Stacked Bar Chart Python: Matplotlib and Pandas Guide

Basic Stacked Bar Chart with Matplotlib

The fundamental approach uses matplotlib's 'bottom' parameter to stack bars on top of each other:

Stacked Bar Chart with Pandas

Pandas makes stacked bar charts incredibly simple with the plot() method:

Horizontal Stacked Bar Chart

For long category labels or ranked data, use horizontal stacked bars:

100% Stacked Bar Chart (Normalized)

Show proportions instead of absolute values by normalizing data to 100%:

Adding Data Labels to Stacked Bars

Show exact values on each segment for precise reading:

Customizing Colors and Styles

Control every visual aspect of your stacked bar chart:

• Custom colors: Pass a list to color parameter or use colormap
• Edge colors: Add edgecolor='black' for bar borders
• Transparency: Use alpha=0.8 for semi-transparent bars
• Hatching patterns: Add hatch='/' for textured fills (accessibility)
• Color palettes: Use plt.cm.viridis or seaborn palettes

Working with Large Datasets

When working with many categories or large datasets, consider these techniques:

• Limit categories to top N values for readability
• Use horizontal bars for 10+ categories
• Aggregate small segments into 'Others' category
• Consider interactive visualization with Plotly for exploration
• Save as vector format (PDF/SVG) for large charts

Common Pitfalls and Solutions

Avoid these frequent issues when creating Python stacked bar charts:

• Numpy arrays for bottom: Convert lists to numpy arrays for math operations
• Legend order: Reverse legend to match visual stack order with handles[::-1]
• Crowded labels: Use rotation=45 for angled x-axis labels
• Overlapping bars: Ensure all data series have the same length
• Memory issues: Close figures with plt.close() when creating many charts

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