Popularity Based Recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the files\n",
    "data = pd.read_csv('listing.csv', encoding = 'latin-1')\n",
    "books = pd.read_csv('books.csv', encoding = 'latin-1')\n"
   ]
  },
  {
   "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>genre</th>\n",
       "      <th>name</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4833</td>\n",
       "      <td>Biographies &amp; Memoirs</td>\n",
       "      <td>The Glass Castle</td>\n",
       "      <td>Jeannette Walls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>590</td>\n",
       "      <td>Biographies &amp; Memoirs</td>\n",
       "      <td>Night (The Night Trilogy, #1)</td>\n",
       "      <td>Elie Wiesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4264</td>\n",
       "      <td>Biographies &amp; Memoirs</td>\n",
       "      <td>Angela's Ashes (Frank McCourt, #1)</td>\n",
       "      <td>Frank McCourt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3361</td>\n",
       "      <td>Biographies &amp; Memoirs</td>\n",
       "      <td>Eat, Pray, Love</td>\n",
       "      <td>Elizabeth Gilbert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4535</td>\n",
       "      <td>Biographies &amp; Memoirs</td>\n",
       "      <td>Into Thin Air: A Personal Account of the Mount...</td>\n",
       "      <td>Jon Krakauer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id                  genre  \\\n",
       "0     4833  Biographies & Memoirs   \n",
       "1      590  Biographies & Memoirs   \n",
       "2     4264  Biographies & Memoirs   \n",
       "3     3361  Biographies & Memoirs   \n",
       "4     4535  Biographies & Memoirs   \n",
       "\n",
       "                                                name             author  \n",
       "0                                  The Glass Castle     Jeannette Walls  \n",
       "1                     Night (The Night Trilogy, #1)         Elie Wiesel  \n",
       "2                Angela's Ashes (Frank McCourt, #1)       Frank McCourt  \n",
       "3                                   Eat, Pray, Love   Elizabeth Gilbert  \n",
       "4  Into Thin Air: A Personal Account of the Mount...       Jon Krakauer  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using head() function to view first 5 rows for the object based on position. Just to test if we have right data.\n",
    "data.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>book_id</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>no_of_ratings</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4833</td>\n",
       "      <td>4.25</td>\n",
       "      <td>7156.0</td>\n",
       "      <td>3466</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>590</td>\n",
       "      <td>4.31</td>\n",
       "      <td>7821.0</td>\n",
       "      <td>3466</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4264</td>\n",
       "      <td>4.08</td>\n",
       "      <td>3836.0</td>\n",
       "      <td>3453</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3361</td>\n",
       "      <td>3.52</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>3453</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4535</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>3453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  avg_rating  no_of_ratings  user_id  user_rating\n",
       "0     4833        4.25         7156.0     3466            0\n",
       "1      590        4.31         7821.0     3466            5\n",
       "2     4264        4.08         3836.0     3453            5\n",
       "3     3361        3.52         1245.0     3453            4\n",
       "4     4535        4.13         3107.0     3453            0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.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>book_id</th>\n",
       "      <th>no_of_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>4755</td>\n",
       "      <td>9936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2409</td>\n",
       "      <td>9768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>2194</td>\n",
       "      <td>9754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>4696</td>\n",
       "      <td>9754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>1616</td>\n",
       "      <td>9542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>722</td>\n",
       "      <td>9542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>3004</td>\n",
       "      <td>9533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>3073</td>\n",
       "      <td>9533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>2240</td>\n",
       "      <td>9460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>433</td>\n",
       "      <td>9451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      book_id  no_of_ratings\n",
       "529      4755         9936.0\n",
       "707      2409         9768.0\n",
       "1252     2194         9754.0\n",
       "1384     4696         9754.0\n",
       "747      1616         9542.0\n",
       "713       722         9542.0\n",
       "1093     3004         9533.0\n",
       "1124     3073         9533.0\n",
       "2222     2240         9460.0\n",
       "282       433         9451.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting recommendation based on No. Of ratings \n",
    "rating_count = pd.DataFrame(books, columns=['book_id','no_of_ratings'])\n",
    "\n",
    "rating_count.sort_values('no_of_ratings', ascending=False).drop_duplicates().head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>genre</th>\n",
       "      <th>name</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4755</td>\n",
       "      <td>Law</td>\n",
       "      <td>One Man Out: Curt Flood versus Baseball (Landm...</td>\n",
       "      <td>Robert M. Goldman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2409</td>\n",
       "      <td>History</td>\n",
       "      <td>In Defense of History</td>\n",
       "      <td>Richard J. Evans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2194</td>\n",
       "      <td>Test Preparation</td>\n",
       "      <td>CLEP Micro &amp; Macro Economics Examinations Esse...</td>\n",
       "      <td>ExamREVIEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4696</td>\n",
       "      <td>Crafts, Hobbies &amp; Home</td>\n",
       "      <td>Mosaic Basics: Everything You Need to Know to ...</td>\n",
       "      <td>Teresa Mills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1616</td>\n",
       "      <td>Arts &amp; Photography</td>\n",
       "      <td>The Art of The Incredibles</td>\n",
       "      <td>Mark Cotta Vaz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id                   genre  \\\n",
       "0     4755                     Law   \n",
       "1     2409                 History   \n",
       "2     2194        Test Preparation   \n",
       "3     4696  Crafts, Hobbies & Home   \n",
       "4     1616      Arts & Photography   \n",
       "\n",
       "                                                name             author  \n",
       "0  One Man Out: Curt Flood versus Baseball (Landm...  Robert M. Goldman  \n",
       "1                              In Defense of History   Richard J. Evans  \n",
       "2  CLEP Micro & Macro Economics Examinations Esse...         ExamREVIEW  \n",
       "3  Mosaic Basics: Everything You Need to Know to ...       Teresa Mills  \n",
       "4                         The Art of The Incredibles     Mark Cotta Vaz  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the detail of most rated books\n",
    "most_rated_books = pd.DataFrame([4755, 2409, 2194, 4696, 1616], index=np.arange(5), columns=['book_id'])\n",
    "\n",
    "detail = pd.merge(most_rated_books, data, on='book_id')\n",
    "detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book_id          4999.0\n",
       "user_id          7131.0\n",
       "avg_rating          5.0\n",
       "no_of_ratings    9936.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the most rated book\n",
    "most_rated_book = pd.DataFrame(books, columns=['book_id', 'user_id', 'avg_rating', 'no_of_ratings'])\n",
    "most_rated_book.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>avg_rating</th>\n",
       "      <th>no_of_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2312.000000</td>\n",
       "      <td>2312.000000</td>\n",
       "      <td>2310.000000</td>\n",
       "      <td>2310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2515.098616</td>\n",
       "      <td>3108.624135</td>\n",
       "      <td>3.867026</td>\n",
       "      <td>2145.869264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1453.933954</td>\n",
       "      <td>1111.356799</td>\n",
       "      <td>1.010567</td>\n",
       "      <td>2746.151516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1257.000000</td>\n",
       "      <td>3221.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2610.000000</td>\n",
       "      <td>3471.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>660.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3729.000000</td>\n",
       "      <td>3476.000000</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>3608.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4999.000000</td>\n",
       "      <td>7131.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9936.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           book_id      user_id   avg_rating  no_of_ratings\n",
       "count  2312.000000  2312.000000  2310.000000    2310.000000\n",
       "mean   2515.098616  3108.624135     3.867026    2145.869264\n",
       "std    1453.933954  1111.356799     1.010567    2746.151516\n",
       "min       6.000000   117.000000     0.000000       0.000000\n",
       "25%    1257.000000  3221.000000     3.000000     101.000000\n",
       "50%    2610.000000  3471.000000     4.000000     660.500000\n",
       "75%    3729.000000  3476.000000     4.730000    3608.000000\n",
       "max    4999.000000  7131.000000     5.000000    9936.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting description for most rated book\n",
    "most_rated_book.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      1011\n",
       "unique                      992\n",
       "top       McGraw-Hill Education\n",
       "freq                          3\n",
       "Name: author, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can also get the description for author \n",
    "data['author'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}