API reference
Seaborn API reference
Seaborn statistical visualization
- Identifiers
- 12
- With full guides
- 12
- Types
- 2
Topics covered
- Relational plots
- Distribution plots
- Categorical plots
- Heatmaps
- Pair plots
- FacetGrid
- Themes
- Palettes
Class
| Name | What it does |
|---|---|
FacetGrid | FacetGrid builds a grid of Matplotlib axes for conditional plots, organized by row and column variable levels. An optional hue variable adds colored subsets, after which plotting and formatting methods can be applied. |
Function
| Name | What it does |
|---|---|
scatterplot | Draws a scatter plot where x/y position shows the main relationship and hue, size, and style can encode additional subsets or dimensions. It returns an axes-level Matplotlib plot. |
lineplot | seaborn.lineplot draws a line chart for an x–y relationship and can distinguish subsets with color, width, and style. It aggregates repeated observations by default and shows an estimate with uncertainty. |
barplot | Show aggregate estimates for numeric data as rectangular bars, with optional uncertainty intervals. By default, barplot displays group means with 95% confidence intervals. |
heatmap | seaborn.heatmap displays a rectangular two-dimensional dataset as a color-encoded matrix. It can add annotations, cell borders, labels, masking, and a colorbar. |
histplot | Plot univariate or bivariate histograms that show dataset distributions. histplot supports configurable bins, normalization, grouping, cumulative views, and optional KDE overlays. |
boxplot | Draws box-and-whisker plots that compare quantitative distributions across categorical levels. Quartiles form the box, whiskers follow the configured whis rule, and outlying observations are shown separately. |
pairplot | Creates a figure-level grid of pairwise relationships for numeric variables, with each variable’s marginal distribution on the diagonal. Grouping, variable selection, plot type, sizing, and triangular layouts can be configured. |
catplot | Figure-level interface for categorical plots, combining several axes-level plot types with FacetGrid-based faceting. The kind argument selects the representation, and the returned grid supports further customization. |
set_theme | Configures Seaborn's global visual theme for subsequent Seaborn and Matplotlib plots. It independently controls context, axes style, palette, typography, color shorthand behavior, and Matplotlib rc overrides. |
color_palette | Build a Seaborn palette from a named palette, colormap, palette specification, or explicit colors. It can return discrete colors or a continuous colormap. |
kdeplot | Plots univariate or bivariate distributions with kernel density estimation. It produces smooth probability-density curves or contours and supports grouping, normalization, bandwidth, and display controls. |
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