The best way to convert raw data right into a more usable and structured format.

When learning a brand new tool, we often go over the docs, watch tutorials, read articles, and solve examples. It is a good-enough approach and can aid you learn the tool to a certain extent.

Nevertheless, once we start using the tool in real-life settings or for solving real issues, we’d like to go just a little beyond what’s covered in most tutorials.

In this text, I’ll explain step-by-step how I used Python for handling two different data cleansing and preprocessing tasks at my job. For every task, I’ll show you the raw data and the specified format. Then, I’ll explain the code for getting the info to that format.

We’ll dive deep into Python’s built-in data structures and Pandas library so you need to expect to learn some interesting stuff on data wrangling with Python.

I actually have a DataFrame with a listing of issues and their summaries. I’m not using or sharing the unique data I actually have here. As a substitute, I generated mock data in the identical format as with the unique one. If you need to follow along by executing the code, download the “mock_issues.csv” file from my datasets repository.

What we’ll do by way of data wrangling relies on the format moderately than the content so the functions and methods we’ll learn in this text are applicable to the unique data. Actually, the method is precisely the identical as what I did at my job.

Consider now we have a DataFrame of several rows with the next columns:

Each row within the raw issues column comprises a listing of issues in the next format:

"""
"[1-The find_duplicates method is inefficiently using the info structures resulting in high time complexity., 
2- Built-in data structures aren't used efficiently within the generate_meta method.,
3- Within the ExerciseGenerator class, excessive use of worldwide variables may decelerate this system.,
4- The get_all_contributors_for_repo method is just not using built-in…