Probability-Probability (P-P) Plot in Python, R, Julia and JavaScript

Source code in 15 libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts.

A diagnostic plot comparing the cumulative distribution function (CDF) of observed data against a theoretical distribution by plotting empirical CDF values against theoretical CDF values. Unlike Q-Q plots which compare quantiles, P-P plots compare cumulative probabilities on both axes (0 to 1), making them more sensitive to deviations in the center of the distribution. Points falling along the 45-degree diagonal indicate a good fit.

Renders

• Full-resolution render, light theme
• Full-resolution render, dark theme

Preview render from the plotnine implementation.

Implementations

• Probability-Probability (P-P) Plot in Altair (Python)
• Probability-Probability (P-P) Plot in Bokeh (Python)
• Probability-Probability (P-P) Plot in lets-plot (Python)
• Probability-Probability (P-P) Plot in Matplotlib (Python)
• Probability-Probability (P-P) Plot in Plotly (Python)
• Probability-Probability (P-P) Plot in plotnine (Python)
• Probability-Probability (P-P) Plot in Pygal (Python)
• Probability-Probability (P-P) Plot in Seaborn (Python)
• Probability-Probability (P-P) Plot in ggplot2 (R)
• Probability-Probability (P-P) Plot in Makie.jl (Julia)
• Probability-Probability (P-P) Plot in Chart.js (JavaScript)
• Probability-Probability (P-P) Plot in D3.js (JavaScript)
• Probability-Probability (P-P) Plot in Apache ECharts (JavaScript)
• Probability-Probability (P-P) Plot in Highcharts (JavaScript)
• Probability-Probability (P-P) Plot in MUI X Charts (JavaScript)