Refactoring Pandas

Have you ever needed to come back to your Pandas code and been at a complete loss? Have you needed to rewrite confusing Pandas code and make sure that it still "worked"? Have you been asked to ensure that your Pandas code works? Do you need to deploy your Pandas code? If you answered "yes", this course is for you.

Refactoring Real-World Data

You've read the books, seen the Medium articles, and scoured the documentation. It all looks so simple. Yet when you start working with real-world data, it looks so messy. Your notebook is hard to manage. Your code is confusing. Collaboration is hard. You wonder how it ever worked.

This course will rip apart real code by looking at financial data, not just random dummy data. We will show a process to

Read the code
Discover issues with the code
Figure out an approach to clean the code
Reformat notebooks to make them easy to use
Update the code and validate results
Determine whether Numpy provides a benefit
Create intermediate objects that we need to make calculations for our final data
Use functions to make production code
Leverage Pytest and IPytest to ensure the code works

I'm OK with basic Pandas but I struggle to handle complex Pandas

Pandas can be tricky. Writing good Pandas code takes time and effort to use best practices. We will walk through a real-world example and show you a process that you can use to write better code. We even show how to test your code to ensure it works.

Then you can use these techniques and try them out in the lab. You can compare your results with the walkthrough.

Write Tests!

Leverage modern Python tooling to test Pandas code! Yes, novel, no one is talking about it. But you should really leverage tests to have confidence in your code.