Amazon Books Web Scraping

Amazon most popular data-science-related books dataset.

The dataset contains 946 books obtained from scraping Amazon books related to data science, statistics, data analysis, Python, deep learning and machine learning.
You can find this dataset on Kaggle or directly here.
It includes 18 columns:

• title: title of the book
• author: author (or the authors) of the book
• price: price (in dollars)
• pages: number of pages
• avg_reviews: average reviews (out of 5)
• n_reviews: reviews done for each book
• star5: percentage of 5 star reviews
• star4: percentage of 4 star reviews
• star3: percentage of 3 star reviews
• star2: percentage of 2 star reviews
• star1: percentage of 1 star reviews
• dimensions: size of the book (in inches)
• weight: weight (in pounds or ounces)
• language: language of the book
• publisher: publisher
• ISBN-13: ISBN_13 code
• link: link of the Amazon book
• complete_link: complete link of the Amazon book (including the domain https://amazon.com)

Inspiration

You can perform an exploratory data analysis of the dataset, working with Pandas or Numpy. Interesting visualizations can be performed too using, for instance, Python libraries to plot the different features. This dataset can be also used to practice queries using SQL or Pandas. Moreover, you can also rank the books based on the number of positive reviews or you can explore the dataset to have a reference for buying data-science-related books in the future.

Remember to star the repository if you found the dataset useful :).

Collection methodology

The dataset was obtained through web scraping from Amazon.

More than 1700 books were scraped, fetching the most important information for each book. Duplicated were deleted, each column has been formatted and made easy to use for data analysis purposes. Links with a lot of missing values were deleted, and in some cases, were filled with an appropriate value based on the column. Therefore, the total number of books passed from 1788 to 946.

Here, you can find all the notebooks (in .ipynb) I used to scrape and clean the data.