book_recommendation_system.ipynb

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              "   bookID                                              title  \\\n",
              "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
              "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
              "2       4  Harry Potter and the Chamber of Secrets (Harry...   \n",
              "3       5  Harry Potter and the Prisoner of Azkaban (Harr...   \n",
              "4       8  Harry Potter Boxed Set  Books 1-5 (Harry Potte...   \n",
              "\n",
              "                      authors average_rating  \n",
              "0  J.K. Rowling/Mary GrandPré           4.57  \n",
              "1  J.K. Rowling/Mary GrandPré           4.49  \n",
              "2                J.K. Rowling           4.42  \n",
              "3  J.K. Rowling/Mary GrandPré           4.56  \n",
              "4  J.K. Rowling/Mary GrandPré           4.78  "
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11127,\n  \"fields\": [\n    {\n      \"column\": \"bookID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13093,\n        \"min\": 1,\n        \"max\": 45641,\n        \"num_unique_values\": 11127,\n        \"samples\": [\n          33438,\n          1537,\n          32329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10352,\n        \"samples\": [\n          \"Den of Thieves\",\n          \"Madame Bovary\",\n          \"The Prestige\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"authors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6643,\n        \"samples\": [\n          \"Amy Krouse Rosenthal\",\n          \"Plato/John M. Cooper/Benjamin Jowett/Dorothea Frede/Alexander Nehamas/Paul Woodruff/Anthony Kenny/Rosamond Kent Sprague/Nicholas D. Smith/Karen Bell/D.S. Hutchinson/Donald J. Zeyl/Francisco J. Gonz\\u00e1lez/Diskin Clay/Malcolm Schofield/Glenn R. Morrow/Jonathan Barnes/G.M.A. Grube/Brad Inwood/Mark Joyal/Jackson P. Hershbell/J.M. Edmonds/C.D.C. Reeve/Myles Burnyeat/Nicholas P. White/Mary Louise Gill/C.J. Rowe/Stanley Lombardo/David Gallop/Jeffrey Mitscherling/Richard D. McKiharan Jr./Andrew S. Becker/Mark Reuter/M.J. Levett/Paul        Ryan\",\n          \"Don Ihde\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 213,\n        \"samples\": [\n          \"4.21\",\n          \"4.72\",\n          \"3.71\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "data = pd.read_csv(\"books_data.csv\")\n",
        "data.head()"
      ]
    },
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      "source": [
        "data.columns"
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      "execution_count": 2,
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            "text/plain": [
              "Index(['bookID', 'title', 'authors', 'average_rating'], dtype='object')"
            ]
          },
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          "execution_count": 2
        }
      ]
    },
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      "cell_type": "code",
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        "fig = px.histogram(data, x=\"average_rating\", nbins=10,\n",
        "                   title=\"Distribution of Average Ratings\")\n",
        "fig.update_xaxes(title_text='Average Rating')\n",
        "fig.update_yaxes(title_text='Frequency')\n",
        "fig.show()\n"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "top_authors = data['authors'].value_counts().head(20)\n",
        "top_authors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "D5jhXik5emtW",
        "outputId": "e683551b-752f-4a84-e4f8-706bfc976531"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "authors\n",
              "Stephen King                   40\n",
              "P.G. Wodehouse                 40\n",
              "Rumiko Takahashi               39\n",
              "Orson Scott Card               35\n",
              "Agatha Christie                33\n",
              "Piers Anthony                  30\n",
              "Sandra Brown                   29\n",
              "Mercedes Lackey                29\n",
              "Dick Francis                   28\n",
              "Terry Pratchett                23\n",
              "James Patterson                23\n",
              "Laurell K. Hamilton            23\n",
              "Margaret Weis/Tracy Hickman    23\n",
              "Gordon Korman                  22\n",
              "Bill Bryson                    21\n",
              "Janet Evanovich                21\n",
              "Alan Dean Foster               21\n",
              "Dan Simmons                    21\n",
              "Gabriel García Márquez         20\n",
              "Dean Koontz                    20\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>authors</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Stephen King</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P.G. Wodehouse</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rumiko Takahashi</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Orson Scott Card</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agatha Christie</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Piers Anthony</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sandra Brown</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mercedes Lackey</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dick Francis</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Terry Pratchett</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James Patterson</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Laurell K. Hamilton</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Margaret Weis/Tracy Hickman</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gordon Korman</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bill Bryson</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Janet Evanovich</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alan Dean Foster</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dan Simmons</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gabriel García Márquez</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dean Koontz</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(top_authors)\n",
        "fig.update_xaxes(title_text='Author')\n",
        "fig.update_yaxes(title_text='Number of Books')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11127 entries, 0 to 11126\n",
            "Data columns (total 4 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   bookID          11127 non-null  int64 \n",
            " 1   title           11127 non-null  object\n",
            " 2   authors         11127 non-null  object\n",
            " 3   average_rating  11127 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 347.8+ KB\n"
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      "source": [
        "#converting this data type into numeric dtype\n",
        "data['average_rating'] = pd.to_numeric(data['average_rating'], errors='coerce')"
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        "id": "KSt4cIBoe9ej"
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      "outputs": []
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              "          + ' 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",
              "\n",
              "\n",
              "<div id=\"df-86734376-b791-4b28-a72b-9826ea2fcc80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86734376-b791-4b28-a72b-9826ea2fcc80')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-86734376-b791-4b28-a72b-9826ea2fcc80 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11127,\n  \"fields\": [\n    {\n      \"column\": \"bookID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13093,\n        \"min\": 1,\n        \"max\": 45641,\n        \"num_unique_values\": 11127,\n        \"samples\": [\n          33438,\n          1537,\n          32329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10352,\n        \"samples\": [\n          \"Den of Thieves\",\n          \"Madame Bovary\",\n          \"The Prestige\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"authors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6643,\n        \"samples\": [\n          \"Amy Krouse Rosenthal\",\n          \"Plato/John M. Cooper/Benjamin Jowett/Dorothea Frede/Alexander Nehamas/Paul Woodruff/Anthony Kenny/Rosamond Kent Sprague/Nicholas D. Smith/Karen Bell/D.S. Hutchinson/Donald J. Zeyl/Francisco J. Gonz\\u00e1lez/Diskin Clay/Malcolm Schofield/Glenn R. Morrow/Jonathan Barnes/G.M.A. Grube/Brad Inwood/Mark Joyal/Jackson P. Hershbell/J.M. Edmonds/C.D.C. Reeve/Myles Burnyeat/Nicholas P. White/Mary Louise Gill/C.J. Rowe/Stanley Lombardo/David Gallop/Jeffrey Mitscherling/Richard D. McKiharan Jr./Andrew S. Becker/Mark Reuter/M.J. Levett/Paul        Ryan\",\n          \"Don Ihde\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3504853116512785,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          4.06,\n          3.18,\n          3.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"book_content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10816,\n        \"samples\": [\n          \"The Flame Trees of Thika: Memories of an African Childhood Elspeth Huxley\",\n          \"Star Wars:  The New Essential Guide to Vehicles & Vessels W. Haden Blackman/Ian Fullwood\",\n          \"Romeo and Juliet William Shakespeare/Michael Sheen/Kate Beckinsale/Fiona  Shaw/Norman Rodway\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer(stop_words='english')\n",
        "tfidf_matrix = tfidf_vectorizer.fit_transform(data['book_content'])"
      ],
      "metadata": {
        "id": "uePvuzQke9Z7"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the cosine similarity between books\n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
      ],
      "metadata": {
        "id": "78F5Ji8Qe9UL"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def recommend_books(book_title, cosine_sim=cosine_sim):\n",
        "    # Get the index of the book that matches the title\n",
        "    idx = data[data['title'] == book_title].index[0]\n",
        "\n",
        "    # Get the cosine similarity scores for all books with this book\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort the books based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the top 10 most similar books (excluding the input book)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # Get the book indices\n",
        "    book_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return the top 10 recommended books\n",
        "    return data['title'].iloc[book_indices]"
      ],
      "metadata": {
        "id": "4vR5e2-Ee9KT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "here the cosine values of the input is matched with the title & compares the cosine similarities value and dissimilarities and gets the top 10 sets of the data which matches the input book"
      ],
      "metadata": {
        "id": "r3eir22lqs06"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "it sorts the books based on the similarities value\n",
        "\n",
        "0 - 0' -> similiar to 100 %\n",
        "\n",
        "0 - 90' -> similiar to 0 %\n"
      ],
      "metadata": {
        "id": "FiVJlXHLrGcq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_title = 'Seven Plays'\n",
        "recommend_books(book_title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "8cTXTDXRp--z",
        "outputId": "fa70a511-5e2d-4e2c-893b-1ef888362737"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3322                               Buried Child\n",
              "3559     See You Around  Sam! (Sam Krupnik  #3)\n",
              "2871                Sam Walton: Made In America\n",
              "4854    The Secret Seven (The Secret Seven  #1)\n",
              "7768                               Seven Novels\n",
              "7978                      The Atlantis Dialogue\n",
              "2205                          Early Candlelight\n",
              "449                       Medea and Other Plays\n",
              "452                 The Bacchae and Other Plays\n",
              "5850                                 Waterworks\n",
              "Name: title, dtype: object"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3322</th>\n",
              "      <td>Buried Child</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3559</th>\n",
              "      <td>See You Around  Sam! (Sam Krupnik  #3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2871</th>\n",
              "      <td>Sam Walton: Made In America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4854</th>\n",
              "      <td>The Secret Seven (The Secret Seven  #1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7768</th>\n",
              "      <td>Seven Novels</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7978</th>\n",
              "      <td>The Atlantis Dialogue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2205</th>\n",
              "      <td>Early Candlelight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>Medea and Other Plays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>The Bacchae and Other Plays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5850</th>\n",
              "      <td>Waterworks</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M8KzhdxIqFhP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}