Extending Libraries/Packages

Here are a couple of examples of how I make my data science workflow a little more convenient as it relates to extracting, cleaning, and summarizing data with pandas and SQLalchemy, but especially pandas_flavor (yum). We never want to store intermediate data sets in production code if it can be avoided, which is the major motivation for chaining methods in a scalable data science workflow. While you can "monkey-patch" a library to 'add' a method you would like to chain, this is not the best practice and can backfire when sharing your ecosystem, attempting to avoid version control issues, or possibly overriding a pre-existing method yikes! Ultimately, the biggest motivation for extending a package compared to just writing the same method as a function in your script is that it allows you to reliably chain your custom methods. then just import your package with the method you like as one might expect:

For example: from <package>.<folder_containing_your_extended_methods> import <your_method>

Pandas Flavoring and Decorators: See time_series.py for Use case

• Many data scientists manipulate data in Python through a classic combo of NumPy and Pandas (although Polars are getting pretty enticing...)
• Pandas is built on the Data.Frame data structure. While we are making extensions to the Pandas library, it does not know these methods operate on a Data.Frame. I can't chain!!! Infinite sadness ensues
• pandas_flavor to the rescue!
  • We can use a decorator to modify the behavior of my summarize_by_time method (or any method; Decorators are wildly useful)
  • In using the pandas_flavor library, we can register the summarize_by_time as a method that takes a Data.Frame object with @pf.register_dataframe_method Crisis averted, we can now df.summarize_by_time(...) where the data argument is already registered as df. Chain away, dear readers
• An insightful article about how pandas_flavor can make your workflow a little smoother see this great Medium Article by Luke Garzia
• Similar in flavor (heh): the Pandas pipe() method with some solid examples in the documentation
  • Note: you'll still have to pandas flavor it up depending on what methods (ie summarize_by_time) you are piping.

Happy Chaining!