "100815      610     3.5    158721\n",
      "100816      610     3.5    158872\n",
      "100817      610     3.0    158956\n",
      "100818      610     3.0    159093\n",
      "100819      610     3.0    160080\n",
      "100820      610     2.5    160341\n",
      "100821      610     4.5    160527\n",
      "100822      610     3.0    160571\n",
      "100823      610     3.0    160836\n",
      "100824      610     4.0    161582\n",
      "100825      610     4.0    161634\n",
      "100826      610     3.5    162350\n",
      "100827      610     3.5    163937\n",
      "100828      610     3.5    163981\n",
      "100829      610     5.0    164179\n",
      "100830      610     4.0    166528\n",
      "100831      610     4.0    166534\n",
      "100832      610     5.0    168248\n",
      "100833      610     5.0    168250\n",
      "100834      610     5.0    168252\n",
      "100835      610     3.0    170875\n",
      "\n",
      "[100836 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# # load less data for speed\n",
    "\n",
    "df = df1\n",
    "#df = df1.append(df2)\n",
    "#df = df.append(df3)\n",
    "#df = df.append(df4)\n",
    "\n",
    "df.index = np.arange(0,len(df))\n",
    "print('Full dataset shape: {}'.format(df.shape))\n",
    "print('-Dataset examples-')\n",
    "print(df.iloc[::1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df.groupby('Rating')['Rating'].agg(['count'])\n",
    "\n",
    "# get movie count\n",
    "movie_count = df.isnull().sum()[1]\n",
    "\n",
    "# get customer count\n",
    "cust_count = df['Cust_Id'].nunique() - movie_count\n",
    "\n",
    "# get rating count\n",
    "rating_count = df['Cust_Id'].count() - movie_count\n",
    "\n",
    "ax = p.plot(kind = 'barh', legend = False, figsize = (15,10))\n",
    "plt.title('Total pool: {:,} Movies, {:,} customers, {:,} ratings given'.format(movie_count, cust_count, rating_count), fontsize=20)\n",
    "plt.axis('off')\n",
    "\n",
    "for i in range(1,6):\n",
    "    ax.text(p.iloc[i-1][0]/4, i-1, 'Rating {}: {:.0f}%'.format(i, p.iloc[i-1][0]*100 / p.sum()[0]), color = 'white', weight = 'bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie minimum times of review: 1.0\n",
      "Customer minimum times of review: 20.0\n"
     ]
    }
   ],
   "source": [
    "f = ['count','mean']\n",
    "\n",
    "df_movie_summary = df.groupby('Movie_Id')['Rating'].agg(f)\n",
    "df_movie_summary.index = df_movie_summary.index.map(int)\n",
    "movie_benchmark = round(df_movie_summary['count'].quantile(0.0),0)\n",
    "drop_movie_list = df_movie_summary[df_movie_summary['count'] < movie_benchmark].index\n",
    "\n",
    "print('Movie minimum times of review: {}'.format(movie_benchmark))\n",
    "\n",
    "df_cust_summary = df.groupby('Cust_Id')['Rating'].agg(f)\n",
    "df_cust_summary.index = df_cust_summary.index.map(int)\n",
    "cust_benchmark = round(df_cust_summary['count'].quantile(0.0),0)\n",
    "drop_cust_list = df_cust_summary[df_cust_summary['count'] < cust_benchmark].index\n",
    "\n",
    "print('Customer minimum times of review: {}'.format(cust_benchmark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shape: (100836, 3)\n",
      "After Trim Shape: (100836, 3)\n",
      "-Data Examples-\n",
      "        Cust_Id  Rating  Movie_Id\n",
      "0             1     4.0         1\n",
      "1             1     4.0         3\n",
      "2             1     4.0         6\n",
      "3             1     5.0        47\n",
      "4             1     5.0        50\n",
      "5             1     3.0        70\n",
      "6             1     5.0       101\n",
      "7             1     4.0       110\n",
      "8             1     5.0       151\n",
      "9             1     5.0       157\n",
      "10            1     5.0       163\n",
      "11            1     5.0       216\n",
      "12            1     3.0       223\n",
      "13            1     5.0       231\n",
      "14            1     4.0       235\n",
      "15            1     5.0       260\n",
      "16            1     3.0       296\n",
      "17            1     3.0       316\n",
      "18            1     5.0       333\n",
      "19            1     4.0       349\n",
      "20            1     4.0       356\n",
      "21            1     5.0       362\n",
      "22            1     4.0       367\n",
      "23            1     3.0       423\n",
      "24            1     4.0       441\n",
      "25            1     5.0       457\n",
      "26            1     4.0       480\n",
      "27            1     3.0       500\n",
      "28            1     5.0       527\n",
      "29            1     4.0       543\n",
      "...         ...     ...       ...\n",
      "100806      610     3.0    150401\n",
      "100807      610     4.0    152077\n",
      "100808      610     4.0    152081\n",
      "100809      610     3.5    152372\n",
      "100810      610     3.5    155064\n",
      "100811      610     5.0    156371\n",
      "100812      610     4.5    156726\n",
      "100813      610     4.0    157296\n",
      "100814      610     5.0    158238\n",
      "100815      610     3.5    158721\n",
      "100816      610     3.5    158872\n",
      "100817      610     3.0    158956\n",
      "100818      610     3.0    159093\n",
      "100819      610     3.0    160080\n",
      "100820      610     2.5    160341\n",
      "100821      610     4.5    160527\n",
      "100822      610     3.0    160571\n",
      "100823      610     3.0    160836\n",
      "100824      610     4.0    161582\n",
      "100825      610     4.0    161634\n",
      "100826      610     3.5    162350\n",
      "100827      610     3.5    163937\n",
      "100828      610     3.5    163981\n",
      "100829      610     5.0    164179\n",
      "100830      610     4.0    166528\n",
      "100831      610     4.0    166534\n",
      "100832      610     5.0    168248\n",
      "100833      610     5.0    168250\n",
      "100834      610     5.0    168252\n",
      "100835      610     3.0    170875\n",
      "\n",
      "[100836 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print('Original Shape: {}'.format(df.shape))\n",
    "df = df[~df['Movie_Id'].isin(drop_movie_list)]\n",
    "df = df[~df['Cust_Id'].isin(drop_cust_list)]\n",
    "print('After Trim Shape: {}'.format(df.shape))\n",
    "print('-Data Examples-')\n",
    "print(df.iloc[::1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(610, 9724)\n"
     ]
    }
   ],
   "source": [
    "df_p = pd.pivot_table(df,values='Rating',index='Cust_Id',columns='Movie_Id')\n",
    "\n",
    "print(df_p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                       Name\n",
      "Movie_Id                                                   \n",
      "1                                                Toy Story \n",
      "2                                            Jumanji (1995)\n",
      "3                                   Grumpier Old Men (1995)\n",
      "4                                  Waiting to Exhale (1995)\n",
      "5                        Father of the Bride Part II (1995)\n",
      "6                                               Heat (1995)\n",
      "7                                            Sabrina (1995)\n",
      "8                                       Tom and Huck (1995)\n",
      "9                                       Sudden Death (1995)\n",
      "10                                         GoldenEye (1995)\n",
      "11                           American President, The (1995)\n",
      "12                       Dracula: Dead and Loving It (1995)\n",
      "13                                             Balto (1995)\n",
      "14                                             Nixon (1995)\n",
      "15                                  Cutthroat Island (1995)\n",
      "16                                            Casino (1995)\n",
      "17                             Sense and Sensibility (1995)\n",
      "18                                        Four Rooms (1995)\n",
      "19                    Ace Ventura: When Nature Calls (1995)\n",
      "20                                       Money Train (1995)\n",
      "21                                        Get Shorty (1995)\n",
      "22                                           Copycat (1995)\n",
      "23                                         Assassins (1995)\n",
      "24                                            Powder (1995)\n",
      "25                                 Leaving Las Vegas (1995)\n",
      "26                                           Othello (1995)\n",
      "27                                      Now and Then (1995)\n",
      "28                                        Persuasion (1995)\n",
      "29        City of Lost Children, The (CitÃ© des enfants ...\n",
      "30        Shanghai Triad (Yao a yao yao dao waipo qiao) ...\n",
      "...                                                     ...\n",
      "78                               Crossing Guard, The (1995)\n",
      "79                                        Juror, The (1996)\n",
      "80              White Balloon, The (Badkonake sefid) (1995)\n",
      "81           Things to Do in Denver When You're Dead (1995)\n",
      "82                          Antonia's Line (Antonia) (1995)\n",
      "83          Once Upon a Time... When We Were Colored (1995)\n",
      "85                                Angels and Insects (1995)\n",
      "86                                      White Squall (1996)\n",
      "87                                 Dunston Checks In (1996)\n",
      "88                                       Black Sheep (1996)\n",
      "89                                      Nick of Time (1995)\n",
      "92                                       Mary Reilly (1996)\n",
      "93                               Vampire in Brooklyn (1995)\n",
      "94                                   Beautiful Girls (1996)\n",
      "95                                      Broken Arrow (1996)\n",
      "96                            In the Bleak Midwinter (1995)\n",
      "97                                  Hate (Haine, La) (1995)\n",
      "99                     Heidi Fleiss: Hollywood Madam (1995)\n",
      "100                                        City Hall (1996)\n",
      "101                                    Bottle Rocket (1996)\n",
      "102                                        Mr. Wrong (1996)\n",
      "103                                    Unforgettable (1996)\n",
      "104                                    Happy Gilmore (1996)\n",
      "105                   Bridges of Madison County, The (1995)\n",
      "106              Nobody Loves Me (Keiner liebt mich) (1994)\n",
      "107                           Muppet Treasure Island (1996)\n",
      "108                                          Catwalk (1996)\n",
      "110                                       Braveheart (1995)\n",
      "111                                      Taxi Driver (1976)\n",
      "112              Rumble in the Bronx (Hont faan kui) (1995)\n",
      "\n",
      "[100 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df_title = pd.read_csv(r'C:\\Users\\HP\\Desktop\\BE data\\data\\mlmoviestest.csv', encoding = \"ISO-8859-1\", header = None, names = ['Movie_Id', 'Name'], usecols = [0,1])\n",
    "df_title.set_index('Movie_Id', inplace = True)\n",
    "print (df_title.head(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(610, 9724)\n"
     ]
    }
   ],
   "source": [
    "df = df1\n",
    "df_p = pd.pivot_table(df,values='Rating',index='Cust_Id',columns='Movie_Id')\n",
    "\n",
    "print(df_p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie_Id  1       2       3       4       5       6       7       8       \\\n",
      "Cust_Id                                                                    \n",
      "1            4.0     NaN     4.0     NaN     NaN     4.0     NaN     NaN   \n",
      "2            NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "3            NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "4            NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "5            4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "6            NaN     4.0     5.0     3.0     5.0     4.0     4.0     3.0   \n",
      "7            4.5     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "8            NaN     4.0     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "9            NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "10           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "11           NaN     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
      "12           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "13           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "14           NaN     NaN     NaN     3.0     NaN     NaN     3.0     NaN   \n",
      "15           2.5     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "16           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "17           4.5     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "18           3.5     3.0     NaN     NaN     NaN     4.0     NaN     NaN   \n",
      "19           4.0     3.0     3.0     NaN     NaN     NaN     2.0     NaN   \n",
      "20           NaN     3.0     NaN     NaN     NaN     NaN     NaN     1.0   \n",
      "21           3.5     3.5     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "22           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "23           NaN     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
      "24           NaN     NaN     NaN     NaN     NaN     4.5     NaN     NaN   \n",
      "25           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "26           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "27           3.0     4.0     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "28           NaN     NaN     NaN     NaN     NaN     3.5     NaN     NaN   \n",
      "29           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "30           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "...          ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "581          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "582          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "583          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "584          5.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "585          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "586          NaN     4.0     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "587          5.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "588          NaN     NaN     3.0     NaN     NaN     5.0     NaN     NaN   \n",
      "589          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "590          4.0     2.5     3.0     NaN     2.0     3.5     NaN     NaN   \n",
      "591          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "592          NaN     4.0     NaN     NaN     NaN     3.0     NaN     NaN   \n",
      "593          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "594          NaN     4.0     4.0     NaN     NaN     NaN     NaN     NaN   \n",
      "595          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "596          4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "597          4.0     NaN     NaN     NaN     NaN     3.0     1.0     NaN   \n",
      "598          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "599          3.0     2.5     1.5     NaN     NaN     4.5     2.5     NaN   \n",
      "600          2.5     4.0     NaN     1.5     2.5     NaN     3.5     NaN   \n",
      "601          4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "602          NaN     4.0     NaN     NaN     NaN     3.0     NaN     NaN   \n",
      "603          4.0     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
      "604          3.0     5.0     NaN     NaN     3.0     3.0     NaN     NaN   \n",
      "605          4.0     3.5     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "606          2.5     NaN     NaN     NaN     NaN     NaN     2.5     NaN   \n",
      "607          4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "608          2.5     2.0     2.0     NaN     NaN     NaN     NaN     NaN   \n",
      "609          3.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "610          5.0     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
      "\n",
      "Movie_Id  9       10       ...    193565  193567  193571  193573  193579  \\\n",
      "Cust_Id                    ...                                             \n",
      "1            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "2            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "3            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "4            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "5            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "6            NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "7            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "8            NaN     2.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "9            NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "10           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "11           NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "12           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "13           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "14           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "15           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "16           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "17           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "18           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "19           NaN     2.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "20           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "21           NaN     5.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "22           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "23           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "24           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "25           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "26           NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "27           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "28           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "29           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "30           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "...          ...     ...   ...       ...     ...     ...     ...     ...   \n",
      "581          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "582          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "583          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "584          NaN     5.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "585          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "586          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "587          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "588          NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "589          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "590          NaN     3.5   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "591          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "592          NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "593          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "594          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "595          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "596          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "597          NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "598          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "599          1.5     3.5   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "600          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "601          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "602          NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "603          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "604          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "605          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "606          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "607          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "608          NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "609          NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "610          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
      "\n",
      "Movie_Id  193581  193583  193585  193587  193609  \n",
      "Cust_Id                                           \n",
      "1            NaN     NaN     NaN     NaN     NaN  \n",
      "2            NaN     NaN     NaN     NaN     NaN  \n",
      "3            NaN     NaN     NaN     NaN     NaN  \n",
      "4            NaN     NaN     NaN     NaN     NaN  \n",
      "5            NaN     NaN     NaN     NaN     NaN  \n",
      "6            NaN     NaN     NaN     NaN     NaN  \n",
      "7            NaN     NaN     NaN     NaN     NaN  \n",
      "8            NaN     NaN     NaN     NaN     NaN  \n",
      "9            NaN     NaN     NaN     NaN     NaN  \n",
      "10           NaN     NaN     NaN     NaN     NaN  \n",
      "11           NaN     NaN     NaN     NaN     NaN  \n",
      "12           NaN     NaN     NaN     NaN     NaN  \n",
      "13           NaN     NaN     NaN     NaN     NaN  \n",
      "14           NaN     NaN     NaN     NaN     NaN  \n",
      "15           NaN     NaN     NaN     NaN     NaN  \n",
      "16           NaN     NaN     NaN     NaN     NaN  \n",
      "17           NaN     NaN     NaN     NaN     NaN  \n",
      "18           NaN     NaN     NaN     NaN     NaN  \n",
      "19           NaN     NaN     NaN     NaN     NaN  \n",
      "20           NaN     NaN     NaN     NaN     NaN  \n",
      "21           NaN     NaN     NaN     NaN     NaN  \n",
      "22           NaN     NaN     NaN     NaN     NaN  \n",
      "23           NaN     NaN     NaN     NaN     NaN  \n",
      "24           NaN     NaN     NaN     NaN     NaN  \n",
      "25           NaN     NaN     NaN     NaN     NaN  \n",
      "26           NaN     NaN     NaN     NaN     NaN  \n",
      "27           NaN     NaN     NaN     NaN     NaN  \n",
      "28           NaN     NaN     NaN     NaN     NaN  \n",
      "29           NaN     NaN     NaN     NaN     NaN  \n",
      "30           NaN     NaN     NaN     NaN     NaN  \n",
      "...          ...     ...     ...     ...     ...  \n",
      "581          NaN     NaN     NaN     NaN     NaN  \n",
      "582          NaN     NaN     NaN     NaN     NaN  \n",
      "583          NaN     NaN     NaN     NaN     NaN  \n",
      "584          NaN     NaN     NaN     NaN     NaN  \n",
      "585          NaN     NaN     NaN     NaN     NaN  \n",
      "586          NaN     NaN     NaN     NaN     NaN  \n",
      "587          NaN     NaN     NaN     NaN     NaN  \n",
      "588          NaN     NaN     NaN     NaN     NaN  \n",
      "589          NaN     NaN     NaN     NaN     NaN  \n",
      "590          NaN     NaN     NaN     NaN     NaN  \n",
      "591          NaN     NaN     NaN     NaN     NaN  \n",
      "592          NaN     NaN     NaN     NaN     NaN  \n",
      "593          NaN     NaN     NaN     NaN     NaN  \n",
      "594          NaN     NaN     NaN     NaN     NaN  \n",
      "595          NaN     NaN     NaN     NaN     NaN  \n",
      "596          NaN     NaN     NaN     NaN     NaN  \n",
      "597          NaN     NaN     NaN     NaN     NaN  \n",
      "598          NaN     NaN     NaN     NaN     NaN  \n",
      "599          NaN     NaN     NaN     NaN     NaN  \n",
      "600          NaN     NaN     NaN     NaN     NaN  \n",
      "601          NaN     NaN     NaN     NaN     NaN  \n",
      "602          NaN     NaN     NaN     NaN     NaN  \n",
      "603          NaN     NaN     NaN     NaN     NaN  \n",
      "604          NaN     NaN     NaN     NaN     NaN  \n",
      "605          NaN     NaN     NaN     NaN     NaN  \n",
      "606          NaN     NaN     NaN     NaN     NaN  \n",
      "607          NaN     NaN     NaN     NaN     NaN  \n",
      "608          NaN     NaN     NaN     NaN     NaN  \n",
      "609          NaN     NaN     NaN     NaN     NaN  \n",
      "610          NaN     NaN     NaN     NaN     NaN  \n",
      "\n",
      "[610 rows x 9724 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_data = pd.merge(df, df_title, on='Movie_Id')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating  Movie_Id        Name\n",
       "0        1     4.0         1  Toy Story \n",
       "1        5     4.0         1  Toy Story \n",
       "2        7     4.5         1  Toy Story \n",
       "3       15     2.5         1  Toy Story \n",
       "4       17     4.5         1  Toy Story "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "'71 (2014)                                 4.0\n",
       "'Hellboy': The Seeds of Creation (2004)    4.0\n",
       "'Round Midnight (1986)                     3.5\n",
       "'Salem's Lot (2004)                        5.0\n",
       "'Til There Was You (1997)                  4.0\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('Name')['Rating'].mean().head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Karlson Returns (1970)                           5.0\n",
       "Winter in Prostokvashino (1984)                  5.0\n",
       "My Love (2006)                                   5.0\n",
       "Sorority House Massacre II (1990)                5.0\n",
       "Winnie the Pooh and the Day of Concern (1972)    5.0\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('Name')['Rating'].mean().sort_values(ascending=False).head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Forrest Gump (1994)                 329\n",
       "Shawshank Redemption, The (1994)    317\n",
       "Pulp Fiction (1994)                 307\n",
       "Silence of the Lambs, The (1991)    279\n",
       "Matrix, The (1999)                  278\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.groupby('Name')['Rating'].count().sort_values(ascending=False).head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean_count = pd.DataFrame(movie_data.groupby('Name')['Rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean_count['rating_counts'] = pd.DataFrame(movie_data.groupby('Name')['Rating'].count()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
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       "      <th>'Salem's Lot (2004)</th>\n",
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      "text/plain": [
       "                                         Rating  rating_counts\n",
       "Name                                                          \n",
       "'71 (2014)                                  4.0              1\n",
       "'Hellboy': The Seeds of Creation (2004)     4.0              1\n",
       "'Round Midnight (1986)                      3.5              2\n",
       "'Salem's Lot (2004)                         5.0              1\n",
       "'Til There Was You (1997)                   4.0              2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    "ratings_mean_count.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "scrolled": true
   },
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    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 9719 columns</p>\n",
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      ],
      "text/plain": [
       "Name     '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "Cust_Id                                                        \n",
       "1               NaN                                      NaN   \n",
       "2               NaN                                      NaN   \n",
       "3               NaN                                      NaN   \n",
       "4               NaN                                      NaN   \n",
       "5               NaN                                      NaN   \n",
       "\n",
       "Name     'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "Cust_Id                                                \n",
       "1                           NaN                  NaN   \n",
       "2                           NaN                  NaN   \n",
       "3                           NaN                  NaN   \n",
       "4                           NaN                  NaN   \n",
       "5                           NaN                  NaN   \n",
       "\n",
       "Name     'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "Cust_Id                                                               \n",
       "1                              NaN                              NaN   \n",
       "2                              NaN                              NaN   \n",
       "3                              NaN                              NaN   \n",
       "4                              NaN                              NaN   \n",
       "5                              NaN                              NaN   \n",
       "\n",
       "Name     'burbs, The (1989)  'night Mother (1986)  \\\n",
       "Cust_Id                                             \n",
       "1                       NaN                   NaN   \n",
       "2                       NaN                   NaN   \n",
       "3                       NaN                   NaN   \n",
       "4                       NaN                   NaN   \n",
       "5                       NaN                   NaN   \n",
       "\n",
       "Name     (500) Days of Summer (2009)  *batteries not included (1987)  \\\n",
       "Cust_Id                                                                \n",
       "1                                NaN                             NaN   \n",
       "2                                NaN                             NaN   \n",
       "3                                NaN                             NaN   \n",
       "4                                NaN                             NaN   \n",
       "5                                NaN                             NaN   \n",
       "\n",
       "Name                        ...                       Zulu (2013)  \\\n",
       "Cust_Id                     ...                                     \n",
       "1                           ...                               NaN   \n",
       "2                           ...                               NaN   \n",
       "3                           ...                               NaN   \n",
       "4                           ...                               NaN   \n",
       "5                           ...                               NaN   \n",
       "\n",
       "Name     [REC] (2007)  [REC]Â² (2009)  [REC]Â³ 3 GÃ©nesis (2012)  \\\n",
       "Cust_Id                                                            \n",
       "1                 NaN             NaN                        NaN   \n",
       "2                 NaN             NaN                        NaN   \n",
       "3                 NaN             NaN                        NaN   \n",
       "4                 NaN             NaN                        NaN   \n",
       "5                 NaN             NaN                        NaN   \n",
       "\n",
       "Name     anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "Cust_Id                                                           \n",
       "1                                                      NaN        \n",
       "2                                                      NaN        \n",
       "3                                                      NaN        \n",
       "4                                                      NaN        \n",
       "5                                                      NaN        \n",
       "\n",
       "Name     eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "Cust_Id                                                                \n",
       "1                    NaN         NaN                             NaN   \n",
       "2                    NaN         NaN                             NaN   \n",
       "3                    NaN         NaN                             NaN   \n",
       "4                    NaN         NaN                             NaN   \n",
       "5                    NaN         NaN                             NaN   \n",
       "\n",
       "Name     Â¡Three Amigos! (1986)  Ã nous la libertÃ© (Freedom for Us) (1931)  \n",
       "Cust_Id                                                                       \n",
       "1                           4.0                                          NaN  \n",
       "2                           NaN                                          NaN  \n",
       "3                           NaN                                          NaN  \n",
       "4                           NaN                                          NaN  \n",
       "5                           NaN                                          NaN  \n",
       "\n",
       "[5 rows x 9719 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_rating = movie_data.pivot_table(index='Cust_Id', columns='Name', values='Rating')  \n",
    "user_movie_rating.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "after_hours_ratings = user_movie_rating['After Hours (1985)']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust_Id\n",
       "1   NaN\n",
       "2   NaN\n",
       "3   NaN\n",
       "4   NaN\n",
       "5   NaN\n",
       "Name: After Hours (1985), dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after_hours_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>AlienÂ³ (a.k.a. Alien 3) (1992)</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All About Eve (1950)</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All the President's Men (1976)</th>\n",
       "      <td>0.080064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almost Famous (2000)</th>\n",
       "      <td>0.458831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amadeus (1984)</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amazing Spider-Man, The (2012)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amelie (Fabuleux destin d'AmÃ©lie Poulain, Le) (2001)</th>\n",
       "      <td>-0.392357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America's Sweethearts (2001)</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Beauty (1999)</th>\n",
       "      <td>-0.910182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American History X (1998)</th>\n",
       "      <td>0.944911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Hustle (2013)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Pie (1999)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Pie 2 (2001)</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Splendor (2003)</th>\n",
       "      <td>0.464140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amistad (1997)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anchorman: The Legend of Ron Burgundy (2004)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anger Management (2003)</th>\n",
       "      <td>0.277350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal House (1978)</th>\n",
       "      <td>0.526152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animatrix, The (2003)</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation\n",
       "Name                                                           \n",
       "(500) Days of Summer (2009)                            0.000000\n",
       "10 Things I Hate About You (1999)                      0.188982\n",
       "12 Angry Men (1957)                                    1.000000\n",
       "13 Assassins (JÃ»san-nin no shikaku) (2010)            1.000000\n",
       "2001: A Space Odyssey (1968)                          -0.628619\n",
       "24 Hour Party People (2002)                            1.000000\n",
       "25th Hour (2002)                                       1.000000\n",
       "28 Days Later (2002)                                   0.866025\n",
       "30 Days of Night (2007)                                1.000000\n",
       "300 (2007)                                             0.577350\n",
       "40-Year-Old Virgin, The (2005)                         0.925820\n",
       "48 Hrs. (1982)                                         1.000000\n",
       "A Million Ways to Die in the West (2014)               1.000000\n",
       "A.I. Artificial Intelligence (2001)                    1.000000\n",
       "About a Boy (2002)                                     0.927173\n",
       "Abyss, The (1989)                                      0.500000\n",
       "Ace Ventura: Pet Detective (1994)                      0.097590\n",
       "Ace Ventura: When Nature Calls (1995)                 -1.000000\n",
       "Adaptation (2002)                                      0.784465\n",
       "Addams Family Values (1993)                            1.000000\n",
       "Adventureland (2009)                                   1.000000\n",
       "Adventures of Priscilla, Queen of the Desert, T...    -1.000000\n",
       "After Hours (1985)                                     1.000000\n",
       "Age of Innocence, The (1993)                          -1.000000\n",
       "Airplane! (1980)                                       0.500000\n",
       "Akira (1988)                                          -0.500000\n",
       "Aladdin (1992)                                         1.000000\n",
       "Alice Doesn't Live Here Anymore (1974)                -1.000000\n",
       "Alien (1979)                                          -0.388889\n",
       "Alien: Resurrection (1997)                            -1.000000\n",
       "Aliens (1986)                                         -0.372678\n",
       "AlienÂ³ (a.k.a. Alien 3) (1992)                       -1.000000\n",
       "All About Eve (1950)                                  -1.000000\n",
       "All the President's Men (1976)                         0.080064\n",
       "Almost Famous (2000)                                   0.458831\n",
       "Amadeus (1984)                                         0.000000\n",
       "Amazing Spider-Man, The (2012)                         1.000000\n",
       "Amelie (Fabuleux destin d'AmÃ©lie Poulain, Le) ...    -0.392357\n",
       "America's Sweethearts (2001)                          -1.000000\n",
       "American Beauty (1999)                                -0.910182\n",
       "American History X (1998)                              0.944911\n",
       "American Hustle (2013)                                 1.000000\n",
       "American Pie (1999)                                    1.000000\n",
       "American Pie 2 (2001)                                  0.500000\n",
       "American Splendor (2003)                               0.464140\n",
       "Amistad (1997)                                         1.000000\n",
       "Anchorman: The Legend of Ron Burgundy (2004)           1.000000\n",
       "Anger Management (2003)                                0.277350\n",
       "Animal House (1978)                                    0.526152\n",
       "Animatrix, The (2003)                                 -1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_like_after_hours = user_movie_rating.corrwith(after_hours_ratings)\n",
    "\n",
    "corr_after_hours = pd.DataFrame(movies_like_after_hours, columns=['Correlation'])  \n",
    "corr_after_hours.dropna(inplace=True)  \n",
    "corr_after_hours.head(50)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_after_hours.to_csv(r'C:\\Users\\HP\\Desktop\\BE data\\mlop.csv', sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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