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. While nbmake verifies that a notebook executes without error, testbook lets you write pytest test functions that call specific functions defined in a notebook, inject test data, and assert outputs—the same level of testing you'd apply
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. Install it, point pytest at a directory of notebooks, and every notebook that executes without error passes. Every notebook that raises an exception fails. It is the simplest possible notebook testing setup, and for most teams it is
Python
Jupyter notebooks are the primary artifact for data science work—model training, data exploration, report generation. Yet most teams treat them as write-only documents. Notebooks break silently when dependencies update, when data shapes change, or when code paths that ran during development are skipped during the final run. Testing
Python
Papermill is a tool for parameterizing and executing Jupyter notebooks programmatically. It was designed for production notebook workflows—running the same analysis against different datasets, date ranges, or configuration sets—but it also makes notebooks significantly more testable. By injecting parameters and capturing outputs, you can test a notebook against