Books_dataset_table_preprocessing.ipynb

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      "cell_type": "markdown",
      "source": [
        "# The code is for split the messed table into easy to use table"
      ],
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      }
    },
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      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
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      "cell_type": "code",
      "execution_count": 1,
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ast"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load dataset"
      ],
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        "id": "GZQPKx3MqbhS"
      }
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Chaloklum bookshop dataset/v_2.4.0/books_dataset_w_review_2.4.0_reordered.csv')"
      ],
      "metadata": {
        "id": "jCs5AYoSqbPw"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "df.head()"
      ],
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      "execution_count": 3,
      "outputs": [
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              "   id                                  title  \\\n",
              "0   1      Útěky a návraty Bohumila Laušmana   \n",
              "1   2          A nők tartják az égbolt felét   \n",
              "2   3                                 Wallis   \n",
              "3   4  Cheeseburgers, the Best of Bob Greene   \n",
              "4   5                         We, the Nation   \n",
              "\n",
              "                                          subtitle            authors  \\\n",
              "0                            osud českého politika      ['Jiří Šolc']   \n",
              "1  egy rendkívüli asszony rendkívüli élettörténete                NaN   \n",
              "2                                        The Novel   ['Anne Edwards']   \n",
              "3                                              NaN     ['Bob Greene']   \n",
              "4  The Conservative Party and the Pursuit of Power  ['Andrew Davies']   \n",
              "\n",
              "      isbn_10        isbn_13  isbn_other      isbn_book language  \\\n",
              "0         NaN            NaN         NaN  9788020609564       cs   \n",
              "1         NaN            NaN         NaN  9789637253089       hu   \n",
              "2  068808835X  9780688088354         NaN     068808835X       en   \n",
              "3  068911611X  9780689116117         NaN     068911611X       en   \n",
              "4         NaN            NaN         NaN  9780316914454       en   \n",
              "\n",
              "                                         google_desc  ... page_count  \\\n",
              "0                                                NaN  ...      403.0   \n",
              "1                                                NaN  ...        NaN   \n",
              "2  Wallis Warfield Spencer Simpson retains her pr...  ...      478.0   \n",
              "3                                                NaN  ...      318.0   \n",
              "4  From Sir Robert Peel to John Major, this is a ...  ...      532.0   \n",
              "\n",
              "  rating_avg #reviews #ratings  #text_reviews  \\\n",
              "0        NaN      NaN      NaN            NaN   \n",
              "1        NaN      NaN      NaN            NaN   \n",
              "2       3.74    312.0    151.0           22.0   \n",
              "3       4.11    165.0     93.0            9.0   \n",
              "4        NaN      NaN      NaN            NaN   \n",
              "\n",
              "                                           thumbnail  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2  http://books.google.com/books/content?id=wOyMH...   \n",
              "3  http://books.google.com/books/content?id=3ssiA...   \n",
              "4                                                NaN   \n",
              "\n",
              "                                               genre  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2  ['Historical', 'Historical Fiction', 'Fiction'...   \n",
              "3            ['Nonfiction', 'Writing', 'Journalism']   \n",
              "4                                                NaN   \n",
              "\n",
              "                                        text_reviews  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2  [[5.0, \"NetGalley provided me this book file i...   \n",
              "3  [[4.0, \"I don&apost remember all his essays, b...   \n",
              "4                                                NaN   \n",
              "\n",
              "                                          also_enjoy status  \n",
              "0                                                NaN   -1.0  \n",
              "1                                                NaN   -1.0  \n",
              "2  [['The Glorious Guinness Girls', 'https://i.gr...    1.0  \n",
              "3  [['Veeck As In Wreck: The Autobiography of Bil...    1.0  \n",
              "4                                                NaN   -1.0  \n",
              "\n",
              "[5 rows x 24 columns]"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3937 entries, 0 to 3936\n",
            "Data columns (total 24 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id              3937 non-null   int64  \n",
            " 1   title           3937 non-null   object \n",
            " 2   subtitle        1132 non-null   object \n",
            " 3   authors         3780 non-null   object \n",
            " 4   isbn_10         2854 non-null   object \n",
            " 5   isbn_13         3140 non-null   object \n",
            " 6   isbn_other      0 non-null      float64\n",
            " 7   isbn_book       3937 non-null   object \n",
            " 8   language        3909 non-null   object \n",
            " 9   google_desc     2099 non-null   object \n",
            " 10  categories      2095 non-null   object \n",
            " 11  goodreads_desc  2766 non-null   object \n",
            " 12  publisher       1458 non-null   object \n",
            " 13  published_date  3815 non-null   object \n",
            " 14  page_count      3684 non-null   float64\n",
            " 15  rating_avg      3095 non-null   float64\n",
            " 16  #reviews        3095 non-null   float64\n",
            " 17  #ratings        3095 non-null   float64\n",
            " 18  #text_reviews   3095 non-null   float64\n",
            " 19  thumbnail       2348 non-null   object \n",
            " 20  genre           2851 non-null   object \n",
            " 21  text_reviews    2851 non-null   object \n",
            " 22  also_enjoy      2753 non-null   object \n",
            " 23  status          3930 non-null   float64\n",
            "dtypes: float64(7), int64(1), object(16)\n",
            "memory usage: 738.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bookList\n",
        "Provide books detail corressponding to id"
      ],
      "metadata": {
        "id": "VLVWqN0GGMq5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bookList = df[['id', 'title','subtitle', 'authors', 'language', 'genre', 'published_date', 'page_count']]"
      ],
      "metadata": {
        "id": "UXw23XMBrCoa"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for index in bookList.index:\n",
        "  authors_str = df.at[index, 'authors']\n",
        "  genres_str = df.at[index, 'genre']\n",
        "\n",
        "  ### Authors list split ###\n",
        "  if authors_str is not np.nan:\n",
        "    if type(authors_str) is str:\n",
        "      authors_list = ast.literal_eval(authors_str)\n",
        "      bookList.at[index, 'authors'] = ' | '.join(str(e) for e in authors_list)\n",
        "    else:\n",
        "      bookList.at[index, 'authors'] = ' | '.join(str(e) for e in authors_str)\n",
        "\n",
        "  ### Genres list split ###\n",
        "  if genres_str is not np.nan:\n",
        "    if type(genres_str) is str:\n",
        "      genres_list = ast.literal_eval(genres_str)\n",
        "      bookList.at[index, 'genre'] = ' | '.join(str(e) for e in genres_list)\n",
        "    else:\n",
        "      bookList.at[index, 'genre'] = ' | '.join(str(e) for e in genres_str)"
      ],
      "metadata": {
        "id": "-vgT5W4arfi3"
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      "execution_count": 6,
      "outputs": []
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      "source": [
        "bookList"
      ],
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        "outputId": "5f1a2f0a-596b-4804-b5a4-fccd556259f8"
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      "execution_count": 7,
      "outputs": [
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              "        id                                              title  \\\n",
              "0        1                  Útěky a návraty Bohumila Laušmana   \n",
              "1        2                      A nők tartják az égbolt felét   \n",
              "2        3                                             Wallis   \n",
              "3        4              Cheeseburgers, the Best of Bob Greene   \n",
              "4        5                                     We, the Nation   \n",
              "...    ...                                                ...   \n",
              "3932  3933                 Troostende armen by Barbara Pierce   \n",
              "3933  3934  Ποιος σκότωσε τον Ακρόυντ (Hercule Poirot, #4)...   \n",
              "3934  3935  Ρετρό στην ομίχλη (Miss Marple, #13) by Agatha...   \n",
              "3935  3936        Ο σατανάς με την πλερέζα by Agatha Christie   \n",
              "3936  3937        O μπρούντζινος καβαλάρης by Paullina Simons   \n",
              "\n",
              "                                             subtitle  \\\n",
              "0                               osud českého politika   \n",
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              "3932                                              NaN   \n",
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              "3934                                              NaN   \n",
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              "3936                                              NaN   \n",
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              "3935   Agatha Christie | Αχιλλέας Κυριαζής      NaN   \n",
              "3936   Paullina Simons | Γιάννης Σπανδωνής      NaN   \n",
              "\n",
              "                                                  genre published_date  \\\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bookReview"
      ],
      "metadata": {
        "id": "6k1D4jmlGcFH"
      }
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    {
      "cell_type": "code",
      "source": [
        "id_list = []\n",
        "rating_list = []\n",
        "text_list = []\n",
        "\n",
        "for index in df.index:\n",
        "  id = df.at[index, 'id']\n",
        "  rateNreview = df.at[index, 'text_reviews']\n",
        "  if rateNreview is not np.nan:\n",
        "    rateNreview = ast.literal_eval(rateNreview)\n",
        "    for review in rateNreview:\n",
        "      rating = review[0]\n",
        "      text = review[1]\n",
        "      id_list.append(id)\n",
        "      rating_list.append(rating)\n",
        "      text_list.append(text)"
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      "execution_count": 8,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "bookReview = pd.DataFrame()\n",
        "bookReview = bookReview.assign(id = id_list)\n",
        "bookReview = bookReview.assign(rating = rating_list)\n",
        "bookReview = bookReview.assign(text_review = text_list)"
      ],
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      "execution_count": 10,
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              "         id  rating                                        text_review\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d0509a1a-18c5-4ea4-b085-0029273f9354 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-d0509a1a-18c5-4ea4-b085-0029273f9354');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jcuk7OzELOqw"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bookDesc"
      ],
      "metadata": {
        "id": "CU7xxSKtL6BY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bookDesc = df[['id', 'google_desc', 'goodreads_desc']]"
      ],
      "metadata": {
        "id": "URaIy08TMAIZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookDesc = bookDesc[bookDesc['google_desc'].notna() | bookDesc['goodreads_desc'].notna()]"
      ],
      "metadata": {
        "id": "hRHpamQnMLYE"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookDesc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
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        "id": "J4mdVjT6MMWj",
        "outputId": "1081c4d6-b803-4527-bcf7-375ed064b9bb"
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      "execution_count": 13,
      "outputs": [
        {
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            "text/plain": [
              "        id                                        google_desc  \\\n",
              "2        3  Wallis Warfield Spencer Simpson retains her pr...   \n",
              "3        4                                                NaN   \n",
              "4        5  From Sir Robert Peel to John Major, this is a ...   \n",
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              "3920  3921  漱石からケネディ、チャップリンまで、とっておきの名言をもとに「生き方がうまくなるコツ」をモタ...   \n",
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              "3926  3927  Dawn French is one of the greatest comedy actr...   \n",
              "3927  3928  \"There are some things in life you can always ...   \n",
              "3929  3930  21st Century Mumbai is a city of new money and...   \n",
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              "3921                                                NaN  \n",
              "3926  \\nOne of the greatest comedy actresses of our ...  \n",
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              "      <th>3926</th>\n",
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              "      <td>3930</td>\n",
              "      <td>21st Century Mumbai is a city of new money and...</td>\n",
              "      <td>\\n21st Century Mumbai is a city of new money a...</td>\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",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bookStat"
      ],
      "metadata": {
        "id": "GrBxF4XUO4IE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bookStat = df[['id', 'rating_avg', '#ratings', '#reviews', '#text_reviews']]"
      ],
      "metadata": {
        "id": "tRxQKpSNNudC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookStat = bookStat.dropna(subset=['rating_avg', '#ratings', '#reviews', '#text_reviews'])"
      ],
      "metadata": {
        "id": "OqkTAJ3JO249"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookStat"
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        "id": "8xdlCZZTPPnx",
        "outputId": "c540bbce-8a86-4990-f650-34e67fc27f1f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  rating_avg  #ratings  #reviews  #text_reviews\n",
              "2        3        3.74     151.0     312.0           22.0\n",
              "3        4        4.11      93.0     165.0            9.0\n",
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              "7        8        3.85     781.0    1408.0           50.0\n",
              "...    ...         ...       ...       ...            ...\n",
              "3932  3933        3.52      52.0     187.0            4.0\n",
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              "3934  3935        3.97   38677.0   70692.0         2313.0\n",
              "3935  3936        3.90   35194.0   68117.0         2066.0\n",
              "3936  3937        4.30   79196.0  193065.0         8575.0\n",
              "\n",
              "[3095 rows x 5 columns]"
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c5b08ae-f663-44af-9761-30c791033b14 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-3c5b08ae-f663-44af-9761-30c791033b14');\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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gaWgJvhfPxP-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bookAlsoEnjoy"
      ],
      "metadata": {
        "id": "wJEhTN25Q44g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bookAlsoEnjoy = df[['id', 'also_enjoy']]"
      ],
      "metadata": {
        "id": "Oo7ARl-nRLAr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookAlsoEnjoy = bookAlsoEnjoy[bookAlsoEnjoy['also_enjoy'].notna()]"
      ],
      "metadata": {
        "id": "0O1CPPO3RUyF"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for index in bookAlsoEnjoy.index:\n",
        "  book_list = []\n",
        "  joyset = bookAlsoEnjoy.at[index, 'also_enjoy']\n",
        "  joyset = ast.literal_eval(joyset)\n",
        "  for each in joyset:\n",
        "    book = each[0]\n",
        "    url = each[1]\n",
        "    book_list.append(book)\n",
        "  bookAlsoEnjoy.at[index, 'also_enjoy'] = ' | '.join(str(e) for e in book_list)"
      ],
      "metadata": {
        "id": "WiAFdvNmSFXV"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookAlsoEnjoy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Zwm6xQ05UCyE",
        "outputId": "4cb9c97f-bc6a-4b49-ef8e-1ba10f740839"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id                                         also_enjoy\n",
              "2        3  The Glorious Guinness Girls | The Postmistress...\n",
              "3        4  Veeck As In Wreck: The Autobiography of Bill V...\n",
              "5        6  Cross Bones (Temperance Brennan, #8) | Patrici...\n",
              "6        7  The Honourable Schoolboy by John le Carre Summ...\n",
              "7        8  The Last Nazi (Joe Johnson #1) | Fatal Witness...\n",
              "...    ...                                                ...\n",
              "3918  3919  大ダーク 1 (Dai Dark, #1) | Veil : Température ora...\n",
              "3919  3920  Sisyphean | Le Rakugo, à la vie, à la mort - V...\n",
              "3926  3927  Bonkers: My Life in Laughs | That's Another St...\n",
              "3927  3928  Meet Me in Hawaii (Meet Me #2) | The Curfew | ...\n",
              "3929  3930  The Black Mountain: Quick Reads 2022 | The Cat...\n",
              "\n",
              "[2753 rows x 2 columns]"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>3</th>\n",
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              "      <td>Veeck As In Wreck: The Autobiography of Bill V...</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Cross Bones (Temperance Brennan, #8) | Patrici...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>The Honourable Schoolboy by John le Carre Summ...</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>The Last Nazi (Joe Johnson #1) | Fatal Witness...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3918</th>\n",
              "      <td>3919</td>\n",
              "      <td>大ダーク 1 (Dai Dark, #1) | Veil : Température ora...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3919</th>\n",
              "      <td>3920</td>\n",
              "      <td>Sisyphean | Le Rakugo, à la vie, à la mort - V...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3926</th>\n",
              "      <td>3927</td>\n",
              "      <td>Bonkers: My Life in Laughs | That's Another St...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3927</th>\n",
              "      <td>3928</td>\n",
              "      <td>Meet Me in Hawaii (Meet Me #2) | The Curfew | ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3929</th>\n",
              "      <td>3930</td>\n",
              "      <td>The Black Mountain: Quick Reads 2022 | The Cat...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2753 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e675624e-466d-42df-af51-8179cb05e6ed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e675624e-466d-42df-af51-8179cb05e6ed 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-e675624e-466d-42df-af51-8179cb05e6ed');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YLUftM3JUEoM"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save file"
      ],
      "metadata": {
        "id": "_Ta84_e2Ujya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Chaloklum bookshop dataset/v_2.4.0/'"
      ],
      "metadata": {
        "id": "L5q16Ph1Un27"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bookList.to_csv(path + 'bookList.csv', index=False)\n",
        "bookReview.to_csv(path + 'bookReview.csv', index=False)\n",
        "bookDesc.to_csv(path + 'bookDesc.csv', index=False)\n",
        "bookStat.to_csv(path + 'bookStat.csv', index=False)\n",
        "bookAlsoEnjoy.to_csv(path + 'bookAlsoEnjoy.csv', index=False)"
      ],
      "metadata": {
        "id": "gf-CghnbVEx6"
      },
      "execution_count": 23,
      "outputs": []
    }
  ]
}