Introduction to data analytics with pandas
summary
Pandas reads CSVs, converts types with to_numeric with errors="coerce" and datetime parsing, drops rows, then upsamples to daily with resample(...).asfreq() and fills gaps via interpolate(method="time"); a cumulative series becomes daily deltas with diff()+shift(). Joins bring monthly staff counts in and produce a coffees‑per‑person column. Data yields concrete findings: nine contributors, ~657 usable rows after cleaning, seminar Wednesdays and Mondays peak, weekends drop, and machine‑broken periods align with consumption dips.
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chapters (10)
- 0:00Workshop introduction
- 1:47Setting up pandas
- 8:50Cleaning coffee dataset
- 20:18Fixing data types
- 31:29Exploring time series
- 42:13Analyzing contributor habits
- 55:15Resampling time series
- 1:19:32Daily coffee consumption
- 1:35:13Joining datasets
- 1:46:26Machine insights
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