Apply reproducible analytical pipeline principles to make a Python notebook auditable and re-runnable.
A re-runnable notebook is a lightweight pipeline. Declare inputs Name the files, tables, dates, filters, and owners. Hidden local inputs are the fastest way to lose trust in a result. Run top to bottom The final notebook should restart and run in order. Exploration can be messy; the shared version should not depend on memory of which cells were skipped. Validate assumptions Add small checks for the things that would most damage the answer: row counts, key uniqueness, missingness, date range, or accepted categories. Generate outputs Write tables and figures from code. Avoid manual edits between the notebook and the…
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