Testing
Data transformation bugs are silent killers. A shape mismatch, an off-by-one in a rolling window, a dtype coercion you didn't notice — these don't raise exceptions. They silently corrupt downstream models, dashboards, and decisions. This guide focuses on pytest patterns specifically for pandas and numpy
Python
testbook bridges the gap between notebook exploration and software engineering rigor.
Python
Reproducibility is the foundation of scientific computing. A notebook that produces a result should produce the same result when re-executed—same inputs, same code, same environment. nbval enforces this by re-running a notebook and comparing each cell's output to the recorded output in the .ipynb file.
Python
nbmake is a pytest plugin that treats Jupyter notebooks as test cases.
Python
Jupyter notebooks are the primary artifact for data science work—model training, data exploration, report generation.
Python
Papermill is a tool for parameterizing and executing Jupyter notebooks programmatically.