An Audible.in webscraper to gather audiobooks data.
The code in the Notebooks mentioned below and the scraping code are well documented/explained so as to cover all the steps that were taken. If you are new to data wrangling and data cleaning you can use them to understand better on Pandas and Matplotlib. As for the scraping, I'm currently writing a blogpost and will soon be out on how I organized it.
- Regex
- Beautiful Soup
- Selenium
- csv
The code used Selenium and Beautiful Soup in unison to gather the data. NOTE: It's recommended to have Firefox before you use the code as the Selenium driver used is based of Mozilla. You can elsewise, write your own Chrome driver by following this link.
If you are going through the code, inside Loop : 2 you may see a bit of handwritten customization of the code.
That is cause of Audible redirects incorrectly under few sub-categories. Such as under any sub-category if LGBTQ+ is present it redirects
to the LGBTQ+ category and the category it was orginally under is ignored.
It is recommended to check them prior to running the code as Audible.in is updated everyday. Also, while running Loop: 1 since it's recommended to use 3-5 pages at once instead of all as this makes sure that the program runs smoothly.
You can find the dataset here: Audiobooks Dataset
The description of the dataset is mentioned in the description section.
The Audiobooks dataset has two .csv files.
- audible_uncleaned.csv
- audible_cleaned.csv
If you are looking to use the uncleaned one to practice or wrangle the data based on your needs, here's a notebook that might help you get started. It covers from beginners to intermidiate.
If you are rather interested to directly dive into the data and create exiciting data visulation, I've a notebook here.
You can directly start with exploring the data, however make sure you are using the audible_cleaned.csv to start.