Notebook Hygiene: Making Yours Rerunnable

A Jupyter notebook lets you run cells in any order, redefine variables halfway through, and delete a cell without erasing the variables it created. That flexibility is exactly what makes notebooks dangerous for research: it's easy to end up with a notebook that produces the "right" result only because of the specific, undocumented sequence in which cells happened to be run — a sequence nobody, including the author a week later, can reconstruct.

Notebook hygiene is the discipline of keeping a notebook honest: it means the notebook produces the same output when run top-to-bottom from a fresh kernel as it did when you were actively editing it. Practical habits include restarting the kernel and running all cells before treating a result as final; avoiding global mutable state that later cells silently depend on; keeping expensive computations (like loading a large dataset) in early cells so re-running doesn't require redoing hours of work; and moving code that's stabilized into a proper module or script rather than leaving it to live only inside notebook cells.

Worked example. A researcher computes a Sharpe ratio in cell 12, then goes back to cell 4 to widen the date range, forgetting that cell 9 already cached an intermediate dataframe under the old range. Every cell after 9 that reuses that dataframe silently uses stale data — the notebook still runs and produces numbers, but they're wrong, and only a full restart-and-run-all would have caught it.