I finally understood why my time-series aggregation was silently wrong — and how Polars' group_by_dynamic fixed it # Resampling Minute-by-Minute Data into Hourly Summaries If you've ever needed to convert minute-by-minute data into hourly summaries, that's called resampling — aggregating fine-grained time series data into coarser time buckets. It sounds simple. It's not always. Here's the trap I fell into. I had a table with timestamps, a category column (Product A and Product B in the same dataset), and a price column. I wanted hourly aggregates per category. The catch: a new category sometimes starts mid-hour. If you just group by the time bucket, you silently mix rows from two different categories into the same aggregate. No errors. Wrong numbers. In pandas, I was doing a groupby + resample combo and getting subtle corruptions at exactly those category-switch boundaries. Took me embarrassingly long to notice. Polars has a clean answer: **group\_by\_dynamic** with a **group\_by** parameter.…