WSBooks.ipynb

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        "<a href=\"https://colab.research.google.com/github/ktugceerdemli/WebScraping/blob/main/WSBooks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Price Analysis with Web Scraping\n",
        "\n",
        "**Bussiness Problem**\n",
        "\n",
        "A company selling books online finds that it is under-selling in the ‘Travel’ and ‘Nonfiction’ categories. For this reason, the company needs to obtain information on the competitor's website https://books.toscrape.com/, which is permitted to be scraped, for books in the ‘Travel’ and ‘Nonfiction’ categories and to carry out a competitor and price analysis. The company expects you to go to the detail pages of each book in these categories and retrieve some of the information available there."
      ],
      "metadata": {
        "id": "f4NtkbXHNi7R"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install selenium\n",
        "!pip install bs4"
      ],
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          "text": [
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Import all necessary libraries"
      ],
      "metadata": {
        "id": "ABqI4wzcnK1U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "l_QxW76ONSQJ"
      },
      "outputs": [],
      "source": [
        "\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "from selenium import webdriver\n",
        "from selenium.webdriver.chrome.options import Options\n",
        "from selenium.webdriver.common.by import By\n",
        "import time\n",
        "from selenium.webdriver.common.keys import Keys"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Functions"
      ],
      "metadata": {
        "id": "wHATymy9nTvd"
      }
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "def scrape_opt():\n",
        "    # Chrome options for Selenium\n",
        "    chrome_options = Options()\n",
        "    chrome_options.add_argument('--headless')\n",
        "    chrome_options.add_argument('--no-sandbox')\n",
        "    chrome_options.add_argument('--disable-blink-features=AutomationControlled')\n",
        "    # Driver generation\n",
        "    driver = webdriver.Chrome(options=chrome_options)\n",
        "    return driver\n",
        "\n",
        "\n",
        "def soup_html_data(driver, book_data):\n",
        "    soup = BeautifulSoup(driver.page_source, 'html.parser')\n",
        "    book_elements = soup.select(\"ol.row li\")\n",
        "\n",
        "    if not book_elements:\n",
        "        return False\n",
        "\n",
        "    for book_element in book_elements:\n",
        "        book_name = book_element.select_one(\"h3 a\").get(\"title\")\n",
        "        book_price = float(book_element.select_one(\"p.price_color\").text.replace('£', '').strip())\n",
        "        book_availability = book_element.select_one(\"p.instock\").text.strip()\n",
        "        book_star = book_element.select_one(\"p.star-rating\").get(\"class\")[-1]\n",
        "\n",
        "        book_info = {\n",
        "            'name': book_name,\n",
        "            'price': book_price,\n",
        "            'availability': book_availability,\n",
        "            'star': book_star\n",
        "        }\n",
        "        book_data.append(book_info)\n",
        "\n",
        "    return True\n",
        "\n"
      ],
      "metadata": {
        "id": "imhiSdQdXZVR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scrapping"
      ],
      "metadata": {
        "id": "BDdM14wDn8nG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "driver = scrape_opt()\n",
        "main_url = 'https://books.toscrape.com/'\n",
        "driver.get(main_url)\n",
        "time.sleep(3)\n",
        "\n",
        "# Get category links\n",
        "cat_elements = driver.find_elements(By.XPATH, \"//a[contains(text(),'Travel') or contains(text(), 'Nonfiction')]\")\n",
        "cat_urls = [element.get_attribute(\"href\") for element in cat_elements]\n",
        "\n",
        "book_data = []\n",
        "\n",
        "for cat in cat_urls:\n",
        "    driver.get(cat)\n",
        "    time.sleep(3)\n",
        "\n",
        "    if 'travel' in cat.lower():\n",
        "        page_url = cat\n",
        "        driver.get(page_url)\n",
        "        time.sleep(3)\n",
        "        soup_html_data(driver, book_data)\n",
        "    else:\n",
        "        page_number = 1\n",
        "        while True:\n",
        "            page_url = f\"{cat.replace('index.html', '')}page-{page_number}.html\"\n",
        "            driver.get(page_url)\n",
        "            time.sleep(3)\n",
        "\n",
        "            if not soup_html_data(driver, book_data):\n",
        "                break\n",
        "\n",
        "            page_number += 1\n",
        "\n",
        "driver.quit()\n"
      ],
      "metadata": {
        "id": "w9N4hLszXmgq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save the data"
      ],
      "metadata": {
        "id": "wN6U4dQ7oAlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a DataFrame from the collected book data\n",
        "book_data_df = pd.DataFrame(book_data)\n",
        "\n",
        "# Display the DataFrame\n",
        "print(book_data_df)\n",
        "\n",
        "book_data_df.to_csv(\"/content/book_df.csv\", index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZ4LTcWdXxQ9",
        "outputId": "676865bb-e7f5-4be5-f25c-24acc41bee09"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  name  price availability  \\\n",
            "0                              It's Only the Himalayas  45.17     In stock   \n",
            "1    Full Moon over Noah’s Ark: An Odyssey to Mount...  49.43     In stock   \n",
            "2    See America: A Celebration of Our National Par...  48.87     In stock   \n",
            "3    Vagabonding: An Uncommon Guide to the Art of L...  36.94     In stock   \n",
            "4                                 Under the Tuscan Sun  37.33     In stock   \n",
            "..                                                 ...    ...          ...   \n",
            "116                                      H is for Hawk  57.42     In stock   \n",
            "117         Travels with Charley: In Search of America  57.82     In stock   \n",
            "118                                          The Tumor  41.56     In stock   \n",
            "119     The End of the Jesus Era (An Investigation #1)  14.40     In stock   \n",
            "120                                    Eat, Pray, Love  51.32     In stock   \n",
            "\n",
            "      star  \n",
            "0      Two  \n",
            "1     Four  \n",
            "2    Three  \n",
            "3      Two  \n",
            "4    Three  \n",
            "..     ...  \n",
            "116   Five  \n",
            "117   Five  \n",
            "118   Five  \n",
            "119    One  \n",
            "120  Three  \n",
            "\n",
            "[121 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyze the data"
      ],
      "metadata": {
        "id": "G_Ug_jVOoan0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_data_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYvtT_8qlrAm",
        "outputId": "5153160f-1f98-499c-cc1e-461c4e3eec35"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 121 entries, 0 to 120\n",
            "Data columns (total 6 columns):\n",
            " #   Column          Non-Null Count  Dtype   \n",
            "---  ------          --------------  -----   \n",
            " 0   name            121 non-null    object  \n",
            " 1   price           121 non-null    float64 \n",
            " 2   availability    121 non-null    object  \n",
            " 3   star            121 non-null    object  \n",
            " 4   price_category  121 non-null    category\n",
            " 5   star_int        121 non-null    int64   \n",
            "dtypes: category(1), float64(1), int64(1), object(3)\n",
            "memory usage: 5.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by star rating and summarize\n",
        "groupbystar = book_data_df.groupby('star').agg({\n",
        "    'name': 'count',  # Count number of books per star rating\n",
        "    'price': 'mean',  # Average price per star rating\n",
        "}).rename(columns={'name': 'count', 'price': 'average_price'})\n",
        "\n",
        "print(groupbystar)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CV520w7X82Z",
        "outputId": "ca50e3e5-95d9-485a-c03e-b404e5a2ed02"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       count  average_price\n",
            "star                       \n",
            "Five      21      36.537143\n",
            "Four      20      37.705500\n",
            "One       26      35.057308\n",
            "Three     28      33.362143\n",
            "Two       26      32.282308\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plotting the count of books per star rating\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "groupbystar['count'].plot(kind='bar', color='skyblue')\n",
        "plt.title('Count of Books for Each Star Rating')\n",
        "plt.xlabel('Star Rating')\n",
        "plt.ylabel('Count of Books')\n",
        "\n",
        "# Plotting the average price per star rating\n",
        "plt.subplot(1, 2, 2)\n",
        "groupbystar['average_price'].plot(kind='bar', color='blue')\n",
        "plt.title('Average Price for Each Star Rating')\n",
        "plt.xlabel('Star Rating')\n",
        "plt.ylabel('Average Price (£)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "k-XXiqgzhJny",
        "outputId": "9fd8a690-bd91-49c7-bb57-4f7172bf1e0f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<Figure size 1000x500 with 2 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "book_data_df['price_category'] = pd.qcut(book_data_df['price'], q=3, labels=['Cheap', 'Average', 'Expensive'])"
      ],
      "metadata": {
        "id": "3dsOVke3hs_q"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert star ratings from text to numbers\n",
        "book_data_df['star_int'] = book_data_df['star'].map({\n",
        "    'One': 1,\n",
        "    'Two': 2,\n",
        "    'Three': 3,\n",
        "    'Four': 4,\n",
        "    'Five': 5\n",
        "})\n",
        "\n",
        "book_data_df['star_int'].astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtboBbihkL7x",
        "outputId": "f35308ee-b55c-4218-d9d1-7e169a7ed99e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2\n",
              "1      4\n",
              "2      3\n",
              "3      2\n",
              "4      3\n",
              "      ..\n",
              "116    5\n",
              "117    5\n",
              "118    5\n",
              "119    1\n",
              "120    3\n",
              "Name: star_int, Length: 121, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by star rating and summarize\n",
        "groupbyprice = book_data_df.groupby('price_category').agg({\n",
        "    'star_int': 'median',\n",
        "    'name': 'count',  # Count number of books per star rating\n",
        "    'price': 'mean',  # Average price per star rating\n",
        "}).rename(columns={'name': 'count', 'price': 'average_price'})\n",
        "print(groupbyprice)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oaXRGk8Fk-2f",
        "outputId": "e6f81a63-c0c4-40c6-bd2f-d41dcc504508"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                star_int  count  average_price\n",
            "price_category                                \n",
            "Cheap                3.0     41       19.57439\n",
            "Average              3.0     40       34.40750\n",
            "Expensive            3.0     40       50.68775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "book_data_df[\"availability\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QHopAxngdld",
        "outputId": "b3564681-ecbd-481c-b160-e6e4e5d3798a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "availability\n",
              "In stock    121\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ]
}