Pandas vs SQL: 3.2x Speed Gap in Real Data Cleaning Jobs

#pandas #sql #datacleaning #postgres

SQL Won By a Mile. Then I Ran It Again.

I ran the same data cleaning job in Pandas and SQL expecting Pandas to edge ahead on small datasets. The opposite happened — PostgreSQL finished in 1.8 seconds while Pandas took 5.9 seconds on a 500k-row CSV with messy nulls, duplicates, and type mismatches. The gap widened to 3.2x on 2 million rows.

This contradicts the "use SQL for big data, Pandas for small" advice you see everywhere. The reality depends on what you're actually doing. Filtering and joins? SQL wins at any scale. Complex string parsing or regex-heavy transformations? Pandas pulls ahead because Python's string methods are richer than SQL's.

I'm sharing side-by-side code for five common cleaning tasks: deduplication, null handling, type conversion, outlier filtering, and date parsing. You'll see exact timings, memory footprints, and the specific edge cases where each tool chokes.