plotastic: Bridging Plotting and Statistics in Python
Martin Kuric, Regina Ebert · The Journal of Open Source Software · 2024
plotastic addresses the challenges of transitioning from exploratory data analysis to hypothesis testing in Python's data science ecosystem.Bridging the gap between seaborn and pingouin, this library offers a unified environment for plotting and statistical analysis.It simplifies the workflow with user-friendly syntax and seamless integration with familiar seaborn parameters (y, x, hue, row, col).Inspired by seaborn's consistency, plotastic utilizes a DataAnalysis object to intelligently pass parameters to pingouin statistical functions.Hence, statistics and plotting are performed on the same set of parameters, so that the strength of seaborn in visualizing multidimensional data is extended onto statistical analysis.In essence, plotastic translates seaborn parameters into statistical terms, configures statistical protocols based on intuitive plotting syntax and returns a matplotlib figure with known customization options and more.This approach streamlines data analysis, allowing researchers to focus on correct statistical testing and less about specific syntax and implementations.