Sports Data Visualization - course bundle
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Ten chart types, each built on real sports data, ending with the styling pass that makes a figure publishable.

Run order:
 1. 52_stacked_bar_win_loss.py  -  Stacked Bar Charts: Win-Loss Composition
      https://sportsdatatutorials.com/tutorials/stacked-bar-win-loss.html
 2. 54_grouped_bar_chart_comparisons.py  -  Grouped Bar Charts: Compare Two Metrics Side by Side
      https://sportsdatatutorials.com/tutorials/grouped-bar-chart-comparisons.html
 3. 61_pie_and_donut_part_to_whole.py  -  Pie and Donut Charts: Showing Part-to-Whole (and When Not To)
      https://sportsdatatutorials.com/tutorials/pie-and-donut-part-to-whole.html
 4. 58_fill_between_shaded_ranges.py  -  fill_between: Shade Above and Below a Baseline
      https://sportsdatatutorials.com/tutorials/fill-between-shaded-ranges.html
 5. 59_dual_axis_charts_with_twinx.py  -  Dual-Axis Charts: Plot Two Scales with twinx()
      https://sportsdatatutorials.com/tutorials/dual-axis-charts-with-twinx.html
 6. 39_small_multiples_one_chart_per_team.py  -  Small Multiples: Compare Every Team at Once with Subplots
      https://sportsdatatutorials.com/tutorials/small-multiples-one-chart-per-team.html
 7. 51_correlation_heatmap.py  -  A Correlation Heatmap: Which Team Stats Move Together
      https://sportsdatatutorials.com/tutorials/correlation-heatmap.html
 8. 66_shot_density_heatmap_hexbin.py  -  Make a Shot-Density Heatmap with hexbin
      https://sportsdatatutorials.com/tutorials/shot-density-heatmap-hexbin.html
 9. 40_a_standings_bump_chart.py  -  A Standings Bump Chart: Visualizing a Season's Twists
      https://sportsdatatutorials.com/tutorials/a-standings-bump-chart.html
10. 23_publication_ready_charts_matplotlib_styling.py  -  Publication-Ready Charts: matplotlib Styling and Annotations
      https://sportsdatatutorials.com/tutorials/publication-ready-charts-matplotlib-styling.html

Every script runs from this folder as-is:  python <script>.py
The shared helpers and every sample CSV the scripts read are included,
so the whole course works offline.

Course page: https://sportsdatatutorials.com/tracks/data-visualization.html
All datasets documented: https://sportsdatatutorials.com/datasets.html
