Scatter Plot with LOWESS Regression 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 scatter plot with a LOWESS (Locally Weighted Scatterplot Smoothing) regression curve overlaid. LOWESS is a non-parametric method that fits smooth curves by performing local weighted regressions at each point, adapting to local data patterns without assuming a specific functional form. This makes it ideal for exploring complex relationships where the underlying pattern is unknown or varies across the data range.

Renders

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

Preview render from the plotnine implementation.

Implementations

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