Another BI & Programming Blog - Jason Yousef

Pydantic: your bouncer for messy data (with real examples) If you’ve ever had a pipeline blow up because "quantity" showed up as "two" , you already get why validation matters. Meta description suggestion: Learn how to use Pydantic (v2) to validate API inputs, clean data-pipeline rows, load typed settings from environment variables, and produce clear error messages—with practical Python examples. Quick jumps Why validation saves your sanity The problem Pydantic solves Install Examples you can copy/paste Benefits Use cases Wrap-up Why validation saves your sanity Pydantic is a guardrail for input data. You describe the shape using Python type hints, and Pydantic checks it at runtime. When something’s off, you get a clean ValidationError that tells you exactly what faile...

Pytest fixtures: clean tests without the ceremony Pytest makes testing feel less like a chore and more like a safety net—especially once fixtures enter the picture. Meta description suggestion: Learn how Pytest fixtures and markers help you write clean, isolated tests that scale—from simple unit tests to large CI pipelines. Quick jumps Why Pytest clicks The testing problem Using fixtures the right way Markers for test control Pros & cons Wrap-up Why Pytest clicks Pytest is one of those tools that feels obvious after you’ve used it for a bit. Tests are just functions. Assertions read like normal Python. And when you need context—database sessions, config, mock data—you reach for fixtures instead of duct tape. The big idea Pytest fixtures handle setup and teardown for you, so eac...