book-recommendation-system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Book Recommender System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Dataset: https://www.kaggle.com/zygmunt/goodbooks-10k "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1] Importing required libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " -----------------------------------------------------------------------------------------------------------------------\n",
    " \n",
    " \n",
    " **The data used for this task consist of 5 different datasets which are loaded and presented as below :**\n",
    " \n",
    "  1) **books.csv** - Books contains all the information about the rated books, including author, title, book ID, publication year, average rating, etc.\n",
    "  \n",
    " 2) **ratings.csv** - Ratings includes all the rates given by our selected group of users to the books they have chosen to rate.\n",
    " \n",
    " 3) **book_tags.csv** - Book Tags contains all the tags associated to each and every book included in the analysis.\n",
    " \n",
    " 4) **tags.csv** - Tags collects all the tags included in 'bookTags' and explains their meaning.\n",
    " \n",
    " 5) **to_read.csv** - to_read indicates all the books that have been flagged as 'to read' by the readers included in the analysis.\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>best_book_id</th>\n",
       "      <th>work_id</th>\n",
       "      <th>books_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>authors</th>\n",
       "      <th>original_publication_year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_ratings_count</th>\n",
       "      <th>work_text_reviews_count</th>\n",
       "      <th>ratings_1</th>\n",
       "      <th>ratings_2</th>\n",
       "      <th>ratings_3</th>\n",
       "      <th>ratings_4</th>\n",
       "      <th>ratings_5</th>\n",
       "      <th>image_url</th>\n",
       "      <th>small_image_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2792775</td>\n",
       "      <td>272</td>\n",
       "      <td>439023483</td>\n",
       "      <td>9.780439e+12</td>\n",
       "      <td>Suzanne Collins</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>The Hunger Games</td>\n",
       "      <td>...</td>\n",
       "      <td>4780653</td>\n",
       "      <td>4942365</td>\n",
       "      <td>155254</td>\n",
       "      <td>66715</td>\n",
       "      <td>127936</td>\n",
       "      <td>560092</td>\n",
       "      <td>1481305</td>\n",
       "      <td>2706317</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4640799</td>\n",
       "      <td>491</td>\n",
       "      <td>439554934</td>\n",
       "      <td>9.780440e+12</td>\n",
       "      <td>J.K. Rowling, Mary GrandPré</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "      <td>...</td>\n",
       "      <td>4602479</td>\n",
       "      <td>4800065</td>\n",
       "      <td>75867</td>\n",
       "      <td>75504</td>\n",
       "      <td>101676</td>\n",
       "      <td>455024</td>\n",
       "      <td>1156318</td>\n",
       "      <td>3011543</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>41865</td>\n",
       "      <td>41865</td>\n",
       "      <td>3212258</td>\n",
       "      <td>226</td>\n",
       "      <td>316015849</td>\n",
       "      <td>9.780316e+12</td>\n",
       "      <td>Stephenie Meyer</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Twilight</td>\n",
       "      <td>...</td>\n",
       "      <td>3866839</td>\n",
       "      <td>3916824</td>\n",
       "      <td>95009</td>\n",
       "      <td>456191</td>\n",
       "      <td>436802</td>\n",
       "      <td>793319</td>\n",
       "      <td>875073</td>\n",
       "      <td>1355439</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2657</td>\n",
       "      <td>2657</td>\n",
       "      <td>3275794</td>\n",
       "      <td>487</td>\n",
       "      <td>61120081</td>\n",
       "      <td>9.780061e+12</td>\n",
       "      <td>Harper Lee</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "      <td>...</td>\n",
       "      <td>3198671</td>\n",
       "      <td>3340896</td>\n",
       "      <td>72586</td>\n",
       "      <td>60427</td>\n",
       "      <td>117415</td>\n",
       "      <td>446835</td>\n",
       "      <td>1001952</td>\n",
       "      <td>1714267</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4671</td>\n",
       "      <td>4671</td>\n",
       "      <td>245494</td>\n",
       "      <td>1356</td>\n",
       "      <td>743273567</td>\n",
       "      <td>9.780743e+12</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>The Great Gatsby</td>\n",
       "      <td>...</td>\n",
       "      <td>2683664</td>\n",
       "      <td>2773745</td>\n",
       "      <td>51992</td>\n",
       "      <td>86236</td>\n",
       "      <td>197621</td>\n",
       "      <td>606158</td>\n",
       "      <td>936012</td>\n",
       "      <td>947718</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  book_id  best_book_id  work_id  books_count       isbn        isbn13  \\\n",
       "0   1  2767052       2767052  2792775          272  439023483  9.780439e+12   \n",
       "1   2        3             3  4640799          491  439554934  9.780440e+12   \n",
       "2   3    41865         41865  3212258          226  316015849  9.780316e+12   \n",
       "3   4     2657          2657  3275794          487   61120081  9.780061e+12   \n",
       "4   5     4671          4671   245494         1356  743273567  9.780743e+12   \n",
       "\n",
       "                       authors  original_publication_year  \\\n",
       "0              Suzanne Collins                     2008.0   \n",
       "1  J.K. Rowling, Mary GrandPré                     1997.0   \n",
       "2              Stephenie Meyer                     2005.0   \n",
       "3                   Harper Lee                     1960.0   \n",
       "4          F. Scott Fitzgerald                     1925.0   \n",
       "\n",
       "                             original_title  ... ratings_count  \\\n",
       "0                          The Hunger Games  ...       4780653   \n",
       "1  Harry Potter and the Philosopher's Stone  ...       4602479   \n",
       "2                                  Twilight  ...       3866839   \n",
       "3                     To Kill a Mockingbird  ...       3198671   \n",
       "4                          The Great Gatsby  ...       2683664   \n",
       "\n",
       "  work_ratings_count  work_text_reviews_count  ratings_1  ratings_2  \\\n",
       "0            4942365                   155254      66715     127936   \n",
       "1            4800065                    75867      75504     101676   \n",
       "2            3916824                    95009     456191     436802   \n",
       "3            3340896                    72586      60427     117415   \n",
       "4            2773745                    51992      86236     197621   \n",
       "\n",
       "   ratings_3  ratings_4  ratings_5  \\\n",
       "0     560092    1481305    2706317   \n",
       "1     455024    1156318    3011543   \n",
       "2     793319     875073    1355439   \n",
       "3     446835    1001952    1714267   \n",
       "4     606158     936012     947718   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  https://images.gr-assets.com/books/1447303603m...   \n",
       "1  https://images.gr-assets.com/books/1474154022m...   \n",
       "2  https://images.gr-assets.com/books/1361039443m...   \n",
       "3  https://images.gr-assets.com/books/1361975680m...   \n",
       "4  https://images.gr-assets.com/books/1490528560m...   \n",
       "\n",
       "                                     small_image_url  \n",
       "0  https://images.gr-assets.com/books/1447303603s...  \n",
       "1  https://images.gr-assets.com/books/1474154022s...  \n",
       "2  https://images.gr-assets.com/books/1361039443s...  \n",
       "3  https://images.gr-assets.com/books/1361975680s...  \n",
       "4  https://images.gr-assets.com/books/1490528560s...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books = pd.read_csv(\"C:/Users/DELL/Downloads/books.csv\")\n",
    "books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>439</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1185</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  user_id  rating\n",
       "0        1      314       5\n",
       "1        1      439       3\n",
       "2        1      588       5\n",
       "3        1     1169       4\n",
       "4        1     1185       4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(\"C:/Users/DELL/Downloads/ratings.csv\")\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goodreads_book_id</th>\n",
       "      <th>tag_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30574</td>\n",
       "      <td>167697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11305</td>\n",
       "      <td>37174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11557</td>\n",
       "      <td>34173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8717</td>\n",
       "      <td>12986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>33114</td>\n",
       "      <td>12716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   goodreads_book_id  tag_id   count\n",
       "0                  1   30574  167697\n",
       "1                  1   11305   37174\n",
       "2                  1   11557   34173\n",
       "3                  1    8717   12986\n",
       "4                  1   33114   12716"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags = pd.read_csv(\"C:/Users/DELL/Downloads/book_tags.csv\")\n",
    "book_tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tag_id</th>\n",
       "      <th>tag_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>--1-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>--10-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>--12-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>--122-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tag_id tag_name\n",
       "0       0        -\n",
       "1       1     --1-\n",
       "2       2    --10-\n",
       "3       3    --12-\n",
       "4       4   --122-"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = pd.read_csv(\"C:/Users/DELL/Downloads/tags.csv\")\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>user_id</th>\n",
       "      <th>book_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  book_id\n",
       "0        1      112\n",
       "1        1      235\n",
       "2        1      533\n",
       "3        1     1198\n",
       "4        1     1874"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_read = pd.read_csv(\"C:/Users/DELL/Downloads/to_read.csv\")\n",
    "to_read.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2] Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  (i) Data Analysis for \"books.csv\" dataset -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 23)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.shape  # books dataset contains 10000 rows x 23 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                              0\n",
       "book_id                         0\n",
       "best_book_id                    0\n",
       "work_id                         0\n",
       "books_count                     0\n",
       "isbn                          700\n",
       "isbn13                        585\n",
       "authors                         0\n",
       "original_publication_year      21\n",
       "original_title                585\n",
       "title                           0\n",
       "language_code                1084\n",
       "average_rating                  0\n",
       "ratings_count                   0\n",
       "work_ratings_count              0\n",
       "work_text_reviews_count         0\n",
       "ratings_1                       0\n",
       "ratings_2                       0\n",
       "ratings_3                       0\n",
       "ratings_4                       0\n",
       "ratings_5                       0\n",
       "image_url                       0\n",
       "small_image_url                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.isnull().sum()   # Looking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             int64\n",
       "book_id                        int64\n",
       "best_book_id                   int64\n",
       "work_id                        int64\n",
       "books_count                    int64\n",
       "isbn                          object\n",
       "isbn13                       float64\n",
       "authors                       object\n",
       "original_publication_year    float64\n",
       "original_title                object\n",
       "title                         object\n",
       "language_code                 object\n",
       "average_rating               float64\n",
       "ratings_count                  int64\n",
       "work_ratings_count             int64\n",
       "work_text_reviews_count        int64\n",
       "ratings_1                      int64\n",
       "ratings_2                      int64\n",
       "ratings_3                      int64\n",
       "ratings_4                      int64\n",
       "ratings_5                      int64\n",
       "image_url                     object\n",
       "small_image_url               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.dtypes   # Datatypes for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(books.id.unique())  # There is information for 10000 different books in total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "With the purpose of having a quick look at what are the books with the highest average rating, sorted the dataset by average rating, selecting only the most relevant columns out of the 23 columns. \n",
    "\n",
    "The selected sample of users seem to like quite a lot **Bill Watterson's 'Calvin and Hobbes' comic series.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>authors</th>\n",
       "      <th>title</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3627</th>\n",
       "      <td>3628</td>\n",
       "      <td>24812</td>\n",
       "      <td>Bill Watterson</td>\n",
       "      <td>The Complete Calvin and Hobbes</td>\n",
       "      <td>4.82</td>\n",
       "      <td>28900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>3275</td>\n",
       "      <td>8</td>\n",
       "      <td>J.K. Rowling, Mary GrandPré</td>\n",
       "      <td>Harry Potter Boxed Set, Books 1-5 (Harry Potte...</td>\n",
       "      <td>4.77</td>\n",
       "      <td>33220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>17332218</td>\n",
       "      <td>Brandon Sanderson</td>\n",
       "      <td>Words of Radiance (The Stormlight Archive, #2)</td>\n",
       "      <td>4.77</td>\n",
       "      <td>73572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8853</th>\n",
       "      <td>8854</td>\n",
       "      <td>95602</td>\n",
       "      <td>Francine Rivers</td>\n",
       "      <td>Mark of the Lion Trilogy</td>\n",
       "      <td>4.76</td>\n",
       "      <td>9081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7946</th>\n",
       "      <td>7947</td>\n",
       "      <td>5031805</td>\n",
       "      <td>Anonymous, Lane T. Dennis, Wayne A. Grudem</td>\n",
       "      <td>ESV Study Bible</td>\n",
       "      <td>4.76</td>\n",
       "      <td>8953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4482</th>\n",
       "      <td>4483</td>\n",
       "      <td>24814</td>\n",
       "      <td>Bill Watterson</td>\n",
       "      <td>It's a Magical World: A Calvin and Hobbes Coll...</td>\n",
       "      <td>4.75</td>\n",
       "      <td>22351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>6361</td>\n",
       "      <td>70489</td>\n",
       "      <td>Bill Watterson</td>\n",
       "      <td>There's Treasure Everywhere: A Calvin and Hobb...</td>\n",
       "      <td>4.74</td>\n",
       "      <td>16766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>422</td>\n",
       "      <td>862041</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>Harry Potter Boxset (Harry Potter, #1-7)</td>\n",
       "      <td>4.74</td>\n",
       "      <td>190050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3752</th>\n",
       "      <td>3753</td>\n",
       "      <td>10</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>Harry Potter Collection (Harry Potter, #1-6)</td>\n",
       "      <td>4.73</td>\n",
       "      <td>24618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6919</th>\n",
       "      <td>6920</td>\n",
       "      <td>24815</td>\n",
       "      <td>Bill Watterson</td>\n",
       "      <td>The Indispensable Calvin and Hobbes</td>\n",
       "      <td>4.73</td>\n",
       "      <td>14597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   book_id                                     authors  \\\n",
       "3627  3628     24812                              Bill Watterson   \n",
       "3274  3275         8                 J.K. Rowling, Mary GrandPré   \n",
       "861    862  17332218                           Brandon Sanderson   \n",
       "8853  8854     95602                             Francine Rivers   \n",
       "7946  7947   5031805  Anonymous, Lane T. Dennis, Wayne A. Grudem   \n",
       "4482  4483     24814                              Bill Watterson   \n",
       "6360  6361     70489                              Bill Watterson   \n",
       "421    422    862041                                J.K. Rowling   \n",
       "3752  3753        10                                J.K. Rowling   \n",
       "6919  6920     24815                              Bill Watterson   \n",
       "\n",
       "                                                  title  average_rating  \\\n",
       "3627                     The Complete Calvin and Hobbes            4.82   \n",
       "3274  Harry Potter Boxed Set, Books 1-5 (Harry Potte...            4.77   \n",
       "861      Words of Radiance (The Stormlight Archive, #2)            4.77   \n",
       "8853                           Mark of the Lion Trilogy            4.76   \n",
       "7946                                    ESV Study Bible            4.76   \n",
       "4482  It's a Magical World: A Calvin and Hobbes Coll...            4.75   \n",
       "6360  There's Treasure Everywhere: A Calvin and Hobb...            4.74   \n",
       "421            Harry Potter Boxset (Harry Potter, #1-7)            4.74   \n",
       "3752       Harry Potter Collection (Harry Potter, #1-6)            4.73   \n",
       "6919                The Indispensable Calvin and Hobbes            4.73   \n",
       "\n",
       "      ratings_count  \n",
       "3627          28900  \n",
       "3274          33220  \n",
       "861           73572  \n",
       "8853           9081  \n",
       "7946           8953  \n",
       "4482          22351  \n",
       "6360          16766  \n",
       "421          190050  \n",
       "3752          24618  \n",
       "6919          14597  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Books with the highest average rating:\n",
    "\n",
    "books[['id', 'book_id', \n",
    "       'authors', 'title', \n",
    "       'average_rating', 'ratings_count']].sort_values('average_rating', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (ii) Data Analysis for \"ratings.csv\" dataset -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981756, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape   # ratings dataset contains 981756 rows x 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book_id    0\n",
       "user_id    0\n",
       "rating     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.isnull().sum() # Looking for null values. No null-values found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book_id    int64\n",
       "user_id    int64\n",
       "rating     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.dtypes # Datatypes for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.book_id.unique())  # There are ratings for 10000 different books in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53424"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.user_id.unique())  # Ratings to these 10000 books have been given by 53424 unique users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (iii) Data Analysis for \"book_tags\" dataset -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999912, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags.shape  # book_tags dataset contains 999912 rows x 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "goodreads_book_id    0\n",
       "tag_id               0\n",
       "count                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags.isnull().sum() # No null-values found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "goodreads_book_id    int64\n",
       "tag_id               int64\n",
       "count                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags.dtypes  # Datatypes of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(book_tags.goodreads_book_id.unique()) # There are 10000 different books in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34252"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(book_tags.tag_id.unique())  #  34252 unique tags in total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (iv) Data Analysis for \"tags\" dataset -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34252, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape   # tags dataset contains 34252 rows x 2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tag_id      0\n",
       "tag_name    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.isnull().sum()  # No null-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tag_id       int64\n",
       "tag_name    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.dtypes # Datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34252"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tags.tag_id.unique())  # There are 34252 unique tags that means dataset contains no duplicate record. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (v) Data Analysis for \"to_read\" dataset -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(912705, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_read.shape  # to_read dataset contains 912705 rows x 2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    0\n",
       "book_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_read.isnull().sum()  # No null-values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    int64\n",
       "book_id    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_read.dtypes  # Datatypes of each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9986"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(to_read.book_id.unique())  # 9986 uniques books have given 'to_read' tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48871"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(to_read.user_id.unique())  # 48871 unique users have used 'to_read' tag to read at least one book out of 10000 books"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look closely to rating dataset we can see that there are several users who have rated the same book more than once.\n",
    "So, we will erase those duplicated ratings from the rating table i.e for those user-book pairs that have more than one rating we will then keep only one record, indicating the average of the given ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59787</th>\n",
       "      <td>3204</td>\n",
       "      <td>8946</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729574</th>\n",
       "      <td>38259</td>\n",
       "      <td>3996</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11033</th>\n",
       "      <td>691</td>\n",
       "      <td>6472</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665335</th>\n",
       "      <td>34548</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81315</th>\n",
       "      <td>4359</td>\n",
       "      <td>2515</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42643</th>\n",
       "      <td>2308</td>\n",
       "      <td>8946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>42</td>\n",
       "      <td>8946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95001</th>\n",
       "      <td>5091</td>\n",
       "      <td>8946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81324</th>\n",
       "      <td>4359</td>\n",
       "      <td>3470</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54390</th>\n",
       "      <td>2935</td>\n",
       "      <td>8946</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  book_id  rating\n",
       "59787      3204     8946       5\n",
       "729574    38259     3996       4\n",
       "11033       691     6472       4\n",
       "665335    34548     7420       4\n",
       "81315      4359     2515       4\n",
       "42643      2308     8946       4\n",
       "572          42     8946       4\n",
       "95001      5091     8946       4\n",
       "81324      4359     3470       3\n",
       "54390      2935     8946       3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's the code to check which user-book pair have more than one rating\n",
    "\n",
    "user_per_book_ratecount = ratings.groupby(['user_id', 'book_id'], \n",
    "                                      as_index = False).count().sort_values('rating', ascending = False)\n",
    "user_per_book_ratecount.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>883706</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883707</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883708</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883709</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883710</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        book_id  user_id  rating\n",
       "883706     8946     3204       5\n",
       "883707     8946     3204       5\n",
       "883708     8946     3204       5\n",
       "883709     8946     3204       5\n",
       "883710     8946     3204       4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[(ratings.user_id == 3204) & (ratings.book_id == 8946)] # cross-checking if the above code is true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>489739</th>\n",
       "      <td>25094</td>\n",
       "      <td>6395</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280475</th>\n",
       "      <td>14074</td>\n",
       "      <td>6665</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280450</th>\n",
       "      <td>14074</td>\n",
       "      <td>3575</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280449</th>\n",
       "      <td>14074</td>\n",
       "      <td>3539</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280448</th>\n",
       "      <td>14074</td>\n",
       "      <td>3477</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280447</th>\n",
       "      <td>14074</td>\n",
       "      <td>3340</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280445</th>\n",
       "      <td>14074</td>\n",
       "      <td>3128</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280444</th>\n",
       "      <td>14074</td>\n",
       "      <td>2868</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280443</th>\n",
       "      <td>14074</td>\n",
       "      <td>2705</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280442</th>\n",
       "      <td>14074</td>\n",
       "      <td>2668</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  book_id  rating\n",
       "489739    25094     6395     5.0\n",
       "280475    14074     6665     5.0\n",
       "280450    14074     3575     5.0\n",
       "280449    14074     3539     5.0\n",
       "280448    14074     3477     5.0\n",
       "280447    14074     3340     5.0\n",
       "280445    14074     3128     5.0\n",
       "280444    14074     2868     5.0\n",
       "280443    14074     2705     5.0\n",
       "280442    14074     2668     5.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So as to get rid of the duplicates rows, now we calculate rating mean per user-book \n",
    "# So that we can later replace the mean to the rating values\n",
    "\n",
    "user_per_book_ratemean = ratings.groupby(['user_id', 'book_id'],\n",
    "                                       as_index = False).mean().sort_values('rating', ascending = False)\n",
    "user_per_book_ratemean.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59787</th>\n",
       "      <td>3204</td>\n",
       "      <td>8946</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  book_id  rating\n",
       "59787     3204     8946     4.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_per_book_ratemean[(user_per_book_ratemean.user_id == 3204) \n",
    "                       & (user_per_book_ratemean.book_id == 8946)] # cross verifying above code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59787</th>\n",
       "      <td>3204</td>\n",
       "      <td>8946</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729574</th>\n",
       "      <td>38259</td>\n",
       "      <td>3996</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11033</th>\n",
       "      <td>691</td>\n",
       "      <td>6472</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665335</th>\n",
       "      <td>34548</td>\n",
       "      <td>7420</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81315</th>\n",
       "      <td>4359</td>\n",
       "      <td>2515</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  book_id  mean  count\n",
       "59787      3204     8946   4.8      5\n",
       "729574    38259     3996   5.0      4\n",
       "11033       691     6472   5.0      4\n",
       "665335    34548     7420   5.0      4\n",
       "81315      4359     2515   5.0      4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating & Adding mean and rating count column to each user-book pair\n",
    "\n",
    "ratings['count'] = ratings['rating']\n",
    "user_per_book_rate = ratings.groupby(['user_id', 'book_id'], as_index = False) \\\n",
    "                   .agg({'rating' : 'mean', 'count' : 'count'}) \\\n",
    "                   .rename(columns = {'rating':'mean'}).sort_values('count', ascending = False)\n",
    "ratings = ratings.drop(['count'], axis = 1)\n",
    "user_per_book_rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>439</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1185</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  user_id  rating  mean  count\n",
       "0        1      314       5   5.0      1\n",
       "1        1      439       3   3.0      1\n",
       "2        1      588       5   5.0      1\n",
       "3        1     1169       4   4.0      1\n",
       "4        1     1185       4   4.0      1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.merge(ratings, user_per_book_rate, on = ['user_id', 'book_id'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>883710</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883706</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883709</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883708</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883707</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251241</th>\n",
       "      <td>2515</td>\n",
       "      <td>4359</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883651</th>\n",
       "      <td>8946</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399011</th>\n",
       "      <td>3996</td>\n",
       "      <td>38259</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883720</th>\n",
       "      <td>8946</td>\n",
       "      <td>5091</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883719</th>\n",
       "      <td>8946</td>\n",
       "      <td>5091</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        book_id  user_id  rating  mean  count\n",
       "883710     8946     3204       4   4.8      5\n",
       "883706     8946     3204       5   4.8      5\n",
       "883709     8946     3204       5   4.8      5\n",
       "883708     8946     3204       5   4.8      5\n",
       "883707     8946     3204       5   4.8      5\n",
       "251241     2515     4359       5   5.0      4\n",
       "883651     8946       42       5   5.0      4\n",
       "399011     3996    38259       5   5.0      4\n",
       "883720     8946     5091       5   5.0      4\n",
       "883719     8946     5091       5   5.0      4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sort_values('count', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981756, 5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>883706</th>\n",
       "      <td>8946</td>\n",
       "      <td>3204</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399011</th>\n",
       "      <td>3996</td>\n",
       "      <td>38259</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883651</th>\n",
       "      <td>8946</td>\n",
       "      <td>42</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883697</th>\n",
       "      <td>8946</td>\n",
       "      <td>2308</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736954</th>\n",
       "      <td>7420</td>\n",
       "      <td>34548</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883717</th>\n",
       "      <td>8946</td>\n",
       "      <td>5091</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644166</th>\n",
       "      <td>6472</td>\n",
       "      <td>691</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251241</th>\n",
       "      <td>2515</td>\n",
       "      <td>4359</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883694</th>\n",
       "      <td>8946</td>\n",
       "      <td>2078</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234947</th>\n",
       "      <td>2352</td>\n",
       "      <td>2981</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        book_id  user_id      mean\n",
       "883706     8946     3204  4.800000\n",
       "399011     3996    38259  5.000000\n",
       "883651     8946       42  5.000000\n",
       "883697     8946     2308  5.000000\n",
       "736954     7420    34548  5.000000\n",
       "883717     8946     5091  5.000000\n",
       "644166     6472      691  5.000000\n",
       "251241     2515     4359  5.000000\n",
       "883694     8946     2078  5.000000\n",
       "234947     2352     2981  3.333333"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping all the duplicate record for which user has given more than one rating for same book\n",
    "ratings = ratings.drop_duplicates(subset = ['book_id', 'user_id'], \n",
    "                                  keep = 'first', inplace = False).sort_values('count', ascending = False)\n",
    "ratings = ratings.drop(['rating', 'count'], axis = 1)\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(979478, 3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape # We can see 981756- 979478 = 2278 records were duplicate which is removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings.columns = ['book_id', 'user_id', 'rating'] # storing back in te same format as it was"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For making more relevant recommendations, We will look at what is the ratings distribution per users i.e we will check if there are users who have rated very few books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>total_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12873</th>\n",
       "      <td>12874</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30943</th>\n",
       "      <td>30944</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52035</th>\n",
       "      <td>52036</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>12381</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28157</th>\n",
       "      <td>28158</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  total_ratings\n",
       "12873    12874            200\n",
       "30943    30944            200\n",
       "52035    52036            199\n",
       "12380    12381            199\n",
       "28157    28158            199"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_per_user = ratings[['user_id', 'rating']].groupby(['user_id'], as_index = False).count() \\\n",
    "                      .rename(columns = {'rating' : 'total_ratings'}).sort_values('total_ratings', ascending = False)\n",
    "rate_per_user.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "plt.hist(rate_per_user['total_ratings'], bins = 45, color = '#9F079F')\n",
    "plt.xlabel(\"Total Ratings\")\n",
    "plt.ylabel(\" Users_id\")\n",
    "plt.title(\"Total ratings Vs. Users_id\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram above shows how many users have rated only 1 to 25 books. \n",
    "\n",
    "Considering that we are using only the subset 10000 of all the books listed in Goodreads, it will be difficult to give accurate recommendation to users with that few ratings.\n",
    "\n",
    "Further in the analysis, I have included only users that have rated at least 20 of the 10000 books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>total_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>486526</th>\n",
       "      <td>81</td>\n",
       "      <td>12874</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977600</th>\n",
       "      <td>1492</td>\n",
       "      <td>30944</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977605</th>\n",
       "      <td>2209</td>\n",
       "      <td>30944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977604</th>\n",
       "      <td>2269</td>\n",
       "      <td>30944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977603</th>\n",
       "      <td>1431</td>\n",
       "      <td>30944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977602</th>\n",
       "      <td>1391</td>\n",
       "      <td>30944</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977782</th>\n",
       "      <td>3077</td>\n",
       "      <td>30944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977707</th>\n",
       "      <td>245</td>\n",
       "      <td>30944</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977706</th>\n",
       "      <td>232</td>\n",
       "      <td>30944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977705</th>\n",
       "      <td>239</td>\n",
       "      <td>30944</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        book_id  user_id  rating  total_ratings\n",
       "486526       81    12874     4.0            200\n",
       "977600     1492    30944     4.0            200\n",
       "977605     2209    30944     5.0            200\n",
       "977604     2269    30944     5.0            200\n",
       "977603     1431    30944     5.0            200\n",
       "977602     1391    30944     3.0            200\n",
       "977782     3077    30944     5.0            200\n",
       "977707      245    30944     4.0            200\n",
       "977706      232    30944     5.0            200\n",
       "977705      239    30944     4.0            200"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.merge(ratings, rate_per_user, on = ['user_id'])\n",
    "ratings = ratings.sort_values('total_ratings', ascending = False)\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272086"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only the users with more than a minimum of ratings\n",
    "min_ratings = ratings['total_ratings'] <= 20 \n",
    "min_ratings.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "707392"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings[-min_ratings]\n",
    "len(ratings) # Only 707392 are kept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13971"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  total of remaining users after removing the ones with less than the minimum\n",
    "len(ratings.user_id.unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9998"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # total of remaining books after removing users with less than the established minimum\n",
    "len(ratings.book_id.unique()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, looking for the books that have very few ratings.Now to avoid recommandation of those books which are read by few user, we will remove the book with less than 30 ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>ratings_recieved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9342</th>\n",
       "      <td>9345</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9855</th>\n",
       "      <td>9858</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9087</th>\n",
       "      <td>9090</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8802</th>\n",
       "      <td>8804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8371</th>\n",
       "      <td>8372</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      book_id  ratings_recieved\n",
       "9342     9345                 2\n",
       "9855     9858                 1\n",
       "9087     9090                 1\n",
       "8802     8804                 1\n",
       "8371     8372                 1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_per_book = ratings[['book_id', 'rating']].groupby(['book_id'], as_index = False).count() \\\n",
    "                      .rename(columns = {'rating' : 'ratings_recieved'}).sort_values('ratings_recieved', ascending = False)\n",
    "rate_per_book.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "plt.hist(rate_per_book['ratings_recieved'], bins = 45, color = '#117C24')\n",
    "plt.xlabel(\"Ratings Recieved\")\n",
    "plt.ylabel(\" Book_id\")\n",
    "plt.title(\"Rating Recieved Vs Book_id\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23480"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.merge(ratings, rate_per_book, on = ['book_id'])\n",
    "min_ratings_recieved = ratings.ratings_recieved <= 30\n",
    "min_ratings_recieved.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "683912"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings[-min_ratings_recieved]\n",
    "len(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13970"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.user_id.unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8826"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.book_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item Based Collabrative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this we will create user by book matrix, in which each row indicates a single user, and each columns represents a single book. The resulting table contains an extremely high number of NAN's, due to the fact that - even the users who have read the highest number of books in our dataset, they have just read roughly 1% of all the books in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>Angels (Walsh Family, #3)</th>\n",
       "      <th>#GIRLBOSS</th>\n",
       "      <th>'Salem's Lot</th>\n",
       "      <th>'Tis (Frank McCourt, #2)</th>\n",
       "      <th>1,000 Places to See Before You Die</th>\n",
       "      <th>10% Happier: How I Tamed the Voice in My Head, Reduced Stress Without Losing My Edge, and Found Self-Help That Actually Works</th>\n",
       "      <th>100 Bullets, Vol. 1: First Shot, Last Call</th>\n",
       "      <th>100 Love Sonnets</th>\n",
       "      <th>100 Selected Poems</th>\n",
       "      <th>10th Anniversary (Women's Murder Club, #10)</th>\n",
       "      <th>...</th>\n",
       "      <th>god is Not Great: How Religion Poisons Everything</th>\n",
       "      <th>number9dream</th>\n",
       "      <th>sTORI Telling</th>\n",
       "      <th>xxxHolic, Vol. 1 (xxxHOLiC, #1)</th>\n",
       "      <th>الفيل الأزرق</th>\n",
       "      <th>تراب الماس</th>\n",
       "      <th>عزازيل</th>\n",
       "      <th>هيبتا</th>\n",
       "      <th>يوتوبيا</th>\n",
       "      <th>ڤيرتيجو</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>7</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>19</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>23</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>27</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>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53401</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>53403</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>53409</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>53411</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>53413</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>13970 rows × 8795 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title     Angels (Walsh Family, #3)  #GIRLBOSS  'Salem's Lot  \\\n",
       "user_id                                                        \n",
       "7                               NaN        NaN           NaN   \n",
       "19                              NaN        NaN           NaN   \n",
       "23                              NaN        NaN           NaN   \n",
       "27                              NaN        NaN           NaN   \n",
       "35                              NaN        NaN           NaN   \n",
       "...                             ...        ...           ...   \n",
       "53401                           NaN        NaN           NaN   \n",
       "53403                           NaN        NaN           NaN   \n",
       "53409                           NaN        NaN           NaN   \n",
       "53411                           NaN        NaN           NaN   \n",
       "53413                           NaN        NaN           NaN   \n",
       "\n",
       "title    'Tis (Frank McCourt, #2)  1,000 Places to See Before You Die  \\\n",
       "user_id                                                                 \n",
       "7                             NaN                                 NaN   \n",
       "19                            NaN                                 NaN   \n",
       "23                            NaN                                 NaN   \n",
       "27                            NaN                                 NaN   \n",
       "35                            NaN                                 NaN   \n",
       "...                           ...                                 ...   \n",
       "53401                         NaN                                 NaN   \n",
       "53403                         NaN                                 NaN   \n",
       "53409                         NaN                                 NaN   \n",
       "53411                         NaN                                 NaN   \n",
       "53413                         NaN                                 NaN   \n",
       "\n",
       "title    10% Happier: How I Tamed the Voice in My Head, Reduced Stress Without Losing My Edge, and Found Self-Help That Actually Works  \\\n",
       "user_id                                                                                                                                  \n",
       "7                                                      NaN                                                                               \n",
       "19                                                     NaN                                                                               \n",
       "23                                                     NaN                                                                               \n",
       "27                                                     NaN                                                                               \n",
       "35                                                     NaN                                                                               \n",
       "...                                                    ...                                                                               \n",
       "53401                                                  NaN                                                                               \n",
       "53403                                                  NaN                                                                               \n",
       "53409                                                  NaN                                                                               \n",
       "53411                                                  NaN                                                                               \n",
       "53413                                                  NaN                                                                               \n",
       "\n",
       "title    100 Bullets, Vol. 1: First Shot, Last Call  100 Love Sonnets  \\\n",
       "user_id                                                                 \n",
       "7                                               NaN               NaN   \n",
       "19                                              NaN               NaN   \n",
       "23                                              NaN               NaN   \n",
       "27                                              NaN               NaN   \n",
       "35                                              NaN               NaN   \n",
       "...                                             ...               ...   \n",
       "53401                                           NaN               NaN   \n",
       "53403                                           NaN               NaN   \n",
       "53409                                           NaN               NaN   \n",
       "53411                                           NaN               NaN   \n",
       "53413                                           NaN               NaN   \n",
       "\n",
       "title    100 Selected Poems  10th Anniversary (Women's Murder Club, #10)  ...  \\\n",
       "user_id                                                                   ...   \n",
       "7                       NaN                                          NaN  ...   \n",
       "19                      NaN                                          NaN  ...   \n",
       "23                      NaN                                          NaN  ...   \n",
       "27                      NaN                                          NaN  ...   \n",
       "35                      NaN                                          NaN  ...   \n",
       "...                     ...                                          ...  ...   \n",
       "53401                   NaN                                          NaN  ...   \n",
       "53403                   NaN                                          NaN  ...   \n",
       "53409                   NaN                                          NaN  ...   \n",
       "53411                   NaN                                          NaN  ...   \n",
       "53413                   NaN                                          NaN  ...   \n",
       "\n",
       "title    god is Not Great: How Religion Poisons Everything  number9dream  \\\n",
       "user_id                                                                    \n",
       "7                                                      NaN           NaN   \n",
       "19                                                     NaN           NaN   \n",
       "23                                                     NaN           NaN   \n",
       "27                                                     NaN           NaN   \n",
       "35                                                     NaN           NaN   \n",
       "...                                                    ...           ...   \n",
       "53401                                                  NaN           NaN   \n",
       "53403                                                  NaN           NaN   \n",
       "53409                                                  NaN           NaN   \n",
       "53411                                                  NaN           NaN   \n",
       "53413                                                  NaN           NaN   \n",
       "\n",
       "title    sTORI Telling  xxxHolic, Vol. 1 (xxxHOLiC, #1)  الفيل الأزرق  \\\n",
       "user_id                                                                 \n",
       "7                  NaN                              NaN           NaN   \n",
       "19                 NaN                              NaN           NaN   \n",
       "23                 NaN                              NaN           NaN   \n",
       "27                 NaN                              NaN           NaN   \n",
       "35                 NaN                              NaN           NaN   \n",
       "...                ...                              ...           ...   \n",
       "53401              NaN                              NaN           NaN   \n",
       "53403              NaN                              NaN           NaN   \n",
       "53409              NaN                              NaN           NaN   \n",
       "53411              NaN                              NaN           NaN   \n",
       "53413              NaN                              NaN           NaN   \n",
       "\n",
       "title    تراب الماس  عزازيل  هيبتا  يوتوبيا  ڤيرتيجو  \n",
       "user_id                                               \n",
       "7               NaN     NaN    NaN      NaN      NaN  \n",
       "19              NaN     NaN    NaN      NaN      NaN  \n",
       "23              NaN     NaN    NaN      NaN      NaN  \n",
       "27              NaN     NaN    NaN      NaN      NaN  \n",
       "35              NaN     NaN    NaN      NaN      NaN  \n",
       "...             ...     ...    ...      ...      ...  \n",
       "53401           NaN     NaN    NaN      NaN      NaN  \n",
       "53403           NaN     NaN    NaN      NaN      NaN  \n",
       "53409           NaN     NaN    NaN      NaN      NaN  \n",
       "53411           NaN     NaN    NaN      NaN      NaN  \n",
       "53413           NaN     NaN    NaN      NaN      NaN  \n",
       "\n",
       "[13970 rows x 8795 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_title = pd.merge(ratings, books[['id', 'title']], left_on = 'book_id', right_on = 'id')\n",
    "each_book_rating = pd.pivot_table(ratings_title, index = 'user_id', columns = 'title', values = 'rating')\n",
    "each_book_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13970, 8795)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "each_book_rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_corr = np.corrcoef(each_book_rating.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8795, 8795)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Angels (Walsh Family, #3)',\n",
       " '#GIRLBOSS',\n",
       " \"'Salem's Lot\",\n",
       " \"'Tis (Frank McCourt, #2)\",\n",
       " '1,000 Places to See Before You Die',\n",
       " '10% Happier: How I Tamed the Voice in My Head, Reduced Stress Without Losing My Edge, and Found Self-Help That Actually Works',\n",
       " '100 Bullets, Vol. 1: First Shot, Last Call',\n",
       " '100 Love Sonnets',\n",
       " '100 Selected Poems',\n",
       " \"10th Anniversary (Women's Murder Club, #10)\",\n",
       " '11 Birthdays (Willow Falls, #1)',\n",
       " '11/22/63',\n",
       " \"11th Hour (Women's Murder Club, #11)\",\n",
       " \"12th of Never (Women's Murder Club, #12)\",\n",
       " '13 Little Blue Envelopes (Little Blue Envelope, #1)',\n",
       " '14',\n",
       " '1421: The Year China Discovered America',\n",
       " '1491: New Revelations of the Americas Before Columbus',\n",
       " '1493: Uncovering the New World Columbus Created',\n",
       " '14th Deadly Sin (Women’s Murder Club, #14)',\n",
       " \"15th Affair (Women's Murder Club #15)\",\n",
       " '1776',\n",
       " '1919',\n",
       " '1984',\n",
       " '1Q84',\n",
       " '1Q84 BOOK 1 (1Q84, #1)',\n",
       " \"1st to Die (Women's Murder Club, #1)\",\n",
       " '2 States: The Story of My Marriage',\n",
       " '2001: A Space Odyssey (Space Odyssey, #1)',\n",
       " '2010: Odyssey Two (Space Odyssey, #2)',\n",
       " '206 Bones (Temperance Brennan, #12)',\n",
       " '2061: Odyssey Three (Space Odyssey, #3)',\n",
       " '20th Century Ghosts',\n",
       " '2312',\n",
       " '2666',\n",
       " '2BR02B',\n",
       " \"2nd Chance (Women's Murder Club, #2)\",\n",
       " '30 Days of Night, Vol. 1',\n",
       " '300',\n",
       " '3001: The Final Odyssey (Space Odyssey, #4)',\n",
       " \"3rd Degree (Women's Murder Club, #3)\",\n",
       " '44 Scotland Street (44 Scotland Street, #1)',\n",
       " '4:50 from Paddington (Miss Marple, #8)',\n",
       " \"4th of July (Women's Murder Club, #4)\",\n",
       " '61 Hours (Jack Reacher, #14)',\n",
       " '77 Shadow Street (Pendleton, #1)',\n",
       " \"7th Heaven (Women's Murder Club, #7)\",\n",
       " '84, Charing Cross Road',\n",
       " '90 Minutes in Heaven: A True Story of Death and Life',\n",
       " 'A Bad Case of Stripes',\n",
       " 'A Bargain for Frances',\n",
       " 'A Bear Called Paddington (Paddington, #1)',\n",
       " 'A Beautiful Dark (A Beautiful Dark, #1)',\n",
       " 'A Beautiful Mind',\n",
       " 'A Beautiful Wedding (Beautiful, #2.5)',\n",
       " 'A Bend in the River',\n",
       " 'A Bend in the Road',\n",
       " 'A Better World (Brilliance Saga, #2)',\n",
       " 'A Bite to Remember (Argeneau #5)',\n",
       " 'A Blaze of Sun (A Shade of Vampire, #5)',\n",
       " 'A Book of Five Rings: The Classic Guide to Strategy',\n",
       " 'A Breath of Snow and Ashes (Outlander, #6)',\n",
       " 'A Bridge Too Far',\n",
       " 'A Brief History of Seven Killings',\n",
       " 'A Brief History of Time',\n",
       " 'A Briefer History of Time',\n",
       " 'A Canticle for Leibowitz',\n",
       " 'A Caress of Twilight (Merry Gentry, #2)',\n",
       " 'A Caribbean Mystery (Miss Marple, #10)',\n",
       " 'A Case of Need',\n",
       " 'A Castle of Sand (A Shade of Vampire, #3)',\n",
       " 'A Certain Slant of Light (Light, #1)',\n",
       " 'A Chair for My Mother',\n",
       " 'A Charlie Brown Christmas',\n",
       " 'A Child Called \"It\" (Dave Pelzer #1)',\n",
       " \"A Child's Garden of Verses\",\n",
       " 'A Christmas Carol',\n",
       " 'A Christmas Carol and Other Christmas Writings',\n",
       " 'A Christmas Carol, The Chimes and The Cricket on the Hearth',\n",
       " 'A Civil Action',\n",
       " 'A Civil Campaign (Vorkosigan Saga, #12)',\n",
       " 'A Clash of Kings  (A Song of Ice and Fire, #2)',\n",
       " 'A Clockwork Orange',\n",
       " 'A Color of His Own',\n",
       " 'A Complicated Kindness',\n",
       " 'A Coney Island of the Mind',\n",
       " 'A Confederacy of Dunces',\n",
       " \"A Connecticut Yankee in King Arthur's Court\",\n",
       " 'A Constellation of Vital Phenomena',\n",
       " \"A Cook's Tour: Global Adventures in Extreme Cuisines\",\n",
       " 'A Countess Below Stairs',\n",
       " 'A Course in Miracles',\n",
       " 'A Court of Mist and Fury (A Court of Thorns and Roses, #2)',\n",
       " 'A Court of Thorns and Roses (A Court of Thorns and Roses, #1)',\n",
       " 'A Court of Wings and Ruin (A Court of Thorns and Roses, #3)',\n",
       " 'A Crown of Swords (Wheel of Time, #7)',\n",
       " 'A Cry In The Night',\n",
       " 'A Dance with Dragons (A Song of Ice and Fire, #5)',\n",
       " 'A Dance with Dragons: After the Feast (A Song of Ice and Fire #5, Part 2 of 2)',\n",
       " 'A Dance with Dragons: Dreams and Dust (A Song of Ice and Fire #5, Part 1 of 2)',\n",
       " 'A Dangerous Fortune',\n",
       " 'A Dangerous Path (Warriors, #5)',\n",
       " 'A Darker Shade of Magic (Shades of Magic, #1)',\n",
       " 'A Darkness At Sethanon (The Riftwar Saga, #4)',\n",
       " 'A Darkness More Than Night (Harry Bosch, #7; Terry McCaleb, #2; Harry Bosch Universe, #9)',\n",
       " 'A Death in Vienna (Gabriel Allon, #4)',\n",
       " 'A Death in the Family',\n",
       " 'A Deepness in the Sky (Zones of Thought, #2)',\n",
       " 'A Different Blue',\n",
       " 'A Dirty Job (Grim Reaper, #1)',\n",
       " 'A Discovery of Witches (All Souls Trilogy, #1)',\n",
       " 'A Distant Mirror:  The Calamitous 14th Century',\n",
       " \"A Dog's Purpose (A Dog's Purpose, #1)\",\n",
       " \"A Doll's House\",\n",
       " 'A Drink Before the War (Kenzie & Gennaro, #1)',\n",
       " 'A Faint Cold Fear (Grant County, #3)',\n",
       " 'A Fall of Marigolds',\n",
       " 'A Farewell to Arms',\n",
       " 'A Fatal Grace (Chief Inspector Armand Gamache, #2)',\n",
       " 'A Feast for Crows (A Song of Ice and Fire, #4)',\n",
       " 'A Fine Balance',\n",
       " 'A Fire Upon the Deep (Zones of Thought, #1)',\n",
       " 'A Fish Out of Water',\n",
       " 'A Fistful of Charms (The Hollows, #4)',\n",
       " 'A Fraction of the Whole',\n",
       " 'A Game of Thrones (A Song of Ice and Fire, #1)',\n",
       " 'A Game of Thrones / A Clash of Kings (A Song of Ice and Fire, #1-2)',\n",
       " 'A Game of Thrones: Comic Book, Issue 1',\n",
       " 'A Game of Thrones: The Graphic Novel, Vol. 1',\n",
       " 'A Game of You (The Sandman #5)',\n",
       " 'A Gate at the Stairs',\n",
       " 'A Gathering of Shadows (Shades of Magic, #2)',\n",
       " 'A Gentleman in Moscow',\n",
       " 'A Girl Named Zippy',\n",
       " 'A Girl of the Limberlost (Limberlost, #2)',\n",
       " 'A God in Ruins',\n",
       " 'A Good Man is Hard to Find and Other Stories',\n",
       " 'A Good Yarn (Blossom Street, #2)',\n",
       " 'A Great Deliverance (Inspector Lynley, #1)',\n",
       " 'A Great Reckoning (Chief Inspector Armand Gamache, #12)',\n",
       " 'A Great and Terrible Beauty (Gemma Doyle, #1)',\n",
       " 'A Grief Observed',\n",
       " 'A Grown-Up Kind of Pretty',\n",
       " 'A Handful of Dust',\n",
       " 'A Hat Full of Sky (Discworld, #32; Tiffany Aching, #2)',\n",
       " 'A Head Full of Ghosts',\n",
       " 'A Heartbreaking Work of Staggering Genius',\n",
       " 'A Hero of Our Time',\n",
       " 'A Hidden Fire (Elemental Mysteries, #1)',\n",
       " 'A History of God: The 4,000-Year Quest of Judaism, Christianity, and Islam',\n",
       " 'A History of Western Philosophy',\n",
       " 'A History of the World in 6 Glasses',\n",
       " 'A Hologram for the King',\n",
       " 'A Home at the End of the World',\n",
       " 'A Homemade Life: Stories and Recipes from My Kitchen Table',\n",
       " 'A House for Mr Biswas',\n",
       " 'A House in the Sky',\n",
       " 'A Hundred Summers',\n",
       " 'A Hunger Like No Other (Immortals After Dark #2)',\n",
       " 'A Killing Frost (Tomorrow, #3)',\n",
       " \"A King's Ransom (The 39 Clues: Cahills vs. Vespers, #2)\",\n",
       " 'A Kingdom of Dreams (Westmoreland, #1)',\n",
       " 'A Kiss for Little Bear (An I Can Read Book) by Minarik, Else Holmelund [1984]',\n",
       " 'A Kiss in Time',\n",
       " 'A Kiss of Shadows (Merry Gentry, #1)',\n",
       " 'A Knight in Shining Armor',\n",
       " 'A Knight of the Seven Kingdoms (The Tales of Dunk and Egg, #1-3)',\n",
       " 'A Knight of the Word (Word & Void, #2)',\n",
       " 'A Land More Kind Than Home',\n",
       " 'A Leaf on the Wind of All Hallows (Outlander, #8.5)',\n",
       " 'A Lesson Before Dying',\n",
       " 'A Lick of Frost (Merry Gentry, #6)',\n",
       " 'A Light in the Attic',\n",
       " 'A Light in the Window (Mitford Years, #2)',\n",
       " 'A Lion Among Men (The Wicked Years, #3)',\n",
       " 'A Little Life',\n",
       " 'A Little Princess',\n",
       " 'A Living Nightmare (Cirque Du Freak, #1)',\n",
       " 'A Long Walk to Water: Based on a True Story',\n",
       " 'A Long Way Down',\n",
       " 'A Long Way Gone: Memoirs of a Boy Soldier',\n",
       " 'A Long Way from Chicago (A Long Way from Chicago, #1)',\n",
       " 'A Lot like Love (FBI/US Attorney, #2)',\n",
       " 'A Man Called Ove',\n",
       " 'A Man Named Dave (Dave Pelzer #3)',\n",
       " 'A Man Without a Country',\n",
       " 'A Man in Full',\n",
       " 'A Mango-Shaped Space',\n",
       " 'A Map of the World',\n",
       " 'A Matter of Honor',\n",
       " 'A Memory of Light (Wheel of Time, #14)',\n",
       " 'A Mercy',\n",
       " \"A Midsummer Night's Dream\",\n",
       " \"A Midsummer's Nightmare\",\n",
       " 'A Million Little Pieces',\n",
       " 'A Million Miles in a Thousand Years: What I Learned While Editing My Life',\n",
       " 'A Million Suns (Across the Universe, #2)',\n",
       " 'A Modern Witch (A Modern Witch, #1)',\n",
       " 'A Modest Proposal',\n",
       " 'A Modest Proposal and Other Satirical Works',\n",
       " 'A Monster Calls',\n",
       " 'A Monstrous Regiment of Women (Mary Russell, #2)',\n",
       " 'A Morbid Taste for Bones (Chronicles of Brother Cadfael, #1)',\n",
       " \"A Mother's Reckoning: Living in the Aftermath of Tragedy\",\n",
       " 'A Moveable Feast',\n",
       " 'A Murder Is Announced (Miss Marple, #5)',\n",
       " \"A New Earth: Awakening to Your Life's Purpose\",\n",
       " 'A New Hope  (Star Wars: Novelizations #4)',\n",
       " 'A New Song (Mitford Years, #5)',\n",
       " 'A Night to Remember',\n",
       " 'A Northern Light',\n",
       " 'A Painted House',\n",
       " 'A Passage to India',\n",
       " \"A People's History of the United States\",\n",
       " 'A Perfect Blood (The Hollows, #10)',\n",
       " \"A Perfect Evil (Maggie O'Dell, #1)\",\n",
       " 'A Perfect Spy',\n",
       " 'A Piece of Cake',\n",
       " 'A Place Called Freedom',\n",
       " 'A Place Called Here',\n",
       " 'A Pocket Full of Rye (Miss Marple, #7)',\n",
       " 'A Pocket for Corduroy',\n",
       " 'A Portrait of the Artist as a Young Man',\n",
       " 'A Prayer for Owen Meany',\n",
       " 'A Princess of Mars (Barsoom, #1)',\n",
       " 'A Prisoner of Birth',\n",
       " 'A Quick Bite (Argeneau #1)',\n",
       " 'A Raisin in the Sun',\n",
       " 'A Random Walk Down Wall Street',\n",
       " 'A Red Herring Without Mustard (Flavia de Luce, #3)',\n",
       " 'A Redbird Christmas',\n",
       " 'A Reliable Wife',\n",
       " 'A Return to Love: Reflections on the Principles of \"A Course in Miracles\"',\n",
       " 'A Ring of Endless Light (Austin Family, #4)',\n",
       " 'A River Runs Through It and Other Stories',\n",
       " 'A Rogue by Any Other Name (The Rules of Scoundrels, #1)',\n",
       " \"A Room of One's Own\",\n",
       " 'A Room with a View',\n",
       " 'A Rose for Emily and Other Stories',\n",
       " 'A Rule Against Murder (Chief Inspector Armand Gamache, #4)',\n",
       " 'A Sand County Almanac and Sketches Here and There',\n",
       " 'A Scandal in Bohemia (The Adventures of Sherlock Holmes, #1)',\n",
       " 'A Scanner Darkly',\n",
       " 'A Secret Kept',\n",
       " 'A Separate Peace',\n",
       " 'A Separate Reality',\n",
       " 'A Severe Mercy: A Story of Faith, Tragedy and Triumph',\n",
       " 'A Shade of Blood (A Shade of Vampire, #2)',\n",
       " 'A Shade of Vampire (A Shade of Vampire, #1)',\n",
       " 'A Shadow of Light (A Shade of Vampire, #4)',\n",
       " 'A Short History of Nearly Everything',\n",
       " 'A Short History of Tractors in Ukrainian',\n",
       " 'A Sick Day for Amos McGee',\n",
       " 'A Simple Plan',\n",
       " 'A Single Man',\n",
       " 'A Single Shard',\n",
       " 'A Son of the Circus',\n",
       " 'A Song of Ice and Fire (A Song of Ice and Fire, #1-4)',\n",
       " 'A Song of Ice and Fire (A Song of Ice and Fire, #1-5)',\n",
       " 'A Spell for Chameleon (Xanth #1)',\n",
       " 'A Spool of Blue Thread',\n",
       " 'A Spot of Bother',\n",
       " 'A Stolen Life',\n",
       " 'A Storm of Swords (A Song of Ice and Fire, #3)',\n",
       " 'A Storm of Swords: Blood and Gold (A Song of Ice and Fire, #3: Part 2 of 2)',\n",
       " 'A Storm of Swords: Steel and Snow (A Song of Ice and Fire, #3: Part 1 of 2)',\n",
       " 'A Stranger In The Mirror',\n",
       " 'A Stranger Is Watching',\n",
       " 'A Streetcar Named Desire',\n",
       " 'A Stroke of Midnight (Merry Gentry, #4)',\n",
       " 'A Study in Scarlet',\n",
       " 'A Suitable Boy (A Suitable Boy, #1)',\n",
       " 'A Summer Affair',\n",
       " \"A Supposedly Fun Thing I'll Never Do Again:  Essays and Arguments\",\n",
       " 'A Swiftly Tilting Planet (A Wrinkle in Time Quintet, #3)',\n",
       " 'A Tale for the Time Being',\n",
       " 'A Tale of Two Cities',\n",
       " 'A Tale of Two Cities / Great Expectations',\n",
       " 'A Theory of Justice',\n",
       " 'A Thousand Acres',\n",
       " 'A Thousand Pieces of You (Firebird, #1)',\n",
       " 'A Thousand Splendid Suns',\n",
       " 'A Time to Kill',\n",
       " 'A Torch Against the Night (An Ember in the Ashes, #2)',\n",
       " 'A Touch of Dead (Sookie Stackhouse, #4.1, #4.3, #5.1, #7.1, #8.1)',\n",
       " 'A Town Like Alice',\n",
       " 'A Tree Grows in Brooklyn',\n",
       " 'A Trick of the Light (Chief Inspector Armand Gamache, #7)',\n",
       " 'A Twist in the Tale',\n",
       " 'A Vintage Affair',\n",
       " 'A Virtuous Woman',\n",
       " 'A Visit from the Goon Squad',\n",
       " 'A Voice in the Wind (Mark of the Lion, #1)',\n",
       " 'A Walk Across America',\n",
       " 'A Walk Across the Sun',\n",
       " 'A Walk in the Woods',\n",
       " 'A Walk to Remember',\n",
       " 'A Wanted Man (Jack Reacher, #17)',\n",
       " \"A War of Gifts (Ender's Saga, #1.1)\",\n",
       " 'A Wedding in December',\n",
       " 'A Week in Winter',\n",
       " 'A Whole New Mind: Why Right-Brainers Will Rule the Future',\n",
       " 'A Widow for One Year',\n",
       " 'A Wild Sheep Chase (The Rat, #3)',\n",
       " 'A Wind in the Door (A Wrinkle in Time Quintet, #2)',\n",
       " 'A Window Opens',\n",
       " 'A Wizard of Earthsea (Earthsea Cycle, #1)',\n",
       " 'A Wolf at the Table',\n",
       " 'A Woman of Substance (Emma Harte Saga #1)',\n",
       " 'A World Without Heroes (Beyonders, #1)',\n",
       " 'A Wrinkle in Time (A Wrinkle in Time Quintet, #1)',\n",
       " 'A Wrinkle in Time: The Graphic Novel',\n",
       " 'A Year Down Yonder (A Long Way from Chicago, #2)',\n",
       " 'A Year in Provence',\n",
       " 'A Year in the Merde',\n",
       " 'A Yellow Raft in Blue Water',\n",
       " 'A is for Alibi (Kinsey Millhone, #1)',\n",
       " \"Abaddon's Gate (The Expanse, #3)\",\n",
       " 'Abandon (Abandon, #1)',\n",
       " 'Abarat (Abarat, #1)',\n",
       " 'Abducted (Lizzy Gardner #1)',\n",
       " 'Abhorsen (Abhorsen, #3)',\n",
       " 'About That Night (FBI/US Attorney, #3)',\n",
       " 'About a Boy',\n",
       " 'Abraham Lincoln: Vampire Hunter',\n",
       " 'Absalom, Absalom!',\n",
       " 'Absent In The Spring',\n",
       " 'Absolute Boyfriend, Vol. 1',\n",
       " 'Absolute Fear (New Orleans, #4)',\n",
       " 'Absolute Power',\n",
       " 'Absolution Gap',\n",
       " 'Absurdistan',\n",
       " \"Academ's Fury (Codex Alera, #2)\",\n",
       " 'Accelerando (Singularity)',\n",
       " 'Acceptance (Southern Reach, #3)',\n",
       " 'Accidentally on Purpose (Accidentally on Purpose, #1)',\n",
       " 'Accordance (Significance, #2)',\n",
       " 'Acheron (Dark-Hunter #14)',\n",
       " 'Across the Nightingale Floor (Tales of the Otori, #1)',\n",
       " 'Across the Universe (Across the Universe, #1)',\n",
       " 'Act of Treason (Mitch Rapp, #9)',\n",
       " 'Adam Bede',\n",
       " 'Addicted to You (Addicted, #1)',\n",
       " 'Adrift: Seventy-Six Days Lost at Sea',\n",
       " 'Adultery',\n",
       " \"Aesop's Fables\",\n",
       " 'Affinity',\n",
       " 'Affliction (Anita Blake, Vampire Hunter #22)',\n",
       " 'After',\n",
       " 'After (After, #1)',\n",
       " 'After Dark',\n",
       " \"After I'm Gone\",\n",
       " 'After You (Me Before You, #2)',\n",
       " 'After the Funeral (Hercule Poirot, #29)',\n",
       " 'After the Night',\n",
       " 'After the Quake',\n",
       " 'Afterlife (Evernight, #4)',\n",
       " 'Afterwards',\n",
       " 'Afterworlds (Afterworlds #1)',\n",
       " 'Again the Magic (Wallflowers, #0)',\n",
       " 'Against All Enemies (Max Moore, #1)',\n",
       " 'Against a Dark Background',\n",
       " 'Agamemnon (Oresteia, #1)',\n",
       " 'Agent to the Stars',\n",
       " 'Agents of Light and Darkness (Nightside, #2)',\n",
       " 'Agnes Grey',\n",
       " 'Agnes and the Hitman',\n",
       " \"Ahab's Wife, or The Star-Gazer\",\n",
       " \"Ain't She Sweet\",\n",
       " 'Airborn (Matt Cruse, #1)',\n",
       " 'Airframe',\n",
       " 'Airhead (Airhead, #1)',\n",
       " 'Airman',\n",
       " 'Airport',\n",
       " 'Akira, Vol. 1',\n",
       " 'Al Capone Does My Shirts (Al Capone at Alcatraz, #1)',\n",
       " 'Alanna: The First Adventure (Song of the Lioness, #1)',\n",
       " 'Alas, Babylon',\n",
       " 'Alcatraz Versus the Evil Librarians (Alcatraz, #1)',\n",
       " 'Aleph',\n",
       " 'Alert (Michael Bennett, #8)',\n",
       " \"Alex Cross's Trial (Alex Cross, #15)\",\n",
       " 'Alex Cross, Run (Alex Cross, #20)',\n",
       " 'Alexander Hamilton',\n",
       " 'Alexander and the Terrible, Horrible, No Good, Very Bad Day',\n",
       " 'Alias Grace',\n",
       " 'Alice in Wonderland',\n",
       " 'Alice in Zombieland (White Rabbit Chronicles, #1)',\n",
       " \"Alice's Adventures in Wonderland\",\n",
       " \"Alice's Adventures in Wonderland & Other Stories\",\n",
       " \"Alice's Adventures in Wonderland & Through the Looking-Glass\",\n",
       " \"Alice's Adventures in Wonderland: A Pop-Up Adaptation\",\n",
       " 'Alien',\n",
       " 'Alienated (Alienated, #1)',\n",
       " 'Alif the Unseen',\n",
       " 'Alive: The Story of the Andes Survivors',\n",
       " 'All Around the Town',\n",
       " 'All But My Life: A Memoir',\n",
       " 'All Creatures Great and Small (All Creatures Great and Small, #1-2)',\n",
       " 'All Fall Down',\n",
       " 'All Families are Psychotic',\n",
       " 'All He Ever Wanted',\n",
       " 'All I Really Need to Know I Learned in Kindergarten',\n",
       " 'All I Want is Everything (Gossip Girl, #3)',\n",
       " 'All In (The Blackstone Affair, #2)',\n",
       " 'All My Friends Are Dead',\n",
       " 'All My Puny Sorrows',\n",
       " 'All My Sons',\n",
       " 'All Our Yesterdays',\n",
       " \"All Over But the Shoutin'\",\n",
       " 'All Quiet on the Western Front',\n",
       " 'All Souls: A Family Story from Southie',\n",
       " 'All That Remains (Kay Scarpetta, #3)',\n",
       " 'All Things Bright and Beautiful (All Creatures Great and Small)',\n",
       " 'All Things Wise and Wonderful',\n",
       " 'All Together Dead (Sookie Stackhouse, #7)',\n",
       " \"All Tomorrow's Parties (Bridge, #3)\",\n",
       " 'All You Need Is Kill',\n",
       " 'All the Birds in the Sky',\n",
       " 'All the Bright Places',\n",
       " \"All the King's Men\",\n",
       " 'All the Light We Cannot See',\n",
       " 'All the Missing Girls',\n",
       " 'All the Names',\n",
       " \"All the President's Men\",\n",
       " 'All the Pretty Horses (The Border Trilogy, #1)',\n",
       " 'All the Ugly and Wonderful Things',\n",
       " 'All the Weyrs of Pern (Pern, #11)',\n",
       " 'All-American Girl (All-American Girl, #1)',\n",
       " 'All-Star Superman, Vol. 1',\n",
       " 'All-Star Superman, Vol. 2',\n",
       " 'All-of-a-Kind Family (All-of-a-Kind Family, #1)',\n",
       " 'Allegiant (Divergent, #3)',\n",
       " 'Allies of the Night (Cirque du Freak, #8)',\n",
       " 'Almost French: Love and a New Life in Paris',\n",
       " 'Almost Heaven (Sequels, #3)',\n",
       " 'Aloha from Hell (Sandman Slim, #3)',\n",
       " 'Alone (Detective D.D. Warren, #1)',\n",
       " 'Along Came a Spider (Alex Cross, #1)',\n",
       " 'Along for the Ride',\n",
       " 'Altar of Eden',\n",
       " 'Altered (Altered, #1)',\n",
       " 'Altered Carbon (Takeshi Kovacs, #1)',\n",
       " 'Alvin Journeyman (Tales of Alvin Maker, #4)',\n",
       " 'Always Looking Up: The Adventures of an Incurable Optimist',\n",
       " 'Always You (Best Friend, #1)',\n",
       " 'Amadeus',\n",
       " 'Amazing Grace',\n",
       " 'Amazonia',\n",
       " 'Amelia Bedelia  (Amelia Bedelia #1)',\n",
       " 'Amelia Bedelia and the Surprise Shower',\n",
       " \"America (The Book): A Citizen's Guide to Democracy Inaction\",\n",
       " \"America's First Daughter\",\n",
       " 'American Assassin (Mitch Rapp, #1)',\n",
       " 'American Born Chinese',\n",
       " 'American Gods (American Gods, #1)',\n",
       " 'American Lion: Andrew Jackson in the White House',\n",
       " 'American Pastoral (The American Trilogy, #1)',\n",
       " 'American Psycho',\n",
       " 'American Sniper: The Autobiography of the Most Lethal Sniper in U.S. Military History',\n",
       " 'American Sphinx: The Character of Thomas Jefferson',\n",
       " 'American Tabloid (Underworld USA, #1)',\n",
       " 'American Vampire, Vol. 1',\n",
       " 'American Wife',\n",
       " 'American on Purpose: The Improbable Adventures of an Unlikely Patriot',\n",
       " 'Americanah',\n",
       " 'Amerika',\n",
       " 'Among Others',\n",
       " 'Among the Barons (Shadow Children, #4)',\n",
       " 'Among the Betrayed (Shadow Children, #3)',\n",
       " 'Among the Brave (Shadow Children, #5)',\n",
       " 'Among the Enemy (Shadow Children, #6)',\n",
       " 'Among the Hidden (Shadow Children, #1)',\n",
       " 'Among the Impostors (Shadow Children, #2)',\n",
       " 'Among the Mad (Maisie Dobbs, #6)',\n",
       " 'Among the Ten Thousand Things',\n",
       " 'Amphigorey (Amphigorey, #1)',\n",
       " 'Amsterdam',\n",
       " \"Amy & Roger's Epic Detour\",\n",
       " 'Amy and Isabelle ',\n",
       " 'An Abundance of Katherines',\n",
       " 'An Acceptable Time (A Wrinkle in Time Quintet, #5)',\n",
       " 'An American Tragedy',\n",
       " 'An Anthropologist on Mars: Seven Paradoxical Tales',\n",
       " 'An Army at Dawn: The War in North Africa, 1942-1943 (World War II Liberation Trilogy, #1)',\n",
       " 'An Artist of the Floating World',\n",
       " \"An Astronaut's Guide to Life on Earth\",\n",
       " 'An Echo in the Bone (Outlander, #7)',\n",
       " 'An Echo in the Darkness (Mark of the Lion, #2)',\n",
       " 'An Ember in the Ashes (An Ember in the Ashes, #1)',\n",
       " 'An Enquiry Concerning Human Understanding',\n",
       " 'An Essay Concerning Human Understanding',\n",
       " 'An Ice Cold Grave (Harper Connelly, #3)',\n",
       " 'An Ideal Husband',\n",
       " 'An Incomplete Revenge (Maisie Dobbs, #5)',\n",
       " 'An Instance of the Fingerpost',\n",
       " 'An Invisible Thread: The True Story of an 11-Year-Old Panhandler, a Busy Sales Executive, and an Unlikely Meeting with Destiny',\n",
       " 'An Object of Beauty',\n",
       " 'An Occurrence at Owl Creek Bridge',\n",
       " 'An Offer From a Gentleman (Bridgertons, #3)',\n",
       " 'An Officer and a Spy',\n",
       " 'An Old-Fashioned Girl',\n",
       " 'An Unquiet Mind: A Memoir of Moods and Madness',\n",
       " 'An Untamed State',\n",
       " 'Anansi Boys',\n",
       " 'Anastasia Krupnik (Anastasia Krupnik, #1)',\n",
       " 'Anathem',\n",
       " 'Anatomy of the Spirit: The Seven Stages of Power and Healing',\n",
       " 'Ancestors of Avalon (Avalon, #5)',\n",
       " 'Ancillary Justice (Imperial Radch #1)',\n",
       " 'Ancillary Mercy (Imperial Radch #3)',\n",
       " 'Ancillary Sword (Imperial Radch, #2)',\n",
       " 'And Another Thing...',\n",
       " 'And Ladies of the Club',\n",
       " 'And Then There Were None',\n",
       " 'And the Band Played On: Politics, People, and the AIDS Epidemic',\n",
       " 'And the Mountains Echoed',\n",
       " 'And to Think That I Saw it on Mulberry Street',\n",
       " 'Andy Goldsworthy: A Collaboration with Nature',\n",
       " 'Angel (Angel, #1)',\n",
       " 'Angel (Maximum Ride, #7)',\n",
       " 'Angel Falls',\n",
       " \"Angela's Ashes (Frank McCourt, #1)\",\n",
       " 'Angelfall (Penryn & the End of Days, #1)',\n",
       " 'Angelfire (Angelfire, #1)',\n",
       " 'Angelology (Angelology, #1)',\n",
       " 'Angels & Demons  (Robert Langdon, #1)',\n",
       " 'Angels Fall',\n",
       " 'Angels Flight (Harry Bosch, #6; Harry Bosch Universe, #7)',\n",
       " 'Angels and Demons / The Da Vinci Code (Robert Langdon, #1-2)',\n",
       " 'Angels in America',\n",
       " 'Angels in America, Part One: Millennium Approaches',\n",
       " 'Angels in America, Part Two: Perestroika',\n",
       " \"Angels' Blood (Guild Hunter, #1)\",\n",
       " 'Angle of Repose',\n",
       " 'Angry Housewives Eating Bon Bons',\n",
       " 'Angus, Thongs and Full-Frontal Snogging (Confessions of Georgia Nicolson, #1)',\n",
       " \"Anil's Ghost\",\n",
       " 'Animal Dreams',\n",
       " 'Animal Farm',\n",
       " 'Animal Farm / 1984',\n",
       " 'Animal, Vegetable, Miracle: A Year of Food Life',\n",
       " 'Animalia',\n",
       " 'Anna Dressed in Blood (Anna, #1)',\n",
       " 'Anna Karenina',\n",
       " 'Anna and the French Kiss (Anna and the French Kiss, #1)',\n",
       " 'Annabel (Delirium, #0.5)',\n",
       " 'Anne Frank : The Biography',\n",
       " 'Anne Frank Remembered: The Story of the Woman Who Helped to Hide the Frank Family',\n",
       " 'Anne Frank: Beyond the Diary - A Photographic Remembrance',\n",
       " \"Anne McCaffrey's Dragonflight #1\",\n",
       " \"Anne Rice's The Tale of the Body Thief (A Graphic Novel)\",\n",
       " \"Anne Rice's The Vampire Lestat: A Graphic Novel\",\n",
       " 'Anne of Avonlea (Anne of Green Gables, #2)',\n",
       " 'Anne of Green Gables (Anne of Green Gables, #1)',\n",
       " 'Anne of Ingleside (Anne of Green Gables, #6)',\n",
       " 'Anne of Windy Poplars (Anne of Green Gables, #4)',\n",
       " 'Anne of the Island (Anne of Green Gables, #3)',\n",
       " \"Anne's House of Dreams (Anne of Green Gables, #5)\",\n",
       " 'Annie on My Mind',\n",
       " 'Annihilation (Southern Reach, #1)',\n",
       " 'Another Brooklyn',\n",
       " 'Another Bullshit Night in Suck City',\n",
       " 'Another Fine Myth (Myth Adventures, #1)',\n",
       " 'Another Piece of My Heart',\n",
       " 'Another Roadside Attraction',\n",
       " 'Anthem',\n",
       " 'Antigone',\n",
       " 'Antigone (The Theban Plays, #3)',\n",
       " 'Antony and Cleopatra',\n",
       " 'Antrax (Voyage of the Jerle Shannara, #2)',\n",
       " 'Any Human Heart',\n",
       " \"Anya's Ghost\",\n",
       " 'Anybody Out There? (Walsh Family, #4)',\n",
       " 'Anyone But You',\n",
       " 'Apathy and Other Small Victories',\n",
       " 'Ape House',\n",
       " 'Apollyon (Covenant, #4)',\n",
       " 'Apollyon (Left Behind, #5)',\n",
       " 'Apology',\n",
       " 'Apple Tree Yard',\n",
       " 'Appointment in Samarra',\n",
       " 'Appointment with Death (Hercule Poirot, #19)',\n",
       " 'Apt Pupil',\n",
       " 'Arcadia',\n",
       " \"Archangel's Blade (Guild Hunter, #4)\",\n",
       " \"Archangel's Consort (Guild Hunter, #3)\",\n",
       " \"Archangel's Kiss (Guild Hunter, #2)\",\n",
       " \"Archer's Voice\",\n",
       " 'Are You Afraid of the Dark?',\n",
       " 'Are You My Mother?',\n",
       " \"Are You There God? It's Me, Margaret\",\n",
       " \"Are You There, Vodka? It's Me, Chelsea\",\n",
       " 'Area 7 (Shane Schofield, #2)',\n",
       " 'Ariel',\n",
       " 'Aristotle and Dante Discover the Secrets of the Universe (Aristotle and Dante Discover the Secrets of the Universe, #1)',\n",
       " 'Ark Angel (Alex Rider, #6)',\n",
       " 'Armada',\n",
       " \"Armageddon's Children (Genesis of Shannara, #1)\",\n",
       " 'Armageddon: The Cosmic Battle of the Ages (Left Behind, #11)',\n",
       " 'Armor',\n",
       " 'Around the World in Eighty Days',\n",
       " \"Arrow's Fall (Heralds of Valdemar, #3)\",\n",
       " \"Arrow's Flight (Heralds of Valdemar, #2)\",\n",
       " 'Arrows of the Queen (Heralds of Valdemar, #1)',\n",
       " 'Arsenic and Old Lace',\n",
       " 'Artemis Fowl (Artemis Fowl, #1)',\n",
       " 'Artemis Fowl Boxed Set, Bks 1-5 (Artemis Fowl, #1-5)',\n",
       " 'Arthur & George',\n",
       " 'Article 5 (Article 5, #1)',\n",
       " 'As Chimney Sweepers Come to Dust (Flavia de Luce, #7)',\n",
       " 'As I Lay Dying',\n",
       " 'As Sure as the Dawn (Mark of the Lion, #3)',\n",
       " 'As You Like It',\n",
       " 'As You Wish: Inconceivable Tales from the Making of The Princess Bride',\n",
       " 'As a Man Thinketh',\n",
       " 'As the Crow Flies',\n",
       " 'Ascend (Trylle, #3)',\n",
       " 'Ash',\n",
       " 'Ashes (Ashes Trilogy, #1)',\n",
       " 'Ashes of Midnight (Midnight Breed, #6)',\n",
       " 'Ashes to Ashes (Kovac and Liska, #1)',\n",
       " 'Ashfall (Ashfall, #1)',\n",
       " 'Ashley Bell',\n",
       " 'Ask and It Is Given: Learning to Manifest Your Desires',\n",
       " 'Ask the Dust (Arturo Bandini, #3)',\n",
       " 'Ask the Passengers',\n",
       " \"Assassin's Apprentice (Farseer Trilogy, #1)\",\n",
       " \"Assassin's Quest (Farseer Trilogy, #3)\",\n",
       " 'Assassination Vacation',\n",
       " 'Assassins (Left Behind, #6)',\n",
       " 'Assholes Finish First (Tucker Max, #2)',\n",
       " 'Asterios Polyp',\n",
       " 'Asterix the Gaul (Asterix, #1)',\n",
       " 'Astonish Me',\n",
       " 'Astonishing X-Men, Volume 1: Gifted',\n",
       " 'Asylum (Asylum, #1)',\n",
       " \"At Bertram's Hotel (Miss Marple, #11)\",\n",
       " 'At First Sight (Jeremy Marsh & Lexie Darnell, #2)',\n",
       " \"At Grave's End (Night Huntress, #3)\",\n",
       " 'At Home in Mitford (Mitford Years, #1)',\n",
       " 'At Home: A Short History of Private Life',\n",
       " \"At Peace (The 'Burg, #2)\",\n",
       " 'At the Mountains of Madness',\n",
       " 'At the Mountains of Madness and Other Tales of Terror',\n",
       " \"At the Water's Edge\",\n",
       " 'Atlantis Found (Dirk Pitt, #15)',\n",
       " 'Atlas Shrugged',\n",
       " 'Atonement',\n",
       " 'Attachments',\n",
       " 'Attack of the Deranged Mutant Killer Monster Snow Goons',\n",
       " 'Attack of the Fiend (The Last Apprentice / Wardstone Chronicles, #4)',\n",
       " 'Attack on Titan, Vol. 1 (Attack on Titan, #1)',\n",
       " 'Audrey Rose',\n",
       " 'August: Osage County',\n",
       " 'Aunt Julia and the Scriptwriter',\n",
       " 'Aurora',\n",
       " 'Austenland (Austenland, #1)',\n",
       " 'Authority (Southern Reach, #2)',\n",
       " 'Autobiography of a Face',\n",
       " 'Autobiography of a Yogi',\n",
       " 'Avalon High',\n",
       " 'Avoiding Commitment (Avoiding, #1)',\n",
       " 'Avoiding Responsibility (Avoiding, #2)',\n",
       " 'Await Your Reply',\n",
       " 'Awake at Dawn (Shadow Falls, #2)',\n",
       " 'Awaken the Giant Within: How to Take Immediate Control of Your Mental, Emotional, Physical and Financial Destiny!',\n",
       " 'Awakened (House of Night, #8)',\n",
       " 'Awakening the Buddha Within: Tibetan Wisdom for the Western World',\n",
       " 'Away Laughing on a Fast Camel (Confessions of Georgia Nicolson, #5)',\n",
       " 'Axel (Corps Security, #1)',\n",
       " 'Azincourt',\n",
       " 'Aztec (Aztec, #1)',\n",
       " 'B is for Burglar  (Kinsey Millhone, #2)',\n",
       " 'Babar the King',\n",
       " 'Babbitt',\n",
       " 'Babe: The Gallant Pig',\n",
       " 'Baby Proof',\n",
       " \"Babylon's Ashes (The Expanse, #6)\",\n",
       " 'Babyville',\n",
       " 'Back Roads',\n",
       " 'Back Spin (Myron Bolitar #4)',\n",
       " 'Back When We Were Grownups',\n",
       " 'Backstage Pass (Sinners on Tour, #1)',\n",
       " 'Bad Blood (Virgil Flowers, #4)',\n",
       " 'Bad Feminist',\n",
       " \"Bad Girls Don't Die (Bad Girls Don't Die, #1)\",\n",
       " 'Bad Kitty',\n",
       " 'Bad Luck and Trouble (Jack Reacher, #11)',\n",
       " 'Bad Monkey',\n",
       " 'Bad Moon Rising (Dark-Hunter #18; Were-Hunter #4; Hellchaser #2)',\n",
       " 'Bad Rep (Bad Rep, #1)',\n",
       " 'Bad Romeo (Starcrossed, #1)',\n",
       " 'Bad Science',\n",
       " 'Bad Things (Tristan & Danika, #1)',\n",
       " 'Bad for You (Sea Breeze, #7)',\n",
       " 'Bag of Bones',\n",
       " 'Bagombo Snuff Box',\n",
       " 'Ball Four',\n",
       " 'Ballet Shoes (Shoes, #1)',\n",
       " 'Balzac and the Little Chinese Seamstress',\n",
       " 'Bambi',\n",
       " \"Band of Brothers: E Company, 506th Regiment, 101st Airborne from Normandy to Hitler's Eagle's Nest\",\n",
       " 'Barbarians at the Gate: The Fall of RJR Nabisco',\n",
       " 'Bare Bones (Temperance Brennan, #6)',\n",
       " 'Bared to You (Crossfire, #1)',\n",
       " 'Barefoot',\n",
       " 'Barefoot Contessa Back to Basics',\n",
       " 'Barefoot Contessa Family Style: Easy Ideas and Recipes That Make Everyone Feel Like Family',\n",
       " \"Barefoot Contessa at Home: Everyday Recipes You'll Make Over and Over Again\",\n",
       " 'Barefoot in Paris',\n",
       " 'Barely Breathing (Breathing, #2)',\n",
       " 'Barnyard Dance',\n",
       " 'Barrayar (Vorkosigan Saga, #7)',\n",
       " 'Barrel Fever: Stories and Essays',\n",
       " 'Bartleby the Scrivener',\n",
       " 'Basket Case',\n",
       " 'Bastard Out of Carolina',\n",
       " 'Batman, Volume 1: The Court of Owls',\n",
       " 'Batman: A Death in the Family',\n",
       " 'Batman: Arkham Asylum - A Serious House on Serious Earth',\n",
       " 'Batman: Dark Victory',\n",
       " 'Batman: Hush',\n",
       " 'Batman: Hush, Vol. 1',\n",
       " 'Batman: Hush, Vol. 2',\n",
       " 'Batman: Knightfall, Vol. 1: Broken Bat',\n",
       " 'Batman: The Dark Knight Returns #1',\n",
       " 'Batman: The Dark Knight Returns (The Dark Knight Saga, #1)',\n",
       " 'Batman: The Dark Knight Strikes Again (The Dark Knight Saga, #2)',\n",
       " 'Batman: The Killing Joke',\n",
       " 'Batman: The Long Halloween',\n",
       " 'Batman: The Man Who Laughs',\n",
       " 'Batman: Whatever Happened to the Caped Crusader?',\n",
       " 'Batman: Year One',\n",
       " 'Battle Cry of Freedom',\n",
       " 'Battle Hymn of the Tiger Mother',\n",
       " 'Battle Royale',\n",
       " 'Battlefield Earth: A Saga of the Year 3000',\n",
       " 'Battlefield of the Mind: Winning the Battle in Your Mind',\n",
       " 'Baudolino',\n",
       " 'Bayou Moon (The Edge, #2)',\n",
       " 'Be Careful What You Wish For (The Clifton Chronicles, #4)',\n",
       " 'Be Frank With Me',\n",
       " 'Be Here Now',\n",
       " 'Be with Me (Wait for You, #2)',\n",
       " 'Beach Music',\n",
       " 'Beach Road',\n",
       " 'Beach Town',\n",
       " 'Beachcombers',\n",
       " 'Beaches',\n",
       " 'Bear Snores On',\n",
       " 'Bearing an Hourglass (Incarnations of Immortality, #2)',\n",
       " 'Beast Master, Vol. 1 (Beast Master, #1)',\n",
       " 'Beastly (Beastly, #1; Kendra Chronicles, #1)',\n",
       " 'Beat the Reaper (Peter Brown #1)',\n",
       " 'Beatrice and Virgil',\n",
       " 'Beautiful Bastard (Beautiful Bastard, #1)',\n",
       " 'Beautiful Beginning (Beautiful Bastard, #3.5)',\n",
       " 'Beautiful Bitch (Beautiful Bastard, #1.5)',\n",
       " 'Beautiful Bombshell (Beautiful Bastard, #2.5)',\n",
       " \"Beautiful Boy: A Father's Journey Through His Son's Addiction\",\n",
       " 'Beautiful Broken Rules (Broken, #1)',\n",
       " 'Beautiful Burn (The Maddox Brothers, #4)',\n",
       " 'Beautiful Chaos (Caster Chronicles, #3)',\n",
       " 'Beautiful Creatures (Caster Chronicles, #1)',\n",
       " 'Beautiful Darkness (Caster Chronicles, #2)',\n",
       " 'Beautiful Day',\n",
       " 'Beautiful Disaster (Beautiful, #1)',\n",
       " 'Beautiful Oblivion (The Maddox Brothers, #1)',\n",
       " 'Beautiful Player (Beautiful Bastard, #3)',\n",
       " 'Beautiful Redemption (Caster Chronicles, #4)',\n",
       " 'Beautiful Redemption (The Maddox Brothers, #2)',\n",
       " 'Beautiful Ruins',\n",
       " 'Beautiful Sacrifice (The Maddox Brothers, #3)',\n",
       " 'Beautiful Secret (Beautiful Bastard, #4)',\n",
       " 'Beautiful Stranger (Beautiful Bastard, #2)',\n",
       " 'Beautifully Damaged (Beautifully Damaged, #1)',\n",
       " 'Beauty Pop, Vol. 1 (Beauty Pop, #1)',\n",
       " 'Beauty Queens',\n",
       " 'Beauty and the Beast',\n",
       " 'Beauty and the Beast (Disney)',\n",
       " 'Beauty from Pain (Beauty, #1)',\n",
       " 'Beauty from Surrender (Beauty, #2)',\n",
       " \"Beauty's Punishment (Sleeping Beauty, #2)\",\n",
       " \"Beauty's Release (Sleeping Beauty, #3)\",\n",
       " 'Beauty: A Retelling of the Story of Beauty and the Beast',\n",
       " 'Because of Low (Sea Breeze, #2)',\n",
       " 'Because of Mr. Terupt',\n",
       " 'Because of Winn-Dixie',\n",
       " 'Bed of Roses (Bride Quartet, #2)',\n",
       " 'Bedtime for Frances',\n",
       " 'Bee Season',\n",
       " 'Beezus and Ramona (Ramona, #1)',\n",
       " 'Before I Die',\n",
       " 'Before I Fall',\n",
       " 'Before I Go',\n",
       " 'Before I Go to Sleep',\n",
       " 'Before Jamaica Lane (On Dublin Street, #3)',\n",
       " 'Before They Are Hanged (The First Law #2)',\n",
       " 'Before the Fall',\n",
       " 'Behemoth (Leviathan, #2)',\n",
       " 'Behind Closed Doors',\n",
       " 'Behind Her Eyes',\n",
       " 'Behind the Beautiful Forevers: Life, Death, and Hope in a Mumbai Undercity',\n",
       " 'Behind the Scenes at the Museum',\n",
       " 'Being Mortal: Medicine and What Matters in the End',\n",
       " 'Being Nikki (Airhead, #2)',\n",
       " 'Being a Green Mother (Incarnations of Immortality, #5)',\n",
       " 'Being and Nothingness',\n",
       " 'Being and Time',\n",
       " 'Bel Canto',\n",
       " 'Belgarath the Sorcerer',\n",
       " 'Believing Christ: The Parable of the Bicycle and Other Good News',\n",
       " 'Belinda',\n",
       " 'Bella Tuscany',\n",
       " 'Belly Laughs: The Naked Truth About Pregnancy and Childbirth',\n",
       " 'Belong to Me',\n",
       " 'Beloved',\n",
       " 'Belzhar',\n",
       " 'Ben-Hur: A Tale of the Christ',\n",
       " 'Beneath This Man (This Man, #2)',\n",
       " 'Beneath a Marble Sky',\n",
       " 'Benediction (Plainsong, #3)',\n",
       " 'Benjamin Franklin: An American Life',\n",
       " 'Beowulf',\n",
       " 'Bergdorf Blondes',\n",
       " 'Best Friends Forever',\n",
       " 'Best Kept Secret (The Clifton Chronicles, #3)',\n",
       " 'Best Served Cold',\n",
       " 'Bet Me',\n",
       " 'Betrayal in Death (In Death, #12)',\n",
       " 'Betrayals (Strange Angels, #2)',\n",
       " 'Betrayed (House of Night, #2)',\n",
       " 'Betsy-Tacy (Betsy-Tacy, #1)',\n",
       " 'Better Homes and Gardens New Cook Book  ',\n",
       " 'Better off Friends',\n",
       " 'Better than Life (Red Dwarf #2)',\n",
       " \"Better: A Surgeon's Notes on Performance\",\n",
       " \"Betty Crocker's Cookbook\",\n",
       " 'Between Shades of Gray',\n",
       " 'Between Sisters',\n",
       " 'Between a Rock and a Hard Place',\n",
       " 'Between the Lines (Between the Lines, #1)',\n",
       " 'Between the World and Me',\n",
       " 'Between, Georgia',\n",
       " 'Beyond Belief: My Secret Life Inside Scientology and My Harrowing Escape',\n",
       " 'Beyond Good and Evil',\n",
       " 'Beyond Reach (Grant County, #6)',\n",
       " 'Beyond the Grave (The 39 Clues #4)',\n",
       " 'Beyond the Highland Mist (Highlander, #1)',\n",
       " 'Beyond the Shadows (Night Angel, #3)',\n",
       " 'Big Bang: The Origin of the Universe',\n",
       " 'Big Boned (Heather Wells, #3)',\n",
       " 'Big Brother',\n",
       " 'Big Cherry Holler (Big Stone Gap, #2)',\n",
       " 'Big Fish',\n",
       " 'Big Girl Panties',\n",
       " 'Big Little Lies',\n",
       " 'Big Magic: Creative Living Beyond Fear',\n",
       " 'Big Red (Big Red, #1)',\n",
       " 'Big Stone Gap (Big Stone Gap, #1)',\n",
       " 'Big Sur',\n",
       " 'Big Trouble',\n",
       " 'Billions & Billions: Thoughts on Life and Death at the Brink of the Millennium',\n",
       " 'Billy Budd, Sailor',\n",
       " \"Billy Lynn's Long Halftime Walk\",\n",
       " 'Bird Box',\n",
       " 'Bird by Bird: Some Instructions on Writing and Life',\n",
       " 'Birds of America',\n",
       " 'Birds of a Feather (Maisie Dobbs, #2)',\n",
       " 'Birdsong',\n",
       " 'Birthmarked (Birthmarked, #1)',\n",
       " 'Birthright',\n",
       " 'Bite Club (The Morganville Vampires, #10)',\n",
       " 'Bite Me (A Love Story, #3)',\n",
       " 'Bite Me If You Can (Argeneau #6)',\n",
       " 'Bitten (Women of the Otherworld, #1)',\n",
       " 'Bitter Is the New Black: Confessions of a Condescending, Egomaniacal, Self-Centered Smartass, Or, Why You Should Never Carry A Prada Bag to the Unemployment Office',\n",
       " 'Bitterblue (Graceling Realm, #3)',\n",
       " 'Bittersweet',\n",
       " 'Black Beauty',\n",
       " 'Black Bird, Vol. 01 (Black Bird, #1)',\n",
       " 'Black Boy',\n",
       " 'Black Butler, Vol. 1 (Black Butler, #1)',\n",
       " 'Black Cherry Blues (Dave Robicheaux, #3)',\n",
       " 'Black Dawn (The Morganville Vampires, #12)',\n",
       " 'Black Elk Speaks: Being the Life Story of a Holy Man of the Oglala Sioux',\n",
       " 'Black Hawk Down',\n",
       " 'Black Hills',\n",
       " 'Black Hole',\n",
       " 'Black House (The Talisman, #2)',\n",
       " 'Black Ice',\n",
       " 'Black Lies',\n",
       " 'Black Like Me',\n",
       " 'Black List (Scot Harvath, #11)',\n",
       " 'Black Magic Sanction (The Hollows, #8)',\n",
       " 'Black Notice (Kay Scarpetta, #10)',\n",
       " 'Black Order (Sigma Force, #3)',\n",
       " 'Black Powder War (Temeraire, #3)',\n",
       " 'Black Rose (In the Garden, #2)',\n",
       " 'Black Sun Rising (The Coldfire Trilogy, #1)',\n",
       " 'Black Swan Green',\n",
       " 'Black Water (Pendragon, #5)',\n",
       " 'Black and Blue',\n",
       " 'Black-Eyed Susans',\n",
       " 'Black: The Birth of Evil (The Circle, #1)',\n",
       " 'Blackberry Wine',\n",
       " 'Blackberry Winter',\n",
       " 'Blackmoore',\n",
       " 'Blackout (All Clear, #1)',\n",
       " 'Blackout (Newsflesh Trilogy, #3)',\n",
       " 'Blackout: Remembering the Things I Drank to Forget',\n",
       " 'Blackwood Farm (The Vampire Chronicles, #9)',\n",
       " 'Blameless (Parasol Protectorate, #3)',\n",
       " 'Blankets',\n",
       " 'Blaze',\n",
       " 'Bleach, Volume 01',\n",
       " 'Bleachers',\n",
       " 'Bleak House',\n",
       " 'Bless Me, Ultima',\n",
       " 'Blessings',\n",
       " 'Blind Willow, Sleeping Woman',\n",
       " 'Blindness',\n",
       " 'Blindsight (Firefall, #1)',\n",
       " 'Blindsighted (Grant County, #1)',\n",
       " 'Blink',\n",
       " 'Blink: The Power of Thinking Without Thinking',\n",
       " 'Blockade Billy',\n",
       " 'Blood And Gold (The Vampire Chronicles, #8)',\n",
       " 'Blood Bound (Mercy Thompson, #2)',\n",
       " 'Blood Brothers (Sign of Seven, #1)',\n",
       " 'Blood Canticle (The Vampire Chronicles, #10)',\n",
       " 'Blood Cross (Jane Yellowrock, #2)',\n",
       " 'Blood Kiss (Black Dagger Legacy, #1)',\n",
       " \"Blood Magick (The Cousins O'Dwyer Trilogy, #3)\",\n",
       " 'Blood Memory',\n",
       " 'Blood Meridian, or the Evening Redness in the West',\n",
       " 'Blood Noir (Anita Blake, Vampire Hunter #16)',\n",
       " 'Blood Promise (Vampire Academy, #4)',\n",
       " 'Blood Red Road (Dust Lands, #1)',\n",
       " 'Blood Rites (The Dresden Files, #6)',\n",
       " 'Blood Rites (The Grey Wolves, #2)',\n",
       " \"Blood Song (Raven's Shadow, #1)\",\n",
       " 'Blood Work (Harry Bosch Universe, #8; Terry McCaleb #1)',\n",
       " 'Blood and Chocolate',\n",
       " 'Blood of Dragons (Rain Wild Chronicles, #4)',\n",
       " 'Blood of the Fold (Sword of Truth, #3)',\n",
       " 'Blood on Snow (Blood on Snow, #1)',\n",
       " 'Blood, Bones, and Butter: The Inadvertent Education of a Reluctant Chef',\n",
       " 'Bloodfever (Fever, #2)',\n",
       " 'Bloodhound (Beka Cooper, #2)',\n",
       " 'Bloodline',\n",
       " 'Bloodline (Sigma Force, #8)',\n",
       " 'Bloodlines (Bloodlines, #1)',\n",
       " 'Bloodrose (Nightshade #3; Nightshade World #6)',\n",
       " 'Bloodsucking Fiends (A Love Story, #1)',\n",
       " 'Bloody Bones (Anita Blake, Vampire Hunter #5)',\n",
       " 'Bloody Jack (Bloody Jack, #1)',\n",
       " 'Bloody Valentine (Blue Bloods, #5.5)',\n",
       " 'Blow Fly (Kay Scarpetta, #12)',\n",
       " 'Blowback (Scot Harvath, #4)',\n",
       " 'Blubber',\n",
       " 'Blue Bloods (Blue Bloods, #1)',\n",
       " 'Blue Dahlia (In the Garden, #1)',\n",
       " 'Blue Exorcist, Vol. 1  (Blue Exorcist, #1)',\n",
       " 'Blue Highways',\n",
       " 'Blue Labyrinth (Pendergast, #14)',\n",
       " 'Blue Like Jazz: Nonreligious Thoughts on Christian Spirituality',\n",
       " 'Blue Lily, Lily Blue (The Raven Cycle, #3)',\n",
       " 'Blue Mars (Mars Trilogy, #3)',\n",
       " 'Blue Moon (Anita Blake, Vampire Hunter, #8)',\n",
       " 'Blue Moon (The Immortals, #2)',\n",
       " 'Blue Nights',\n",
       " 'Blue Noon (Midnighters, #3)',\n",
       " 'Blue Ocean Strategy: How To Create Uncontested Market Space And Make The Competition Irrelevant',\n",
       " \"Blue Shoes and Happiness (No. 1 Ladies' Detective Agency, #7)\",\n",
       " 'Blue Smoke',\n",
       " 'Blue is for Nightmares (Blue is for Nightmares, #1)',\n",
       " 'Blue-Eyed Devil (Travises, #2)',\n",
       " 'Bluebeard',\n",
       " 'Blueberries for Sal',\n",
       " 'Body Double (Rizzoli & Isles, #4)',\n",
       " 'Body Movers (Body Movers, #1)',\n",
       " 'Body Surfing',\n",
       " 'Body of Evidence (Kay Scarpetta, #2)',\n",
       " 'Bone Crossed (Mercy Thompson, #4)',\n",
       " 'Bone Gap',\n",
       " 'Bone: The Complete Edition',\n",
       " 'Bones Are Forever (Temperance Brennan, #15)',\n",
       " 'Bones of the Hills (Conqueror, #3)',\n",
       " 'Bones to Ashes (Temperance Brennan, #10)',\n",
       " 'Boneshaker (The Clockwork Century, #1)',\n",
       " 'Bonhoeffer: Pastor, Martyr, Prophet, Spy',\n",
       " 'Bonjour tristesse',\n",
       " 'Bonk: The Curious Coupling of Science and Sex',\n",
       " 'Book of Mormon, Doctrine and Covenants, Pearl of Great Price',\n",
       " 'Book of Shadows (Sweep, #1)',\n",
       " 'Book of a Thousand Days',\n",
       " 'Book of the Dead (Kay Scarpetta, #15)',\n",
       " 'BookRags Summary:  A Storm of Swords',\n",
       " ...]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books=  list(each_book_rating)\n",
    "book_titles = [] \n",
    "for i in range(len(books)):\n",
    "    book_titles.append(books[i])\n",
    "book_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n"
     ]
    }
   ],
   "source": [
    "book = 'Black Hole'\n",
    "book_index = book_titles.index(book)\n",
    "corr_score = book_corr[book_index]\n",
    "print(sorted(corr_score, reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_recommendation(books_list):\n",
    "    book_similarities = np.zeros(book_corr.shape[0])\n",
    "    for book in books_list:    \n",
    "        book_index = book_titles.index(book)\n",
    "        book_similarities += book_corr[book_index] \n",
    "    book_preferences = []\n",
    "    for i in range(len(book_titles)):\n",
    "        book_preferences.append((book_titles[i],book_similarities[i]))        \n",
    "    return sorted(book_preferences, key= lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_fav_books = ['Black Hole','Attachments', 'The Kite Runner']\n",
    "book_recommendations = new_recommendation(my_fav_books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Angels (Walsh Family, #3)\n",
      "#GIRLBOSS\n",
      "'Salem's Lot\n",
      "'Tis (Frank McCourt, #2)\n",
      "1,000 Places to See Before You Die\n",
      "10% Happier: How I Tamed the Voice in My Head, Reduced Stress Without Losing My Edge, and Found Self-Help That Actually Works\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "count=0\n",
    "while count <= 5:\n",
    "    book_to_read = book_recommendations[i][0]\n",
    "    i += 1\n",
    "    if book_to_read in my_fav_books:\n",
    "        continue\n",
    "    else:\n",
    "        print(book_to_read)\n",
    "        count += 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}