Data Visualization and Exploration with Python
summary
Uses a 1985 car dataset (≈195 rows, mean price $13k median $10k range $5k–$45k) to show concrete recipes: load the Jupyter notebook, use Matplotlib, Pandas, Seaborn, make scatter, hist, box/violin, KDE, joint/pair plots and facet grids, and tune aesthetics (alpha, marker area scaling, color palettes). Demonstrates overplotting problems and fixes (alpha, 2D KDE / hexbin), shows code patterns for figure/axis control, and provides the GitHub notebook link.
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chapters (18)
- 0:00Data visualization intro
- 1:25History of exploratory data analysis
- 4:40Visualizing complex data
- 9:22Loading car dataset
- 15:08Basic Matplotlib/Pandas plots
- 23:36Customizing Matplotlib plots
- 30:44Scatter plots and overplotting
- 37:37Histograms and bar plots
- 43:17Box plots and distributions
- 46:51Seaborn for advanced plots
- 51:35Kernel density and 2D plots
- 1:07:08Beyond 2D with aesthetics
- 1:12:59Transparency and marker size
- 1:25:47Plot aesthetics customization
- 1:29:54Violin plots and split aesthetics
- 1:32:44Multi-axis and scatter matrices
- 1:39:31Facet plotting and grouped data
- 1:47:13Summary and resources
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