Matplotlib Pie Chart: Percentages, Labels, Donuts, and Alternatives
Create readable Matplotlib pie charts with labels, percentages, explode, donut wedges, legends, equal aspect, and better chart alternatives.
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Create readable Matplotlib pie charts with labels, percentages, explode, donut wedges, legends, equal aspect, and better chart alternatives.
Use Matplotlib line patterns, weight, transparency, color, and markers together so plotted series remain clear after export and printing.
Learn Matplotlib boxplot customization in Python, including labels, colors, notches, horizontal plots, whiskers, outliers, means, and figure sizing.
Build Rock Paper Scissors in Python with normalized input, rule mapping, random choice, draw handling, replay flow, and testable game logic.
Use Matplotlib ylim to set y-axis limits, inspect current bounds, preserve automatic scaling, and avoid hiding data with restrictive chart ranges.
Learn the conditional expression syntax, truth-value behavior, precedence, and when a normal if statement is clearer.
Convert Python int to binary with bin(), format(), f-strings, prefixes, padding, negative numbers, and manual division examples.
Make Matplotlib log charts honest by matching the transform to the data domain and labeling the base, limits, ticks, and invalid values.
Control Matplotlib x-axis tick positions, labels, rotation, intervals, date ticks, and locator behavior with clear Axes examples.
Use Matplotlib colormaps as a data contract: match palette type to semantics, fix comparable bounds, label units, and test accessibility.