Web Scraping to CSV | Multiple Pages Scraping with BeautifulSoup

This paragraph introduces the concept of web scraping, emphasizing its utility in efficiently extracting information from websites. The speaker explains the process of scraping 50 pages of a website to gather details such as book names, titles, prices, and star ratings, and exporting this data into a CSV file. The necessity of web scraping is highlighted by the impracticality of manually copying and pasting large volumes of data. The video also introduces 'books.twoscrate.com' as a platform for practicing web scraping, akin to real e-commerce sites like Amazon.

The speaker demonstrates how to inspect the HTML structure of a webpage to identify the elements containing the desired data. By right-clicking and selecting 'Inspect', one can view the HTML code and pinpoint specific tags and classes that hold the information needed for scraping. The paragraph details the process of navigating through the HTML structure, from the article tag to the unordered list, and identifying the tags responsible for displaying the book title, star rating, and price. It also explains how to extract the full title of a book from the image's 'alt' attribute.

This paragraph delves into the actual coding process of web scraping. The speaker begins by importing necessary libraries such as 'requests' and 'beautiful soup' in a Google Colab environment. The process of sending HTTP requests to fetch web pages and parsing the response content using 'beautiful soup' is explained. The paragraph outlines the steps to extract the order list of books, loop through each article to find individual book details, and parse out the image 'alt' text for the title, the class name for the star rating, and the price from the paragraph tag with the class 'price color'.

The speaker continues the coding process by extracting the relevant data from the identified HTML tags. The paragraph details the extraction of the book title from the image 'alt' attribute, the star rating from the class name of a 'p' tag, and the price from a paragraph tag. The process of cleaning and converting the extracted data into a usable format is also discussed. The speaker then demonstrates how to store the extracted data in a list of dictionaries, with each dictionary containing the title, star rating, and price of a book.

The paragraph explains how to automate the scraping process for multiple pages by using a for loop. The speaker shows how to update the URL with the page number and iterate through all 50 pages to gather data. The paragraph also covers the process of exporting the scraped data to a CSV file using the pandas library. The speaker creates a pandas DataFrame from the list of books and exports it to a CSV file, demonstrating how to install and use pandas for data manipulation and storage.

In the concluding paragraph, the speaker wraps up the web scraping tutorial by summarizing the process and its benefits. The ease and efficiency of web scraping are emphasized, along with the practical application of the skills learned. The speaker encourages viewers to like, share, and comment on the video to help it reach a wider audience. The importance of community engagement and the value of learning and sharing knowledge are highlighted.