Data Scientists Adopt 6 Habits to Keep Notebooks Runnable Under 100 Lines
Updated
Updated · KDnuggets · Sep 23
Data Scientists Adopt 6 Habits to Keep Notebooks Runnable Under 100 Lines
1 articles · Updated · KDnuggets · Sep 23
Summary
Six low-cost practices can keep a data science notebook runnable after “Restart Kernel and Run All,” with the example workflow built in under 100 lines of Pandas.
The habits center on reproducibility and isolation: put every path, seed and threshold in the first cell, write one function per cell, avoid mutating inputs, validate assumptions, run assertions, execute docstring examples, and wrap execution in main().
A 352-row Olympics dataset shows why the checks matter: validation found 3 duplicate rows and 2 planted test athletes, cutting the cleaned file to 347 rows.
Those 2 sentinel records were only 0.6% of the file but shifted Athletics medal rate from 0.245 to 0.213, illustrating how small hidden errors can distort headline results.
The article’s broader point is that notebooks should behave like production code—exportable as scripts, reviewable as diffs, and able to reproduce the same numbers months later.