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Web scraping is the procedure of scraping data online in an automated way.

Amazon is among the most significant online marketplaces in the USA. Users can purchase products from Amazon or any third-party seller on Amazon or sell to anybody. Then, the orders are delivered using Amazon’s courier services. Amazon also sells its technology items.

Here is the page that offers a listing of the best seller books on Amazon, and we will extract the bestseller books’ information like name, writer, position, pricing, total reviews, and ratings. Here we would use beautifulsoup4 libraries and python requests for scraping this data.

We’ll pursue the given steps:

1. Download a webpage utilizing requests

2. Parse an HTML source code with BeautifulSoup

3. Scrape name, writer, position, pricing, total reviews, and ratings from a page

4. Store the scraped data into a Python list and dictionaries

5. Save scraped data in the CSV file.

1. Download a webpage utilizing requests


Let’s install Python requests and the BeautifulSoup library that we will utilize to fetch a webpage and import that.

Now we can utilize the get() process from requests. The get() process of requests takes the URL of a page as the parameter and returns the web object having data from a webpage and a few other details.


Now the document is all set for parsing. We can utilize various properties and methods to scrape data from the record.

3. Scrape Data like Book Details, Name, Writer, Position, Pricing, Ratings, and Total Reviews from the Document

In the section, we’ll scrape the bestsellers’ books div. When we get all the books, we will scrape data from every book, like poster, bestseller_ranking, writer, rating, book_name, reviews, and pricing.

3.1. Getting Movie Items


We’ll scrape all books div with doc.find_all() technique and id='gridItemRoot'. It will provide a listing of all the bestseller books in the document. Here, we could also check the number of books scraped by a document with len(book_docs).

Here, we have all book_items and directly above we saw how every book will look like through printing the initial book item

Now we’ll scrape data like poster, bestseller_ranking, book_name, ratings, reviews, writer, and pricing in sequence. Here we’ll scrape data for the first book and then we’ll outline a function that scrape these data for all books.

3.2. Scrape Poster and Position Data of a Book


The bestseller position of a book is within span tag and class_="zg-bdg-text" therefore, we can find a book’s position using find() technique.

Here, we would scrape poster of a book. A poster is within img tag. Therefore, we can have a movie poster using the img tag and an attribute src.

3.3. Scrape a Writer’s Name and Book Name


Here we’ll scrape a book’s name and its writer by utilizing find_all() technique. The name and the writer of a book available in a div that have class name like class_="_cDEzb_p13n-sc-css-line-clamp-1_1Fn1y". Therefore, we scrape book’s name and writer’s name by utilizing .text property.

3.4. Scrape Reviews and Ratings of a Book


Now we’ll scrape reviews and ratings of a book from div that have class name like class_="a-icon-row" by utilizing find() technique.

Here, we have used .find() technique to scrape book ratings from i tag using .text properties.

Also, we have used .find() technique to scrape reviews from a span tag getting class class_="a-size-small" through .text properties.

3.5. Scrape the Book Prices


Here, we have used find() technique to scrape the prices of a book that is within span tag and have class class_="p13n-sc-price" with using a .text property for getting price and .strip('₹') trim the price value.

3.6. Scrape Data from Book Items


Now let’s mix all the given subsections and make a function with these subsections to scrape book data. This act takes the book document like an input and return the necessary data that we want.

The given function scrapes data for a single book.

4. Save the Scraped Data in the Lists’ Python Dictionary


As we can scrape data for different movies by applying the loop on books and store data in the lists’ dictionary.

5. Combine All and Describe a Function for Scraping Data for ‘n’ pages and Save the Scraped Data in the CSV File


As we have books’ data in the lists’ dictionary, we can write and save data into the CSV file. For that we need to import a pandas library and convert that into the dataframe with function pd.DataFrame() to save in the CSV file using DataFrame.to_csv().

Here, we will write a function that scrape >Amazon’s bestseller books to provide total pages.


In this blog, we have extracted Amazon web data to scrape Amazon bestsellers book data for ‘n’ number of pages to save that data in the CSV file. For more information and your opinion on this blog, please comment below!

You can also contact Actowiz Solutions for your web scraping services and mobile app scraping service requirements.