Correlation Based Recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading files\n",
    "data = pd.read_csv('listing.csv', encoding = 'latin-1')\n",
    "books = pd.read_csv('books.csv', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>no_of_ratings</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4833</td>\n",
       "      <td>4.25</td>\n",
       "      <td>7156.0</td>\n",
       "      <td>3466</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>590</td>\n",
       "      <td>4.31</td>\n",
       "      <td>7821.0</td>\n",
       "      <td>3466</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>3361</td>\n",
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       "      <td>1245.0</td>\n",
       "      <td>3453</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4535</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>3453</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   book_id  avg_rating  no_of_ratings  user_id  user_rating\n",
       "0     4833        4.25         7156.0     3466            0\n",
       "1      590        4.31         7821.0     3466            5\n",
       "2     4264        4.08         3836.0     3453            5\n",
       "3     3361        3.52         1245.0     3453            4\n",
       "4     4535        4.13         3107.0     3453            0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Checking the data using head function \n",
    "books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>953.0</td>\n",
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       "      <td>2012.0</td>\n",
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       "      <td>118.0</td>\n",
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      "text/plain": [
       "         no_of_ratings\n",
       "book_id               \n",
       "6                953.0\n",
       "7               2012.0\n",
       "9                172.0\n",
       "15               118.0\n",
       "21              3916.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# calculating the mean\n",
    "rating = pd.DataFrame(books.groupby('book_id')['no_of_ratings'].mean())\n",
    "rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>no_of_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>708.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2040.384181</td>\n",
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       "      <td>2666.449960</td>\n",
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       "      <th>50%</th>\n",
       "      <td>629.000000</td>\n",
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      "text/plain": [
       "       no_of_ratings\n",
       "count     708.000000\n",
       "mean     2040.384181\n",
       "std      2666.449960\n",
       "min         0.000000\n",
       "25%        90.750000\n",
       "50%       629.000000\n",
       "75%      3225.250000\n",
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     "execution_count": 5,
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   "source": [
    "# getting the description of rating\n",
    "rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9754.0</td>\n",
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       "      <th>1616</th>\n",
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      "text/plain": [
       "         no_of_ratings\n",
       "book_id               \n",
       "4755            9936.0\n",
       "2409            9768.0\n",
       "4696            9754.0\n",
       "2194            9754.0\n",
       "1616            9542.0"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "# sorting based on no of ratings that each book got\n",
    "rating.sort_values('no_of_ratings', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "book_id  6     7     9     15    21    29    43    45    47    61    ...   \\\n",
       "user_id                                                              ...    \n",
       "117       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "176       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0   NaN  ...    \n",
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       "295       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "318       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "book_id  4931  4941  4942  4968  4971  4975  4978  4991  4995  4999  \n",
       "user_id                                                              \n",
       "117       NaN   NaN   NaN   NaN   NaN   NaN   4.0   NaN   2.0   NaN  \n",
       "176       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "232       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "295       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "318       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 710 columns]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing data table for analysis\n",
    "user_rating= pd.pivot_table(data=books, values='user_rating', index='user_id', columns='book_id')\n",
    "user_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>6</th>\n",
       "      <td>1.0</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>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</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>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</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>15</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>21</th>\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>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>29</th>\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>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>43</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>1.0</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>45</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.944911</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>47</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>61</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>10 rows × 710 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "book_id  6     7     9     15    21    29    43    45    47    61    ...   \\\n",
       "book_id                                                              ...    \n",
       "6         1.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "7         NaN   1.0   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN  ...    \n",
       "9         NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "15        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "21        NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "29        NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN  ...    \n",
       "43        NaN   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN  ...    \n",
       "45        NaN   1.0   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN  ...    \n",
       "47        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   1.0   NaN  ...    \n",
       "61        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "book_id  4931  4941  4942  4968      4971  4975  4978  4991  4995  4999  \n",
       "book_id                                                                  \n",
       "6         NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "7         NaN   NaN   NaN   NaN  1.000000   NaN   NaN   NaN   NaN   NaN  \n",
       "9         NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "15        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "21        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "29        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "43        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "45        NaN   NaN   NaN   NaN  0.944911   NaN   NaN   NaN   NaN   NaN  \n",
       "47        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "61        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[10 rows x 710 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix  = user_rating.corr(method='pearson')\n",
    "correlation_matrix.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "577     2.0\n",
       "3472    4.0\n",
       "3476    2.0\n",
       "3483    3.0\n",
       "Name: 4755, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the users who rated this particular book and making sure rating is not zero\n",
    "OneManOut_rating = user_rating[4755]\n",
    "OneManOut_rating[OneManOut_rating>=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\numpy\\lib\\function_base.py:3154: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\numpy\\lib\\function_base.py:3088: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  c *= 1. / np.float64(fact)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PearsonR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.981981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PearsonR\n",
       "book_id          \n",
       "9       -0.981981\n",
       "481     -1.000000\n",
       "493      1.000000\n",
       "535     -1.000000\n",
       "755      1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finidng similarity using Pearson correlation\n",
    "similar_to_OneManOut = user_rating.corrwith(OneManOut_rating)\n",
    "\n",
    "corr_OneManOut = pd.DataFrame(similar_to_OneManOut, columns=['PearsonR'])\n",
    "corr_OneManOut.dropna(inplace=True)\n",
    "corr_OneManOut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>PearsonR</th>\n",
       "      <th>no_of_ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2629</th>\n",
       "      <td>1.0</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1.0</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4571</th>\n",
       "      <td>1.0</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2027.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>1.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2380.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PearsonR  no_of_ratings\n",
       "book_id                         \n",
       "2629          1.0          635.0\n",
       "493           1.0          211.0\n",
       "4755          1.0         9936.0\n",
       "4571          1.0          323.0\n",
       "2900          1.0         2027.0\n",
       "1417          1.0           66.0\n",
       "2681          1.0           65.0\n",
       "1676          1.0         5374.0\n",
       "2913          1.0          160.0\n",
       "1431          1.0         2380.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "OneManOut_corr_summary = corr_OneManOut.join(rating)\n",
    "# getting the most similar book\n",
    "OneManOut_corr_summary.sort_values('PearsonR', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>book_id</th>\n",
       "      <th>genre</th>\n",
       "      <th>name</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2629</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Moroccan Arabic: Lonely Planet Phrasebook</td>\n",
       "      <td>Dan Bacon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>493</td>\n",
       "      <td>Health, Fitness &amp; Dieting</td>\n",
       "      <td>The Gene Makeover: The 21st Century Anti-Aging...</td>\n",
       "      <td>Vincent C. Giampapa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4755</td>\n",
       "      <td>Law</td>\n",
       "      <td>One Man Out: Curt Flood versus Baseball (Landm...</td>\n",
       "      <td>Robert M. Goldman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4571</td>\n",
       "      <td>Teen &amp; Young Adult</td>\n",
       "      <td>The Story of the Tampa Bay Buccaneers (NFL Tod...</td>\n",
       "      <td>Sara Gilbert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2900</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Robert Adams: The New West</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1417</td>\n",
       "      <td>Test Preparation</td>\n",
       "      <td>GED Exercise Books: Student Workbook Language ...</td>\n",
       "      <td>STECK-VAUGHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2681</td>\n",
       "      <td>Arts &amp; Photography</td>\n",
       "      <td>The Industrial Design Reader</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1676</td>\n",
       "      <td>Science &amp; Math</td>\n",
       "      <td>Rare Earth Minerals: Chemistry, Origin and Ore...</td>\n",
       "      <td>A.P. Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2913</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Avant-Guide Las Vegas: Insiders' Guide to Prog...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1431</td>\n",
       "      <td>Health, Fitness &amp; Dieting</td>\n",
       "      <td>Linchpin: Are You Indispensable?</td>\n",
       "      <td>Seth Godin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id                      genre  \\\n",
       "0     2629                     Travel   \n",
       "1      493  Health, Fitness & Dieting   \n",
       "2     4755                        Law   \n",
       "3     4571         Teen & Young Adult   \n",
       "4     2900                     Travel   \n",
       "5     1417           Test Preparation   \n",
       "6     2681         Arts & Photography   \n",
       "7     1676             Science & Math   \n",
       "8     2913                     Travel   \n",
       "9     1431  Health, Fitness & Dieting   \n",
       "\n",
       "                                                name               author  \n",
       "0          Moroccan Arabic: Lonely Planet Phrasebook            Dan Bacon  \n",
       "1  The Gene Makeover: The 21st Century Anti-Aging...  Vincent C. Giampapa  \n",
       "2  One Man Out: Curt Flood versus Baseball (Landm...    Robert M. Goldman  \n",
       "3  The Story of the Tampa Bay Buccaneers (NFL Tod...         Sara Gilbert  \n",
       "4                         Robert Adams: The New West                  NaN  \n",
       "5  GED Exercise Books: Student Workbook Language ...         STECK-VAUGHN  \n",
       "6                       The Industrial Design Reader                  NaN  \n",
       "7  Rare Earth Minerals: Chemistry, Origin and Ore...           A.P. Jones  \n",
       "8  Avant-Guide Las Vegas: Insiders' Guide to Prog...                  NaN  \n",
       "9                   Linchpin: Are You Indispensable?           Seth Godin  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the details for most similar books\n",
    "book_corr_OneManOut = pd.DataFrame([2629, 493, 4755, 4571, 2900, 1417, 2681, 1676, 2913, 1431], index = np.arange(10), columns=['book_id'])\n",
    "detail = pd.merge(book_corr_OneManOut, data,on='book_id')\n",
    "detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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