Study/goodbooks-10k/Books_Visualisation_and_recommendation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "52431e1b",
      "metadata": {
        "id": "52431e1b"
      },
      "source": [
        "# Books : Visualisation and recommendation\n",
        "[Kaggle | Books : Visualisation and recommendation](https://www.kaggle.com/code/nayansakhiya/books-visualisation-and-recommendation)   \n",
        "[Kaggle | Bookcrossing EDA & Visualization (Plotly)](https://www.kaggle.com/code/sanzharsamatuly/bookcrossing-eda-visualization-plotly)\n",
        "### DataSet \n",
        "- [goodbooks-10k](https://www.kaggle.com/datasets/zygmunt/goodbooks-10k)\n",
        "- [Book-Crossing: User review ratings](https://www.kaggle.com/datasets/ruchi798/bookcrossing-dataset?select=Book+reviews)\n",
        "---\n",
        "# 캐굴캐굴 필사 스터디 1주차\n",
        "#### 1주차 학습 기간 : 23.01.28 ~ 23.02.04   \n",
        "주제 : EDA    \n",
        "해당 데이터 셋에서 책 제목, 저자, 출판사, 사용자 및 등급에 대한 정보를 확인하여 사용자가 어떤 책을 읽고 있는지 인사이트를 얻어낸다.     \n",
        "1. About Dataset\n",
        "2. Data Visualization \n",
        "    \n",
        "## 복습 \n",
        "2023.03.26 \n",
        "- KNN을 이용한 책 추천 시스템 (커널 필사) "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9391d167",
      "metadata": {
        "id": "9391d167"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly_express"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uptna39SMcAl",
        "outputId": "a65331de-bf62-4147-c74c-c9da1ef3de69"
      },
      "id": "Uptna39SMcAl",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly_express in /usr/local/lib/python3.9/dist-packages (0.4.1)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (0.5.3)\n",
            "Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (1.4.4)\n",
            "Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (1.10.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (1.22.4)\n",
            "Requirement already satisfied: plotly>=4.1.0 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (5.13.1)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.9/dist-packages (from plotly_express) (0.13.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.20.0->plotly_express) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.20.0->plotly_express) (2022.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from patsy>=0.5->plotly_express) (1.16.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from plotly>=4.1.0->plotly_express) (8.2.2)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.9/dist-packages (from statsmodels>=0.9.0->plotly_express) (23.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ah4TNAKtMDKT",
        "outputId": "6b5a1e6f-9ccd-4617-c70a-8ddbad1a2b2a"
      },
      "id": "Ah4TNAKtMDKT",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8829bff0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "8829bff0",
        "outputId": "0eb6076d-9c13-4280-8333-f88704d70160"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import re\n",
        "import string\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "import plotly.graph_objs as go\n",
        "from plotly.offline import  init_notebook_mode, iplot\n",
        "init_notebook_mode(connected=True)\n",
        "\n",
        "import cufflinks as cf\n",
        "cf.go_offline()\n",
        "cf.set_config_file(offline=False, world_readable=True)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud,STOPWORDS\n",
        "import plotly_express as px\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3db845be",
      "metadata": {
        "id": "3db845be"
      },
      "source": [
        "- pandas : data processing, CSV file I/O\n",
        "- numpy : 선형대수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "a6530af9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6530af9",
        "outputId": "aa26d189-12c4-4f1e-dc4e-1c7a04f65e92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# root : /content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset\n",
            "dir: summary\n",
            "dir: reviews\n",
            "dir: goodbooks-10\n",
            "# root : /content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/summary\n",
            "file: Preprocessed_data.csv\n",
            "# root : /content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/reviews\n",
            "file: BX-Book-Ratings.csv\n",
            "file: BX-Users.csv\n",
            "file: BX_Books.csv\n",
            "# root : /content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10\n",
            "file: book_tags.csv\n",
            "file: books.csv\n",
            "file: ratings.csv\n",
            "file: tags.csv\n",
            "file: to_read.csv\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "root = f\"/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/\"\n",
        "data_dir = \"dataset\"\n",
        "for (root, dirs, files) in os.walk(root+data_dir):\n",
        "    print(\"# root : \" + root)\n",
        "    if len(dirs) > 0:\n",
        "        for dir_name in dirs:\n",
        "            print(\"dir: \" + dir_name)\n",
        "\n",
        "    if len(files) > 0:\n",
        "        for file_name in files:\n",
        "            print(\"file: \" + file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4b07fe1a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4b07fe1a",
        "outputId": "6ee5d2da-4054-41da-e611-0dbf90428634"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/summary/Preprocessed_data.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/reviews/BX-Book-Ratings.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/reviews/BX-Users.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/reviews/BX_Books.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/book_tags.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/books.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/ratings.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/tags.csv\n",
            "/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/to_read.csv\n"
          ]
        }
      ],
      "source": [
        "for dirname, _, filenames in os.walk('/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "2a220963",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a220963",
        "outputId": "c7b24d0c-72de-439b-aad5-a202d637cd17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/summary', '/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/reviews', '/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10']\n"
          ]
        }
      ],
      "source": [
        "from glob import glob\n",
        "\n",
        "print(glob('/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/*'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8257c495",
      "metadata": {
        "id": "8257c495"
      },
      "source": [
        "### Read .csv data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8cd19529",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cd19529",
        "outputId": "ab1d46c9-c296-4630-b731-537bbbd1e28f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-5747fa46a06c>:3: FutureWarning:\n",
            "\n",
            "The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# goodreads data\n",
        "goodbooks_path = '/content/drive/MyDrive/캐굴캐굴/goodbooks-10k/dataset/goodbooks-10/'\n",
        "books_data = pd.read_csv(f'{goodbooks_path}/books.csv',error_bad_lines = False)\n",
        "tags_data = pd.read_csv(f'{goodbooks_path}/book_tags.csv')\n",
        "ratings_data = pd.read_csv(f'{goodbooks_path}/ratings.csv')\n",
        "book_tags = pd.read_csv(f'{goodbooks_path}/tags.csv')\n",
        "\n",
        "# book crossing data\n",
        "book_path = '/content/drive/MyDrive/캐굴캐굴/goodbooks-10k//dataset/reviews'\n",
        "\n",
        "user_cols = ['user_id', 'location', 'age']\n",
        "cross_users_data = pd.read_csv(f'{book_path}/BX-Users.csv', sep=';', names=user_cols, encoding='latin-1', low_memory=False, skiprows=1)\n",
        "\n",
        "book_cols = ['isbn','book_title' ,'book_author','year_of_publication', 'publisher', 'img_s', 'img_m', 'img_l']\n",
        "cross_books_data = pd.read_csv(f'{book_path}/BX_Books.csv', sep=';', names=book_cols, encoding='latin-1', low_memory=False, skiprows=1)\n",
        "\n",
        "rating_cols = ['user_id', 'isbn', 'rating']\n",
        "cross_ratings_data = pd.read_csv(f'{book_path}/BX-Book-Ratings.csv', sep=';', names=rating_cols, encoding='latin-1', low_memory=False, skiprows=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "cff779bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "cff779bf",
        "outputId": "e37b6bbe-6935-40cf-aca7-c4dc6a456c7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-364f0569-fef7-47be-89de-d1eb2df50c07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <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>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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      ],
      "source": [
        "books_data.head()"
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    },
    {
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      "id": "454b452c",
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        {
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            "text/plain": [
              "   user_id                            location   age\n",
              "0        1                  nyc, new york, usa   NaN\n",
              "1        2           stockton, california, usa  18.0\n",
              "2        3     moscow, yukon territory, russia   NaN\n",
              "3        4           porto, v.n.gaia, portugal  17.0\n",
              "4        5  farnborough, hants, united kingdom   NaN"
            ],
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              "      <td>18.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>moscow, yukon territory, russia</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>farnborough, hants, united kingdom</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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      ],
      "source": [
        "cross_users_data.head()"
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    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "2b932e6b",
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            "text/plain": [
              "   user_id        isbn  rating\n",
              "0   276725  034545104X       0\n",
              "1   276726  0155061224       5\n",
              "2   276727  0446520802       0\n",
              "3   276729  052165615X       3\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-586ecf99-747e-4eeb-955f-ef791de1b78f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "cross_ratings_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9e81127f",
      "metadata": {
        "scrolled": true,
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          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "9e81127f",
        "outputId": "fda9b4c8-bace-4170-8d8b-1894899a83ba"
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              "         isbn                                         book_title  \\\n",
              "0  0195153448                                Classical Mythology   \n",
              "1  0002005018                                       Clara Callan   \n",
              "2  0060973129                               Decision in Normandy   \n",
              "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
              "4  0393045218                             The Mummies of Urumchi   \n",
              "\n",
              "            book_author  year_of_publication                publisher  \\\n",
              "0    Mark P. O. Morford                 2002  Oxford University Press   \n",
              "1  Richard Bruce Wright                 2001    HarperFlamingo Canada   \n",
              "2          Carlo D'Este                 1991          HarperPerennial   \n",
              "3      Gina Bari Kolata                 1999     Farrar Straus Giroux   \n",
              "4       E. J. W. Barber                 1999   W. W. Norton & Company   \n",
              "\n",
              "                                               img_s  \\\n",
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              "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "cross_books_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "937f55d2",
      "metadata": {
        "id": "937f55d2"
      },
      "source": [
        "### Drop unnecessary data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2a15cba8",
      "metadata": {
        "id": "2a15cba8"
      },
      "outputs": [],
      "source": [
        "books_data = books_data.drop(columns=['id', 'best_book_id', 'work_id', 'isbn', 'isbn13', 'title','work_ratings_count',\n",
        "                                   'work_text_reviews_count', 'ratings_1', 'ratings_2', 'ratings_3', 'ratings_4', 'ratings_5', \n",
        "                                    'image_url','small_image_url'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "813677c5",
      "metadata": {
        "id": "813677c5"
      },
      "outputs": [],
      "source": [
        "books_data = books_data.dropna()\n",
        "cross_books_data = cross_books_data.drop(columns=['img_s', 'img_m', 'img_l'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c24a006b",
      "metadata": {
        "id": "c24a006b"
      },
      "source": [
        "### Drop Duplicates from all the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "43ad4ac1",
      "metadata": {
        "id": "43ad4ac1"
      },
      "outputs": [],
      "source": [
        "ratings_data = ratings_data.sort_values(\"user_id\")\n",
        "\n",
        "ratings_data.drop_duplicates(subset =[\"user_id\",\"book_id\"], keep = False, inplace = True) \n",
        "books_data.drop_duplicates(subset='original_title',keep=False,inplace=True)\n",
        "book_tags.drop_duplicates(subset='tag_id',keep=False,inplace=True)\n",
        "tags_data.drop_duplicates(subset=['tag_id','goodreads_book_id'],keep=False,inplace=True)\n",
        "cross_ratings_data.drop_duplicates(subset =[\"user_id\",\"isbn\"], keep = False, inplace = True) \n",
        "cross_books_data.drop_duplicates(subset='book_title',keep=False,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ebd7146",
      "metadata": {
        "id": "1ebd7146"
      },
      "source": [
        "### Clean the text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "87d7e2af",
      "metadata": {
        "id": "87d7e2af"
      },
      "outputs": [],
      "source": [
        "def clean_text(text):\n",
        "    '''Make text lowercase,\n",
        "       remove text in square brackets,\n",
        "       remove links,remove punctuation\n",
        "       and remove words containing numbers.\n",
        "    '''\n",
        "    \n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "c00db089",
      "metadata": {
        "id": "c00db089"
      },
      "outputs": [],
      "source": [
        "cross_books_data['book_title'] = cross_books_data['book_title'].apply(lambda x:clean_text(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "60d6095e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "60d6095e",
        "outputId": "d2264b66-95f1-4e65-b309-1b27737e1a4d"
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        {
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              "              isbn                                         book_title  \\\n",
              "626418  0000913154  the way things work an illustrated encyclopedi...   \n",
              "587195  0001010565                                     mogs christmas   \n",
              "587194  0001010565                                     mogs christmas   \n",
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              "587194                    Judith Kerr                 1992   \n",
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          "metadata": {},
          "execution_count": 18
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      ],
      "source": [
        "merge_data = pd.merge(cross_books_data, cross_ratings_data, on='isbn')\n",
        "merge_data = merge_data.sort_values('isbn', ascending=True)\n",
        "merge_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "5f13accd",
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        "id": "5f13accd",
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              "    book_id  books_count                      authors  \\\n",
              "0   2767052          272              Suzanne Collins   \n",
              "1         3          491  J.K. Rowling, Mary GrandPré   \n",
              "3      2657          487                   Harper Lee   \n",
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              "\n",
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              "4                     1925.0                          The Great Gatsby   \n",
              "5                     2012.0                    The Fault in Our Stars   \n",
              "\n",
              "  language_code  average_rating  ratings_count  \n",
              "0           eng            4.34        4780653  \n",
              "1           eng            4.44        4602479  \n",
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          "metadata": {},
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        }
      ],
      "source": [
        "books_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fd97810",
      "metadata": {
        "id": "6fd97810"
      },
      "source": [
        "## Data Visualization \n",
        "### Good Reads Visualisation\n",
        "- goodbooks dataset 설명 \n",
        "\n",
        "```\n",
        "There have been good datasets for movies (Netflix, Movielens) and music (Million Songs) recommendation, but not for books. That is, until now.\n",
        "\n",
        "This dataset contains ratings for ten thousand popular books. As to the source, let's say that these ratings were found on the internet. Generally, there are 100 reviews for each book, although some have less - fewer - ratings. Ratings go from one to five.\n",
        "\n",
        "Both book IDs and user IDs are contiguous. For books, they are 1-10000, for users, 1-53424. All users have made at least two ratings. Median number of ratings per user is 8.\n",
        "\n",
        "There are also books marked to read by the users, book metadata (author, year, etc.) and tags.\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly\n",
        "plotly.__version__\n",
        "# 1.12.12"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LYWZDwJBN9Hc",
        "outputId": "c094ea15-18e8-481b-c51a-e57f3a88deb8"
      },
      "id": "LYWZDwJBN9Hc",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5.13.1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly_express as px"
      ],
      "metadata": {
        "id": "v4NMEgONOG25"
      },
      "id": "v4NMEgONOG25",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "c027b31d",
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        "colab": {
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          "height": 717
        },
        "id": "c027b31d",
        "outputId": "636c5ed2-edea-4237-f9bf-c74967bd8c8a"
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      "source": [
        "top_rated = books_data.sort_values('average_rating', ascending=False)\n",
        "top30 = top_rated[:30]\n",
        "\n",
        "fig = px.bar(top30, x=\"average_rating\", y=\"original_title\", title='Top 30 Rated Books and Their Ratings',\n",
        "             orientation='h', color='original_title', width=1500, height=700)\n",
        "fig.show(renderer=\"colab\")"
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              "                            \n",
              "var gd = document.getElementById('5973eb36-cc5a-41de-8848-68c8454238f2');\n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "top_popular = books_data.sort_values('ratings_count', ascending=False)\n",
        "top30_book = top_popular[:30]\n",
        "fig = px.bar(top30_book, x=\"ratings_count\", y=\"original_title\", title='Top 30 Popular Books', orientation='h', color='original_title',\n",
        "             width=1500, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "1dc661b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "1dc661b6",
        "outputId": "8f422de2-320e-4ff7-a01f-93300107e190"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ],
      "source": [
        "fig = px.treemap(top30_book, path=['original_title'], values='ratings_count',title='Top 30 Popular Books', width=1000, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "b32d220a",
      "metadata": {
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          "height": 717
        },
        "id": "b32d220a",
        "outputId": "ad85dbdf-f5ba-48df-8655-83c18ec91d27"
      },
      "outputs": [
        {
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              "                            \n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
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      ],
      "source": [
        "top_authors = top_rated[:50]\n",
        "fig = px.treemap(top_authors, path=['authors'], values='average_rating',title='Popular Authors', width=1000, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a2b5b16f",
      "metadata": {
        "id": "a2b5b16f"
      },
      "source": [
        "### Bookcrossing EDA\n",
        "- bookcrossing이란? \n",
        "    - 공공장소에 책을 남겨 놓아 다른 사람들도 그 책을 읽을 수 있도록 하는 일\n",
        "- 책을 읽은 사람이 리뷰를 남겨놓고 점수를 준 데이터를 모아둔 것 같음 \n",
        "    - 가장 많은 리뷰, 높은 점수 / 적은 리뷰, 적은 점수를 받은 책은 어떤 것이 있는지 알아보자!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0ff1e9e",
      "metadata": {
        "id": "c0ff1e9e"
      },
      "source": [
        "데이터 셋 설명\n",
        "```\n",
        "1. Books\n",
        "ISBN: book's unique id\n",
        "Book-Title: Title of book\n",
        "Book-Author: Author of book\n",
        "Publisher: Organization that published book\n",
        "Year-Of-Publication: published year\n",
        "Image-URL-S: url of book's image small size\n",
        "Image-URL-M: url of book's image medium size\n",
        "Image-URL-L: url of book's image large size\n",
        "\n",
        "2. Users\n",
        "User-ID: unique id for user\n",
        "Location: physicall address of user (city, state, country)\n",
        "Age: age of this user\n",
        "\n",
        "3. Book-Ratings:\n",
        "User-ID: id of user who rated\n",
        "ISBN: id of book that was rated\n",
        "Book-Rating: score that user gives to book (from 1 to 10)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "2c1a836f",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "2c1a836f",
        "outputId": "05bf4738-cb8e-43c9-d5b9-95cefa7ffda1"
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      "outputs": [
        {
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              "              isbn                                         book_title  \\\n",
              "626418  0000913154  the way things work an illustrated encyclopedi...   \n",
              "587195  0001010565                                     mogs christmas   \n",
              "587194  0001010565                                     mogs christmas   \n",
              "441049  0001046713                       twopence to cross the mersey   \n",
              "263949  000104687X     ts eliot reading the wasteland and other poems   \n",
              "\n",
              "                          book_author  year_of_publication  \\\n",
              "626418  C. van Amerongen (translator)                 1967   \n",
              "587195                    Judith Kerr                 1992   \n",
              "587194                    Judith Kerr                 1992   \n",
              "441049                Helen Forrester                 1992   \n",
              "263949                     T.S. Eliot                 1993   \n",
              "\n",
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              "441049  HarperCollins Publishers   196149       0  \n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "merge_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79041d26",
      "metadata": {
        "id": "79041d26"
      },
      "source": [
        "### Top Years of Publishing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "90bc5dad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "90bc5dad",
        "outputId": "aa2cafff-16c1-4ed9-b389-b4debdb8d0da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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year\",\"2001 year\",\"2003 year\",\"1999 year\",\"2000 year\",\"1998 year\",\"1996 year\",\"1997 year\",\"1995 year\",\"1994 year\",\"1993 year\",\"1992 year\",\"1991 year\",\"1990 year\",\"2004 year\",\"1989 year\",\"1988 year\",\"1987 year\",\"1986 year\",\"0 year\",\"1985 year\",\"1984 year\",\"1983 year\",\"1982 year\",\"1981 year\",\"1980 year\",\"1978 year\",\"1979 year\",\"1977 year\",\"1976 year\",\"1975 year\",\"1974 year\",\"1973 year\",\"1972 year\",\"1970 year\",\"1971 year\",\"1969 year\",\"1968 year\",\"1965 year\",\"1960 year\",\"1966 year\",\"1967 year\",\"1963 year\",\"1964 year\",\"1961 year\",\"1962 year\",\"1959 year\",\"1957 year\",\"1958 year\",\"1954 year\"]},\"legend\":{\"title\":{\"text\":\"value\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Top 20 Years of Publishing\"},\"barmode\":\"relative\",\"height\":700,\"width\":1000},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
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          "metadata": {}
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      ],
      "source": [
        "# Top Years of Publishing\n",
        "cross_typ = merge_data['year_of_publication'].value_counts().reset_index()\n",
        "cross_typ.columns = ['value', 'count']\n",
        "cross_typ['value'] = cross_typ['value'].astype(str) +' year'\n",
        "cross_typ = cross_typ.sort_values('count')\n",
        "fig = px.bar(cross_typ.tail(50), x=\"count\", y=\"value\", title='Top 20 Years of Publishing', orientation='h', color='value',\n",
        "             width=1000, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1bb57ef9",
      "metadata": {
        "id": "1bb57ef9"
      },
      "source": [
        "- 2000년 , 2002 ~ 2003년 출판된 책이 가장 많이 읽힘 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "641442bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "641442bb",
        "outputId": "0ada97b4-1b5d-411e-e564-1934bb626728"
      },
      "outputs": [
        {
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              "              isbn                                         book_title  \\\n",
              "626418  0000913154  the way things work an illustrated encyclopedi...   \n",
              "587195  0001010565                                     mogs christmas   \n",
              "587194  0001010565                                     mogs christmas   \n",
              "441049  0001046713                       twopence to cross the mersey   \n",
              "263949  000104687X     ts eliot reading the wasteland and other poems   \n",
              "...            ...                                                ...   \n",
              "413438  B0001FZGTO                             ill let you go a novel   \n",
              "575503  B0001I1KOG       new york public library literature companion   \n",
              "472829  B00029DGGO                         good wife strikes back the   \n",
              "543768  B0002JV9PY                               the blockade runners   \n",
              "543766  B0002K6K8O                               the underground city   \n",
              "\n",
              "                          book_author  year_of_publication  \\\n",
              "626418  C. van Amerongen (translator)                 1967   \n",
              "587195                    Judith Kerr                 1992   \n",
              "587194                    Judith Kerr                 1992   \n",
              "441049                Helen Forrester                 1992   \n",
              "263949                     T.S. Eliot                 1993   \n",
              "...                               ...                  ...   \n",
              "413438                   Bruce Wagner                 2002   \n",
              "575503        New York Public Library                 2001   \n",
              "472829               Elizabeth Buchan                    0   \n",
              "543768                    Jules Verne                    0   \n",
              "543766                    Jules Verne                    0   \n",
              "\n",
              "                       publisher  user_id  rating  \n",
              "626418          Simon & Schuster   171118       8  \n",
              "587195                   Collins   209516       0  \n",
              "587194                   Collins    86123       0  \n",
              "441049  HarperCollins Publishers   196149       0  \n",
              "263949  HarperCollins Publishers    23902       6  \n",
              "...                          ...      ...     ...  \n",
              "413438                   Villard   271705       0  \n",
              "575503                Free Press   148258      10  \n",
              "472829              Viking Adult   100088       0  \n",
              "543768             Digireads.com   179791       0  \n",
              "543766             Digireads.com   179791       0  \n",
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              "      <td>B0001I1KOG</td>\n",
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              "      <td>New York Public Library</td>\n",
              "      <td>2001</td>\n",
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              "      <td>good wife strikes back the</td>\n",
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              "      <td>B0002JV9PY</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 29
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      ],
      "source": [
        "merge_data"
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    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "82918ab9",
      "metadata": {
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          "height": 717
        },
        "id": "82918ab9",
        "outputId": "cbbb938f-bdbf-45f5-9f43-ee6fde84a0c2"
      },
      "outputs": [
        {
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              "                            \n",
              "var gd = document.getElementById('cb875ef6-e465-4e2f-abb1-70e215274602');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
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              "</body>\n",
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          },
          "metadata": {}
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      ],
      "source": [
        "# top authors(frequency of book)\n",
        "cross_author = merge_data['book_author'].value_counts().reset_index()\n",
        "cross_author.columns = ['value', 'count']\n",
        "\n",
        "cross_author['value'] = cross_author['value'].astype(str)\n",
        "cross_author = cross_author.sort_values('count')\n",
        "\n",
        "fig = px.bar(cross_author.tail(50), x=\"count\", y=\"value\", title='Top 20 Authors', orientation='h', color='value',\n",
        "             width=1000, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "22151ea7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "22151ea7",
        "outputId": "dfdf191e-c0be-4b20-e7b4-cd5a903d9b92"
      },
      "outputs": [
        {
          "output_type": "display_data",
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      ],
      "source": [
        "top_book = merge_data['book_title'].value_counts().reset_index()\n",
        "top_book.columns = ['value', 'count']\n",
        "top_book['value'] = top_book['value']\n",
        "top_book = top_book.sort_values('count')\n",
        "fig = px.bar(top_book.tail(20), x=\"count\", y=\"value\", title='Top 20 Books', orientation='h', color='value',\n",
        "             width=1000, height=700)\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "f79fc3ef",
      "metadata": {
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          "height": 717
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        "id": "f79fc3ef",
        "outputId": "e0863b07-1242-4169-f193-587bfeb7ad46"
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      "outputs": [
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club\",\"marker\":{\"color\":\"#FF6692\",\"pattern\":{\"shape\":\"\"}},\"name\":\"white oleander  a novel oprahs book club\",\"offsetgroup\":\"white oleander  a novel oprahs book club\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[356],\"xaxis\":\"x\",\"y\":[\"white oleander  a novel oprahs book club\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"the divine secrets of the yaya sisterhood a novel\",\"marker\":{\"color\":\"#B6E880\",\"pattern\":{\"shape\":\"\"}},\"name\":\"the divine secrets of the yaya sisterhood a novel\",\"offsetgroup\":\"the divine secrets of the yaya sisterhood a novel\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[376],\"xaxis\":\"x\",\"y\":[\"the divine secrets of the yaya sisterhood a novel\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"midwives a novel\",\"marker\":{\"color\":\"#FF97FF\",\"pattern\":{\"shape\":\"\"}},\"name\":\"midwives a novel\",\"offsetgroup\":\"midwives a novel\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[380],\"xaxis\":\"x\",\"y\":[\"midwives a novel\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"the no  ladies detective agency today show book club \",\"marker\":{\"color\":\"#FECB52\",\"pattern\":{\"shape\":\"\"}},\"name\":\"the no  ladies detective agency today show book club \",\"offsetgroup\":\"the no  ladies detective agency today show book club \",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[431],\"xaxis\":\"x\",\"y\":[\"the no  ladies detective agency today show book club \"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"little altars everywhere a novel\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"little altars everywhere a novel\",\"offsetgroup\":\"little altars everywhere a novel\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[434],\"xaxis\":\"x\",\"y\":[\"little altars everywhere a novel\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"shes come undone oprahs book club paperback\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"shes come undone oprahs book club paperback\",\"offsetgroup\":\"shes come undone oprahs book club paperback\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[446],\"xaxis\":\"x\",\"y\":[\"shes come undone oprahs book club paperback\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"shes come undone oprahs book club\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"shes come undone oprahs book club\",\"offsetgroup\":\"shes come undone oprahs book club\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[468],\"xaxis\":\"x\",\"y\":[\"shes come undone oprahs book club\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"the poisonwood bible a novel\",\"marker\":{\"color\":\"#ab63fa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"the poisonwood bible a novel\",\"offsetgroup\":\"the poisonwood bible a novel\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[494],\"xaxis\":\"x\",\"y\":[\"the poisonwood bible a novel\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"girl with a pearl earring\",\"marker\":{\"color\":\"#FFA15A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"girl with a pearl earring\",\"offsetgroup\":\"girl with a pearl earring\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[526],\"xaxis\":\"x\",\"y\":[\"girl with a pearl earring\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"the pilots wife  a novel\",\"marker\":{\"color\":\"#19d3f3\",\"pattern\":{\"shape\":\"\"}},\"name\":\"the pilots wife  a novel\",\"offsetgroup\":\"the pilots wife  a novel\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[568],\"xaxis\":\"x\",\"y\":[\"the pilots wife  a novel\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"where the heart is oprahs book club paperback\",\"marker\":{\"color\":\"#FF6692\",\"pattern\":{\"shape\":\"\"}},\"name\":\"where the heart is oprahs book club paperback\",\"offsetgroup\":\"where the heart is oprahs book club paperback\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[585],\"xaxis\":\"x\",\"y\":[\"where the heart is oprahs book club paperback\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"value=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"the red tent bestselling backlist\",\"marker\":{\"color\":\"#B6E880\",\"pattern\":{\"shape\":\"\"}},\"name\":\"the red tent bestselling backlist\",\"offsetgroup\":\"the red tent bestselling backlist\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[723],\"xaxis\":\"x\",\"y\":[\"the red 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    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('42273029-c12d-4007-bede-306ca2466ffb');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# with publishers\n",
        "by_publisher = merge_data.groupby('publisher')['isbn'].count()\n",
        "by_publisher = by_publisher.sort_values(ascending = False)\n",
        "\n",
        "figure = go.Figure(data = [go.Pie(labels = by_publisher.index[:10], \n",
        "                                  values = by_publisher.values,\n",
        "                                  textinfo='label+percent',\n",
        "                                  title= 'Ten publsihers with most published books.' \n",
        "                                 )])\n",
        "\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "0cc9f7a0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cc9f7a0",
        "outputId": "5128964e-65db-462b-d5b0-0bf670936407"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ten most popular publishers published: 131359, Others: 501928\n"
          ]
        }
      ],
      "source": [
        "print(f'Ten most popular publishers published: {sum(by_publisher.values[:10])}, Others: {sum(by_publisher.values[11:])}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "0ccbd439",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ccbd439",
        "outputId": "d501d56a-2359-4373-b651-e960e1c51d6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ],
      "source": [
        "# with book title\n",
        "stopwords.words('english')\n",
        "print(stopwords.words('english'))\n",
        "# Here I want to split every title of book to words and check whereas this word length greater than 3 or it's not in stop words.\n",
        "# If everything is fine, then I will add that word into array."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "7e3a1998",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e3a1998",
        "outputId": "7fb2898e-3f51-4c65-98fa-16d5cc67fb0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "626418    None\n",
              "587195    None\n",
              "587194    None\n",
              "441049    None\n",
              "263949    None\n",
              "          ... \n",
              "413438    None\n",
              "575503    None\n",
              "472829    None\n",
              "543768    None\n",
              "543766    None\n",
              "Name: book_title, Length: 641731, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "from collections import Counter\n",
        "results = Counter()\n",
        "merge_data['book_title'].str.lower().str.split().apply(results.update)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "9e04ad41",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e04ad41",
        "outputId": "c78303a3-697f-469c-9c53-aa40322d9a01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:06<00:00, 165.92it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm \n",
        "most_common = dict(results.most_common(1000))\n",
        "\n",
        "to_replace = ['(', ')', '*', '((', '))']\n",
        "own_stop_words = ['book', 'guide', 'novel']\n",
        "\n",
        "top_words = {}\n",
        "\n",
        "for k, v in tqdm(most_common.items()):\n",
        "    \n",
        "    for err in to_replace:\n",
        "        k = k.replace(err, \"\")\n",
        "        \n",
        "    if (len(k) > 3) and (k not in stopwords.words()) and (k not in own_stop_words):\n",
        "        top_words[k] = v"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b9ee9a1",
      "metadata": {
        "id": "0b9ee9a1"
      },
      "source": [
        "- 책의 모든 제목을 단어로 나누고 이 단어의 길이가 3보다 크고 stopword에 포함되지 않은 단어 수집 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "9ddfe479",
      "metadata": {
        "id": "9ddfe479"
      },
      "outputs": [],
      "source": [
        "# top_words, Sort dictionary by values\n",
        "top_words = dict(sorted(top_words.items(), key=lambda item: item[1])[::-1][:20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "d5d22e16",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "d5d22e16",
        "outputId": "8b0de85b-b618-4815-e02b-ed2aef47752d"
      },
      "outputs": [
        {
          "output_type": "display_data",
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    ).then(function(){\n",
              "                            \n",
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              "            Plotly.purge(gd);\n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "figure = go.Figure(data = [go.Pie(labels = list(top_words.keys()), \n",
        "                                  values = list(top_words.values()),\n",
        "                                  textinfo='label+percent',\n",
        "                                  title= 'Twenty most used words in book titles.' \n",
        "                                 )])\n",
        "\n",
        "fig.show(renderer=\"colab\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "05b04b1a",
      "metadata": {
        "id": "05b04b1a"
      },
      "source": [
        "- 미스테리, 시리즈, 생활, 클래식, 스토리 등등 가장 많이 사용되는 단어 20개를 시각화"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation System Using KNN \n",
        "### 필사 커널 \n",
        "- https://www.kaggle.com/code/sunilgautam/recommendation-system-using-knn\n"
      ],
      "metadata": {
        "id": "MCOZNwClQNSB"
      },
      "id": "MCOZNwClQNSB"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 협업 필터링 - KNN\n",
        "\n",
        "### 협업 필터링이란? \n",
        "- 협업 필터링은 사용자와 아이템 간의 상호 상관 관계를 분석하여 새로운 사용자-아이템 관계를 찾아주는 것\n",
        "- 사용자의 과거 경험과 행동 방식(User Behavior)에 의존하여 추천하는 시스템"
      ],
      "metadata": {
        "id": "f_6VW-OdQYEe"
      },
      "id": "f_6VW-OdQYEe"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import csr_matrix\n",
        "from sklearn.neighbors import NearestNeighbors"
      ],
      "metadata": {
        "id": "Lu7GUvYvQnCw"
      },
      "id": "Lu7GUvYvQnCw",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "books_data = pd.read_csv(f'{goodbooks_path}/books.csv')\n",
        "ratings_data = pd.read_csv(f'{goodbooks_path}/ratings.csv')"
      ],
      "metadata": {
        "id": "5o_8QI9hRE5l"
      },
      "id": "5o_8QI9hRE5l",
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns',None)"
      ],
      "metadata": {
        "id": "5XFDLrQwQuli"
      },
      "id": "5XFDLrQwQuli",
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rrmWpUesRAsq",
        "outputId": "e21fbdc3-3759-4744-dd37-4907518e98d6"
      },
      "id": "rrmWpUesRAsq",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
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    {
      "cell_type": "markdown",
      "source": [
        "### Clean the data"
      ],
      "metadata": {
        "id": "4nDG8BxuRm7h"
      },
      "id": "4nDG8BxuRm7h"
    },
    {
      "cell_type": "code",
      "source": [
        "books_data = books_data.dropna()"
      ],
      "metadata": {
        "id": "28RHWycxRexa"
      },
      "id": "28RHWycxRexa",
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings = ratings_data.sort_values(\"user_id\")\n",
        "ratings.drop_duplicates(subset=[\"user_id\",\"book_id\"], keep='first', inplace=True) \n",
        "\n",
        "books_data.drop_duplicates(subset='original_title', keep='first', inplace=True)"
      ],
      "metadata": {
        "id": "hMY9jo0OR5O9"
      },
      "id": "hMY9jo0OR5O9",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.merge(books_data, ratings, how='left', left_on=['id'], right_on=['book_id'])\n",
        "df = merged_df[['id','original_title', 'user_id', 'rating']]\n",
        "\n",
        "df = df.rename(columns = {'id':'book_id'})\n",
        "df.head(200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3ci6MMwoRxcq",
        "outputId": "fde7a30e-31c1-43b2-a4bd-9a541fa41825"
      },
      "id": "3ci6MMwoRxcq",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     book_id                            original_title  user_id  rating\n",
              "0          1                          The Hunger Games      314       5\n",
              "1          1                          The Hunger Games      439       3\n",
              "2          1                          The Hunger Games      588       5\n",
              "3          1                          The Hunger Games     1169       4\n",
              "4          1                          The Hunger Games     1185       4\n",
              "..       ...                                       ...      ...     ...\n",
              "195        2  Harry Potter and the Philosopher's Stone    51166       5\n",
              "196        2  Harry Potter and the Philosopher's Stone    51460       4\n",
              "197        2  Harry Potter and the Philosopher's Stone    51838       4\n",
              "198        2  Harry Potter and the Philosopher's Stone    52036       3\n",
              "199        2  Harry Potter and the Philosopher's Stone    53292       5\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
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              "\n",
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              "      <th>0</th>\n",
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              "      <td>The Hunger Games</td>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>439</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>588</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>1169</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>1185</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>195</th>\n",
              "      <td>2</td>\n",
              "      <td>Harry Potter and the Philosopher's Stone</td>\n",
              "      <td>51166</td>\n",
              "      <td>5</td>\n",
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              "      <th>196</th>\n",
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              "      <td>4</td>\n",
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              "<p>200 rows × 4 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Tul0HkSdSE_4",
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      },
      "id": "Tul0HkSdSE_4",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             book_id        user_id         rating\n",
              "count  763421.000000  763421.000000  763421.000000\n",
              "mean     4657.948579   25132.161966       3.852257\n",
              "std      2875.106491   15229.803682       0.980928\n",
              "min         1.000000       1.000000       1.000000\n",
              "25%      2123.000000   11843.000000       3.000000\n",
              "50%      4509.000000   24230.000000       4.000000\n",
              "75%      7087.000000   38032.000000       5.000000\n",
              "max      9999.000000   53424.000000       5.000000"
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              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>user_id</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>763421.000000</td>\n",
              "      <td>763421.000000</td>\n",
              "      <td>763421.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4657.948579</td>\n",
              "      <td>25132.161966</td>\n",
              "      <td>3.852257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2875.106491</td>\n",
              "      <td>15229.803682</td>\n",
              "      <td>0.980928</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2123.000000</td>\n",
              "      <td>11843.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4509.000000</td>\n",
              "      <td>24230.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7087.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "ratings_df = df.pivot_table(index='book_id', columns ='user_id', values='rating').fillna(0)\n",
        "pd.set_option('display.max_columns', 100)\n",
        "ratings_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "i_E3wbNoSTh2",
        "outputId": "b98382f5-f173-44fa-f34e-267614a5a968"
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      "id": "i_E3wbNoSTh2",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id  1      2      3      4      5      6      7      8      9      10     \\\n",
              "book_id                                                                         \n",
              "1          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "2          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "3          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "4          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "5          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "user_id  11     12     13     14     15     16     17     18     19     20     \\\n",
              "book_id                                                                         \n",
              "1          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "2          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "3          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "4          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "5          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "user_id  21     22     23     24     25     26     27     28     29     30     \\\n",
              "book_id                                                                         \n",
              "1          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "2          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
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              "user_id  31     32     33     34     35     36     37     38     39     40     \\\n",
              "book_id                                                                         \n",
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              "book_id                                                                         \n",
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              "\n",
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              "book_id  ...                                                                  \n",
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              "book_id                                                                         \n",
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              "book_id                                                                         \n",
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              "book_id                                                                         \n",
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              "book_id                                                                         \n",
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              "[5 rows x 50681 columns]"
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 50681 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-974306df-6702-4a5a-a369-dd13ba177779')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-974306df-6702-4a5a-a369-dd13ba177779 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-974306df-6702-4a5a-a369-dd13ba177779');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQOYDBDBSlm-",
        "outputId": "3974f4a0-d263-4888-ff36-4a7bcf9a279b"
      },
      "id": "xQOYDBDBSlm-",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50681, 7755)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN 협업 필터링 \n",
        "KNN(K-Nearest Neighbor)\n",
        "K명의 최근접 이웃에 기반해서 찾는 방법으로, 널리 쓰이는 방법이다.    \n",
        "사용자가 준 평점으로 유사한 사람의 아이템을 찾거나, 유사한 아이템을 찾아 추천을 한다.     \n",
        "편향을 제거(전반적으로 평점을 후하게 주거나 적게 주는 경우를 방지)해주기 위해 비교군의 평점을 더해주거나 빼주어 동일하게 해준다.    \n",
        "\n",
        "추천 리스트에 새로운 사용자 또는 아이템이 오더라도 안정정으로 진행이 가능하다.    \n",
        "방법이 간단하고 직관적이어서 접근이 용이하다.    \n",
        "유저 기반의 방법 및 속도, 메모리가 많이 든다.     \n",
        "희소성으로 인한 제약이 발생한다. (유사한 이웃이 사용한 경험이 없으면 추천 불가능하다)     \n",
        "\n",
        "\n",
        "### 알고리즘 구현 \n",
        "\n",
        "아이템 id와 사용자 id를 행과 열로 하는 매트릭스를 생성하고,  코사인 유사도를 이용해 특정 사용자 가장 유사한 사용자를 찾아낸다. "
      ],
      "metadata": {
        "id": "BW6fb_t5TXYl"
      },
      "id": "BW6fb_t5TXYl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 코사인 유사도 공부 "
      ],
      "metadata": {
        "id": "w7ZOHPGRVY2s"
      },
      "id": "w7ZOHPGRVY2s"
    },
    {
      "cell_type": "code",
      "source": [
        "# 샘플 코드 \n",
        "# 여기서 코사인 유사도를 이용해 특정한 사용자와와 가장 유사한 사용자를 찾아낼 것이다.\n",
        "\n",
        "def cosine_simillarity(v1, v2):\n",
        "  A = np.sqrt(np.sum(np.square(v1)))\n",
        "  B = np.sqrt(np.sum(np.square(v2)))\n",
        "  return np.dot(v1,v2) / (A*B)"
      ],
      "metadata": {
        "id": "es_s24nXU1E5"
      },
      "id": "es_s24nXU1E5",
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "print(random.randint(1,53424))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xR0W3nSeYZP3",
        "outputId": "8e503021-df54-47ca-bef5-cf68ef3c1455"
      },
      "id": "xR0W3nSeYZP3",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ratings_df.to_numpy()"
      ],
      "metadata": {
        "id": "kTT7s3gSZtrP"
      },
      "id": "kTT7s3gSZtrP",
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # 특정 사용자의 코사인 유사도\n",
        "\n",
        "# sim_lst = []  # 빈 리스트 생성\n",
        "# best_score = 0\n",
        "# mat = ratings_df.to_numpy()\n",
        "# for idx, vec in enumerate(mat):  # 매트릭스의 각 사용자 별 벡터를 뽑아 vec에 넣기\n",
        "\n",
        "#     similarity = cosine_simillarity( vec, mat[51166]) # 사용자23568의 벡터\n",
        "#     sim_lst.append((idx,similarity))  \n",
        "#     if idx != 2 and best_score < similarity:  # 현재 계산한 유사도가 기존 최고 유사도보다 높다면 바꿔준다. \n",
        "#       best_score = similarity\n",
        "#       best_user = idx +1   \n",
        "# print(sim_lst, f\"\\n사용자 23568과 가장 비슷한 유저: 사용자{best_user} \\n유사도:{best_score}\")"
      ],
      "metadata": {
        "id": "Ak7yoLNQYBFm"
      },
      "id": "Ak7yoLNQYBFm",
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### KNN\n",
        "- https://scikit-learn.org/stable/modules/neighbors.html"
      ],
      "metadata": {
        "id": "0BKEjiLac724"
      },
      "id": "0BKEjiLac724"
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_matrix = csr_matrix(ratings_df.values)"
      ],
      "metadata": {
        "id": "sitC6QDPcsEd"
      },
      "id": "sitC6QDPcsEd",
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " 'algorithm', which must be one of ['auto', 'ball_tree', 'kd_tree', 'brute']"
      ],
      "metadata": {
        "id": "WETWayP7dRhm"
      },
      "id": "WETWayP7dRhm"
    },
    {
      "cell_type": "code",
      "source": [
        "model_knn = NearestNeighbors(metric='cosine', algorithm = 'brute')\n",
        "model_knn.fit(ratings_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "f0T2q5-3d3Vq",
        "outputId": "9420d48b-aef5-48e9-f856-1a1cb791fde2"
      },
      "id": "f0T2q5-3d3Vq",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(algorithm='brute', metric='cosine')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_book_id(book_title):\n",
        "    target_df = df.loc[df['original_title'] == book_title]\n",
        "    return target_df['book_id'].iloc[0]\n",
        "\n",
        "id_TheHungerGames = get_book_id('The Hunger Games')\n",
        "print(id_TheHungerGames)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVP4Ep2vd8Si",
        "outputId": "ae6ff041-8ee8-4866-8a80-618a7a46ac4b"
      },
      "id": "KVP4Ep2vd8Si",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_title(book_id):\n",
        "    target_df = df.loc[df['book_id'] == book_id]\n",
        "    return target_df['original_title'].iloc[0]\n",
        "\n",
        "print(get_title(1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnKHbbE-d-v2",
        "outputId": "d9965cc7-d22e-4884-9671-cbc6c7699669"
      },
      "id": "CnKHbbE-d-v2",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Hunger Games\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_recomm(book_title, num_neighbors=10, display=False): \n",
        "    book_ids = []\n",
        "    distances, indices = None, None\n",
        "    \n",
        "    query_index = get_book_id(book_title) - 1\n",
        "    print( distances, indices)\n",
        "    if num_neighbors > 0:\n",
        "        distances, indices = model_knn.kneighbors(ratings_df.iloc[query_index,:].values.reshape(1, -1), n_neighbors = num_neighbors + 1)\n",
        "    else:\n",
        "        distances, indices = model_knn.kneighbors(ratings_df.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 10 + 1)\n",
        "    \n",
        "    for i in range(0, len(distances.flatten())):\n",
        "        if display is True:\n",
        "            if i == 0:\n",
        "                print('Recommendations for ', book_title, '\\n')\n",
        "            else:    \n",
        "                id = ratings_df.index[indices.flatten()[i]]\n",
        "                \n",
        "                print(f'{i}\\t Book ID: {id} \\tBook Title : {get_title(id)} \\tDistance: {distances.flatten()[i]:.2f} \\n') \n",
        "        book_ids.append(ratings_df.index[indices.flatten()[i]])\n",
        "    \n",
        "    return book_ids"
      ],
      "metadata": {
        "id": "yUOYtLhGd-4-"
      },
      "id": "yUOYtLhGd-4-",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recommendations_for_TheHungerGames = get_recomm('The Hunger Games', num_neighbors=10, display=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9EuB464eU0u",
        "outputId": "7086717b-e582-469f-9f47-9ba332fa7c76"
      },
      "id": "i9EuB464eU0u",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None None\n",
            "Recommendations for  The Hunger Games \n",
            "\n",
            "1\t Book ID: 17 \tBook Title : Catching Fire \tDistance: 0.41 \n",
            "\n",
            "2\t Book ID: 31 \tBook Title : The Help \tDistance: 0.43 \n",
            "\n",
            "3\t Book ID: 2 \tBook Title : Harry Potter and the Philosopher's Stone \tDistance: 0.44 \n",
            "\n",
            "4\t Book ID: 20 \tBook Title : Mockingjay \tDistance: 0.45 \n",
            "\n",
            "5\t Book ID: 3 \tBook Title : Twilight \tDistance: 0.49 \n",
            "\n",
            "6\t Book ID: 93 \tBook Title : The Secret Garden \tDistance: 0.51 \n",
            "\n",
            "7\t Book ID: 5 \tBook Title : The Great Gatsby \tDistance: 0.52 \n",
            "\n",
            "8\t Book ID: 16 \tBook Title : Män som hatar kvinnor \tDistance: 0.52 \n",
            "\n",
            "9\t Book ID: 9 \tBook Title : Angels & Demons  \tDistance: 0.53 \n",
            "\n",
            "10\t Book ID: 37 \tBook Title : The Lion, the Witch and the Wardrobe \tDistance: 0.53 \n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.13"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": true,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}