Introduction to data munging with pandas and IPython Notebook

on YouTube ↗35m

" summary

Shows concrete steps: use IPython/Jupyter Notebook and pd.read_csv dtype for zip to import, inspect and export; create new columns, use apply, round values, run groupby/merge and drop_duplicates, and plot with pandas. Demonstrates handling bad rows (quintuple-zero zip), shows that keeping vs dropping those rows halves average population (about 10,000 vs 5,000), and finds 250 people in zip 33109; builds a merged, reproducible dataset in the notebook with minor live-demo hiccups but working results.

AI-written summary · about

" chapters (8)

1. 0:00Start
2. 2:57Features in the Ipython Notebook
3. 5:21Ipython Notebook
4. 10:23Data Types
5. 24:06Emerging Merging in Pandas
6. 25:43Dropping Duplicates from a Data Frame
7. 27:02Live Demo
8. 33:22Problem of Spatial Adjacency versus Temporal Stacking

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