May 16, 2020
Online presentations!
Brianna McHorse, Adventures in Python 3 Type Annotations for Data Science
Python's type flexibility is awesome, until it's not. With a large and complicated code base, it can be difficult to figure out what's going on (or when you've introduced a bug), especially in the context of data science, where the pandas dataframes sometimes go flying about every which way. This is a talk about the process of gently herding a data science team towards better practices, focusing on how type annotations and static type checking can improve your code, make it much easier to understand the code (especially for new contributors), and safeguard against expensive mistakes. We'll also cover the surprisingly painless process of getting a first round of type annotations up and enforcing type checks, even with no prior experience.
Lightning Talks
Brian Doucet: Great Expectations, a leading tool for validating, documenting, and profiling, your data.
Lee Bernick: special_k is a model serialization library designed to be extensible to arbitrary Python machine learning models. I will explain why model serialization and deserialization can be vulnerable to remote code execution and statistical safety/reproducibility issues, and how the library is designed to mitigate those concerns.
We'll record this and put it on our YouTube channel: https://www.youtube.com/user/bostonpython
Meetup link: https://www.meetup.com/bostonpython/events/270513576/