PCA Biplot with Scores and Loading Vectors

A PCA biplot simultaneously displays both observation scores (as points) and variable loadings (as arrows) in the principal component space. This dual representation is essential for interpreting PCA results, revealing how observations relate to each other and which original variables drive the separation along each principal component. The length and direction of loading arrows indicate variable importance and correlation with the components.

Implementations

• PCA Biplot with Scores and Loading Vectors in Altair (Python)
• PCA Biplot with Scores and Loading Vectors in Bokeh (Python)
• PCA Biplot with Scores and Loading Vectors in lets-plot (Python)
• PCA Biplot with Scores and Loading Vectors in Matplotlib (Python)
• PCA Biplot with Scores and Loading Vectors in Plotly (Python)
• PCA Biplot with Scores and Loading Vectors in plotnine (Python)
• PCA Biplot with Scores and Loading Vectors in Pygal (Python)
• PCA Biplot with Scores and Loading Vectors in Seaborn (Python)
• PCA Biplot with Scores and Loading Vectors in ggplot2 (R)