Visualising Data through Pandas
summary
Demo shows pandas data frame as the main in‑memory workhorse, using IPython notebooks to load CSVs, resample time series, and run groupby split apply combine aggregations. Plots come from R via rpy2 piping so ggplot2 drives visualization while pandas does the heavy lifting; examples include stock weekly resampling and the chickweight diet experiment. Performance notes: pandas queries are blazingly fast in memory, ggplot2 is concise, and SVG renderers (Vincent) slow down past ~2,000 points.
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chapters (30)
- 0:00Introduction
- 1:15Introduction
- 2:17Python and pandas for analysis
- 3:41Excel vs Python
- 4:24Pandas basics
- 6:47Setting up the environment and importing libraries
- 7:30Numpy arrays to series
- 9:08Series to data frame
- 12:13Querying and filtering
- 15:29Modifying data frames
- 17:29Boolean indexing
- 21:48Grouping and aggregating
- 25:10Time series analysis
- 27:17Resampling time series
- 30:08Split apply combine
- 31:32Case study: chicken weight
- 33:29Loading chick weight data
- 34:10Grouping by diet
- 37:48Custom groupby functions
- 41:16Data visualization with ggplot2
- 42:00Why ggplot2
- 43:04ggplot2 syntax
- 45:16Advanced ggplot2 features
- 47:03Pandas with R
- 48:48Pushing to R and plotting
- 50:56Exploring ggplot2 documentation and examples
- 52:46Python visualization libraries
- 54:09Visualization importance
- 54:55Final exercise
- 56:45Q&A
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