Effective Data Analysis with Pandas Indexes
summary
Demonstration shows how Pandas indexing (especially TimeSeriesIndex) changes workflows: timestamps become index objects allowing dot-year/month access, slicing by date strings, and clean resample aggregation (daily/monthly/3D) that yields sensible min/max plots and a weekday/weekend pattern. MultiIndex examples reveal level access and tuple-based slicing. Converting repeated text columns to categorical dtype shrinks memory (example dataset drops to ~40% size; product column to ~3%) while boolean masks and groupby/agg remain straightforward.
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chapters (8)
- 0:00Start
- 0:19Effective Data Analysis with Pandas Indexes
- 2:30Introduction into Ipython
- 4:03Numpy Data Types
- 13:24Prepared Data Set
- 14:13Boolean Index
- 15:23Multi Index
- 20:12The Daytime Index
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