Let's collect pages

Let's collect pages 00703

This code is a web scraping script that extracts information from a specified website and saves it to an Excel file. This Python script uses popular libraries such as Requests, BeautifulSoup, Pandas, and Openpyxl to perform web scraping. It begins by defining the base_url, the website you intend to scrape. The script then sends an HTTP GET request to the main page of the website, retrieves the HTML content, and parses it using BeautifulSoup.

Characteristics:

[Category] <---------------------------------------------------------> [Invoices]
[Creator] <-----------------------------------------------------> [InNotSystem13]
[Project] <---------------------------------------------------> [Let's collect pages]
[Identifier] <--------------------------------------------------------> [00703]
[Contract] <------------> [0xE73Ea61D9d3A8769f4638fC1F56F946F4927Ead8]
[Chain] <---------------------------------------------------------> [BSC Chain]
[Type] <----------------------------------------------------> [Application]

Binance Smart Chain (BSC)

Price: 1 USDT

Video instruction

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Install Python: If you don't already have it, download and install Python from the official website. Python supports various operating systems, and you can choose the version that suits your computer.

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In just 1 minute

    import requests
    from bs4 import BeautifulSoup
    import pandas as pd
    from openpyxl import Workbook

    # The link to the website you want to scrape
    base_url = "http://example.com/"

    # Getting the HTML code of the main page
    response = requests.get(base_url)
    soup = BeautifulSoup(response.text, "html.parser")

    # Creating a list to store page data
    pages = []

    # Function to extract information from a page
    def extract_page_info(url):
        response = requests.get(url)
        page_soup = BeautifulSoup(response.text, "html.parser")
        title = page_soup.title.string
        content = page_soup.get_text()
        return {"URL": url, "Title": title, "Content": content}

    # Getting all the links to website pages
    for link in soup.find_all("a"):
        href = link.get("href")
        if href and href.startswith(base_url):
            pages.append(extract_page_info(href))

    # Creating a DataFrame
    df = pd.DataFrame(pages)

    # Saving the data to an Excel file
    with pd.ExcelWriter("website_pages.xlsx") as writer:
        df.to_excel(writer, sheet_name="Pages", index=False)

    print("Scraping and saving completed.")


    # pip install requests
    # pip install beautifulsoup4
    # pip install pandas
    # pip install openpyxl

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Smart-Contract #000-00000000000028 0xE73Ea61D9d3A8769f4638fC1F56F946F4927Ead8

successfully created

Author's message: A list called pages is created to store information about the website's pages. To extract information from each page, a function extract_page_info(url) is defined. This function sends another HTTP GET request to the specified page, extracts the title and textual content, and returns this data in a dictionary format.