A Python matplotlib-enhancer library that painlessly creates beautiful default plots with improved aesthetics and color perception.
prettyplotlib is a Python library that enhances matplotlib by providing beautiful default plot styles. It solves the problem of matplotlib's default visualizations being aesthetically unpleasing and potentially misleading by implementing research-backed design principles. The library offers drop-in replacements for common matplotlib plotting functions with improved color schemes and cleaner layouts.
Data scientists, researchers, and analysts who use matplotlib for data visualization and want publication-quality plots without extensive styling code. Particularly useful for scientific communication where clear, accurate visualizations are essential.
Developers choose prettyplotlib because it provides beautiful, perception-optimized defaults with minimal code changes—simply replace matplotlib calls with prettyplotlib equivalents. It's specifically designed to implement modern information design research directly into practical plotting tools.
Painlessly create beautiful matplotlib plots.
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Provides visually appealing plots out-of-the-box, replacing matplotlib's defaults with cleaner designs as shown in side-by-side comparison tables in the README.
Implements Cynthia Brewer's color perception research, using optimized palettes like Set2 for better data interpretation and accessibility.
Removes unnecessary visual elements to focus on data, adhering to Edward Tufte's principles for clearer scientific communication.
Functions as a direct substitute for matplotlib plotting calls, requiring minimal code changes to upgrade visuals quickly.
The author has announced no further maintenance, recommending seaborn instead, meaning bugs won't be fixed and compatibility with newer matplotlib versions may break.
Only supports a subset of matplotlib plot types (e.g., scatter, bar, hist), lacking support for more specialized visualizations like 3D plots or interactive charts.
Requires the brewer2mpl library on top of matplotlib, adding an extra layer that can complicate setup or cause conflicts in dependency management.