correlation_based.ipynb

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    "colab": {
      "name": "correlation_based_recommendation_system.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RamKaushikR/Book-Recommendation-System/blob/master/correlation_based.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "AMz3WMK5PG2x",
        "colab_type": "code",
        "outputId": "a6cfb4a6-0a81-4eac-9829-5134dd622a4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "egpafV0blyjR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "id": "glQ4Q38lRJQo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/My Drive/Book Recommendation/listing.csv', encoding = 'latin-1')\n",
        "books = pd.read_csv('/content/gdrive/My Drive/Book Recommendation/books.csv', encoding = 'latin-1')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kAz6zoG8qe8k",
        "colab_type": "code",
        "outputId": "28b2abc2-f82b-4794-d6e6-bc9f52ea557f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "  </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",
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            ],
            "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  "
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "D4fnFuKsqgiT",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "books.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>avg_rating</th>\n",
              "      <th>no_of_ratings</th>\n",
              "      <th>user_id</th>\n",
              "      <th>user_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4833</td>\n",
              "      <td>4.25</td>\n",
              "      <td>7156.0</td>\n",
              "      <td>3466</td>\n",
              "      <td>0</td>\n",
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              "      <td>4.31</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>4.08</td>\n",
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              "    <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"
            ]
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          "execution_count": 5
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        "colab": {
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          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "rating = pd.DataFrame(books.groupby('book_id')['no_of_ratings'].mean())\n",
        "rating.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no_of_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>953.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2012.0</td>\n",
              "    </tr>\n",
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              "      <th>9</th>\n",
              "      <td>172.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3916.0</td>\n",
              "    </tr>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "         no_of_ratings\n",
              "book_id               \n",
              "6                953.0\n",
              "7               2012.0\n",
              "9                172.0\n",
              "15               118.0\n",
              "21              3916.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
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        "outputId": "f83d20ec-0101-49a1-f2e3-72b727c9f7c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "rating.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no_of_ratings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>708.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2040.384181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2666.449960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>90.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>629.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3225.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9936.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       no_of_ratings\n",
              "count     708.000000\n",
              "mean     2040.384181\n",
              "std      2666.449960\n",
              "min         0.000000\n",
              "25%        90.750000\n",
              "50%       629.000000\n",
              "75%      3225.250000\n",
              "max      9936.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
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        "colab": {
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          "height": 235
        },
        "outputId": "383e2546-2fd5-4000-eb45-23c2f8112492"
      },
      "cell_type": "code",
      "source": [
        "rating.sort_values('no_of_ratings', ascending = False).head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>no_of_ratings</th>\n",
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              "    <tr>\n",
              "      <th>book_id</th>\n",
              "      <th></th>\n",
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              "      <th>4755</th>\n",
              "      <td>9936.0</td>\n",
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              "      <th>2409</th>\n",
              "      <td>9768.0</td>\n",
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              "      <td>9754.0</td>\n",
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              "      <th>1616</th>\n",
              "      <td>9542.0</td>\n",
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            ],
            "text/plain": [
              "         no_of_ratings\n",
              "book_id               \n",
              "4755            9936.0\n",
              "2409            9768.0\n",
              "4696            9754.0\n",
              "2194            9754.0\n",
              "1616            9542.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
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          "height": 264
        },
        "outputId": "42ff5ee9-e3dc-4a12-b09c-ac614df3a1a1"
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      "cell_type": "code",
      "source": [
        "rating_pivot = pd.pivot_table(data = books, values = 'user_rating', index = 'user_id', columns = 'book_id')\n",
        "rating_pivot.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>book_id</th>\n",
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              "      <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>232</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>295</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>318</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 710 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "book_id  6     7     9     15    21    29    43    45    47    61    ...   \\\n",
              "user_id                                                              ...    \n",
              "117       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "176       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0   NaN  ...    \n",
              "232       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "295       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "318       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "\n",
              "book_id  4931  4941  4942  4968  4971  4975  4978  4991  4995  4999  \n",
              "user_id                                                              \n",
              "117       NaN   NaN   NaN   NaN   NaN   NaN   4.0   NaN   2.0   NaN  \n",
              "176       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "232       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "295       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "318       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "\n",
              "[5 rows x 710 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "wN7_FDmlDFMi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "17a3967d-e3e1-4878-862d-6772c9c6a91c"
      },
      "cell_type": "code",
      "source": [
        "corr = rating_pivot.corr()\n",
        "corr.head(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 710 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "book_id  6     7     9     15    21    29    43    45    47    61    ...   \\\n",
              "book_id                                                              ...    \n",
              "6         1.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "7         NaN   1.0   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN  ...    \n",
              "9         NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "15        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "21        NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "29        NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN  ...    \n",
              "43        NaN   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN  ...    \n",
              "45        NaN   1.0   NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN  ...    \n",
              "47        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   1.0   NaN  ...    \n",
              "61        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
              "\n",
              "book_id  4931  4941  4942  4968      4971  4975  4978  4991  4995  4999  \n",
              "book_id                                                                  \n",
              "6         NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "7         NaN   NaN   NaN   NaN  1.000000   NaN   NaN   NaN   NaN   NaN  \n",
              "9         NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "15        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "21        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "29        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "43        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "45        NaN   NaN   NaN   NaN  0.944911   NaN   NaN   NaN   NaN   NaN  \n",
              "47        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "61        NaN   NaN   NaN   NaN       NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "\n",
              "[10 rows x 710 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "78KYQSSXDdc2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f8f35ce8-3d5d-451b-b2db-e030abe29dab"
      },
      "cell_type": "code",
      "source": [
        "id = int(input('Enter book id '))\n",
        "one_rating = rating_pivot[id]\n",
        "one_rating[one_rating >= 0]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter book id 4755\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id\n",
              "577     2.0\n",
              "3472    4.0\n",
              "3476    2.0\n",
              "3483    3.0\n",
              "Name: 4755, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "6VA3_JRhD6wI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "0522f9c3-3698-4dda-c589-d1934147e741"
      },
      "cell_type": "code",
      "source": [
        "similar_rating = rating_pivot.corrwith(one_rating)\n",
        "corr_rating = pd.DataFrame(similar_rating, columns = ['PearsonR'])\n",
        "corr_rating.dropna(inplace = True)\n",
        "corr_rating.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py:3175: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
            "  c = cov(x, y, rowvar)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py:3109: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  c *= 1. / np.float64(fact)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PearsonR\n",
              "book_id          \n",
              "9       -0.981981\n",
              "481     -1.000000\n",
              "493      1.000000\n",
              "535     -1.000000\n",
              "755      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "DDzw2k5YEdUz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "423f734e-50a0-4a2a-b8f5-f7f100f0521d"
      },
      "cell_type": "code",
      "source": [
        "one_rating = corr_rating.join(rating)\n",
        "one_rating.sort_values('PearsonR', ascending = False).head(10)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PearsonR</th>\n",
              "      <th>no_of_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2629</th>\n",
              "      <td>1.0</td>\n",
              "      <td>635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>1.0</td>\n",
              "      <td>211.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4755</th>\n",
              "      <td>1.0</td>\n",
              "      <td>9936.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4571</th>\n",
              "      <td>1.0</td>\n",
              "      <td>323.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2900</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2027.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>1.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2681</th>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1676</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5374.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1431</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2380.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PearsonR  no_of_ratings\n",
              "book_id                         \n",
              "2629          1.0          635.0\n",
              "493           1.0          211.0\n",
              "4755          1.0         9936.0\n",
              "4571          1.0          323.0\n",
              "2900          1.0         2027.0\n",
              "1417          1.0           66.0\n",
              "2681          1.0           65.0\n",
              "1676          1.0         5374.0\n",
              "2913          1.0          160.0\n",
              "1431          1.0         2380.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "v6-hNmnTGGlg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "259c66ba-0789-465c-b52a-155ff10fef7c"
      },
      "cell_type": "code",
      "source": [
        "book_corr_OneManOut = pd.DataFrame([2629, 493, 4755, 4571, 2900, 1417, 2681, 1676, 2913, 1431], \n",
        "                      index = np.arange(10), columns=['book_id'])\n",
        "summary = pd.merge(book_corr_OneManOut, data,on='book_id')\n",
        "summary"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>genre</th>\n",
              "      <th>name</th>\n",
              "      <th>author</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2629</td>\n",
              "      <td>Travel</td>\n",
              "      <td>Moroccan Arabic: Lonely Planet Phrasebook</td>\n",
              "      <td>Dan Bacon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>493</td>\n",
              "      <td>Health, Fitness &amp; Dieting</td>\n",
              "      <td>The Gene Makeover: The 21st Century Anti-Aging...</td>\n",
              "      <td>Vincent C. Giampapa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4755</td>\n",
              "      <td>Law</td>\n",
              "      <td>One Man Out: Curt Flood versus Baseball (Landm...</td>\n",
              "      <td>Robert M. Goldman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4571</td>\n",
              "      <td>Teen &amp; Young Adult</td>\n",
              "      <td>The Story of the Tampa Bay Buccaneers (NFL Tod...</td>\n",
              "      <td>Sara Gilbert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2900</td>\n",
              "      <td>Travel</td>\n",
              "      <td>Robert Adams: The New West</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1417</td>\n",
              "      <td>Test Preparation</td>\n",
              "      <td>GED Exercise Books: Student Workbook Language ...</td>\n",
              "      <td>STECK-VAUGHN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2681</td>\n",
              "      <td>Arts &amp; Photography</td>\n",
              "      <td>The Industrial Design Reader</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1676</td>\n",
              "      <td>Science &amp; Math</td>\n",
              "      <td>Rare Earth Minerals: Chemistry, Origin and Ore...</td>\n",
              "      <td>A.P. Jones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2913</td>\n",
              "      <td>Travel</td>\n",
              "      <td>Avant-Guide Las Vegas: Insiders' Guide to Prog...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1431</td>\n",
              "      <td>Health, Fitness &amp; Dieting</td>\n",
              "      <td>Linchpin: Are You Indispensable?</td>\n",
              "      <td>Seth Godin</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   book_id                      genre  \\\n",
              "0     2629                     Travel   \n",
              "1      493  Health, Fitness & Dieting   \n",
              "2     4755                        Law   \n",
              "3     4571         Teen & Young Adult   \n",
              "4     2900                     Travel   \n",
              "5     1417           Test Preparation   \n",
              "6     2681         Arts & Photography   \n",
              "7     1676             Science & Math   \n",
              "8     2913                     Travel   \n",
              "9     1431  Health, Fitness & Dieting   \n",
              "\n",
              "                                                name               author  \n",
              "0          Moroccan Arabic: Lonely Planet Phrasebook            Dan Bacon  \n",
              "1  The Gene Makeover: The 21st Century Anti-Aging...  Vincent C. Giampapa  \n",
              "2  One Man Out: Curt Flood versus Baseball (Landm...    Robert M. Goldman  \n",
              "3  The Story of the Tampa Bay Buccaneers (NFL Tod...         Sara Gilbert  \n",
              "4                         Robert Adams: The New West                  NaN  \n",
              "5  GED Exercise Books: Student Workbook Language ...         STECK-VAUGHN  \n",
              "6                       The Industrial Design Reader                  NaN  \n",
              "7  Rare Earth Minerals: Chemistry, Origin and Ore...           A.P. Jones  \n",
              "8  Avant-Guide Las Vegas: Insiders' Guide to Prog...                  NaN  \n",
              "9                   Linchpin: Are You Indispensable?           Seth Godin  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    }
  ]
}