A gentle introduction to Pandas timeseries and Seaborn
summary
Uses Pandas and Seaborn in a Jupyter/Anaconda stack to manipulate a German utilities CSV (~4,383 rows over ~2006–2017) and shows concrete code: to_datetime parsing, set_index on dates, resample('W').mean() downsampling, 7‑day and 365‑day rolling means, autocorrelation and lag plots, boxplots by weekday, and computing wind+solar/consumption share. Demonstrates missing wind/solar early in the record, holiday dips around December, masking vs query quirks with spaces, and recommends Prophet for forecasting.
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chapters (9)
- 0:00Introduction
- 10:05Setup Pandas and Seaborn
- 23:22Datetime objects in Pandas
- 35:36Load time series data
- 41:30Visualize with Seaborn
- 52:18Seasonality and weekly patterns
- 1:04:26Resampling and rolling averages
- 1:17:54Green energy share
- 1:22:32Summary and resources
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