Scatter Plot with Polynomial 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 displaying the relationship between two numeric variables with a fitted polynomial regression curve (degree 2-4). This visualization extends beyond linear regression to capture non-linear relationships in data, making it ideal for modeling curved trends, parabolic patterns, and complex data relationships where a straight line would not adequately represent the underlying pattern.

Renders

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

Preview render from the Apache ECharts implementation.

Implementations

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