Testing with Pytest for Data Science
summary
Step‑by‑step use of pytest shows actual code, not platitudes: reproduce the Python2 vs Python3 split_bill failure and see tests catch it; run tests locally and on Travis CI across Python versions; use pytest fixtures to load slow data once (three tests drop from ~9s to ~3s); use mock objects to fake a DataFrame shape instead of materializing millions of rows. The tutorial includes runnable examples, a GitHub repo with solutions, and CI logs showing ~200+ unit tests for a real library.
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chapters (8)
- 0:00pytest for data science
- 10:20Testing matters in data science
- 20:50Bill splitting example
- 34:57Basic pytest tests
- 41:02Separating tests from code
- 47:31Fixtures for slow tests
- 1:01:23Mocking objects
- 1:12:38Q&A
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