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Visualizing categorical data# In the relational plot tutorial we saw how to use different visual representations to show the relationship between multiple variables in a dataset. In the examples, we focused on cases where the main relationship was between two numerical variables. If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialize

An interface for declaratively specifying statistical graphics.

Discovering structure in heatmap data# seaborn components used: set_theme(), load_dataset(), husl_palette(), clustermap() import pandas as pd import seaborn as sns sns.set_theme() # Load the brain networks example dataset df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0) # Select a subset of the networks used_networks = [1, 5, 6, 7, 8, 12, 13, 17] used_columns = (df.columns.ge

Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery

seaborn.FacetGrid# class seaborn.FacetGrid(data, *, row=None, col=None, hue=None, col_wrap=None, sharex=True, sharey=True, height=3, aspect=1, palette=None, row_order=None, col_order=None, hue_order=None, hue_kws=None, dropna=False, legend_out=True, despine=True, margin_titles=False, xlim=None, ylim=None, subplot_kws=None, gridspec_kws=None)# Multi-plot grid for plotting conditional relationships.

seaborn.kdeplot# seaborn.kdeplot(data=None, *, x=None, y=None, hue=None, weights=None, palette=None, hue_order=None, hue_norm=None, color=None, fill=None, multiple='layer', common_norm=True, common_grid=False, cumulative=False, bw_method='scott', bw_adjust=1, warn_singular=True, log_scale=None, levels=10, thresh=0.05, gridsize=200, cut=3, clip=None, legend=True, cbar=False, cbar_ax=None, cbar_kws=

Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter On this page User guide and tutorial# An introduction to seaborn A high-level API for statistical graphics Multivariate views on complex datasets Opinionated defaults and flexible customization API Overview# Overview of seaborn plotting functions Similar functions for similar tasks Figure-level

seaborn.heatmap# seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)# Plot rectangular data as a color-encoded matrix. This is an Axes-level function and will draw the hea

Visualizing distributions of data# An early step in any effort to analyze or model data should be to understand how the variables are distributed. Techniques for distribution visualization can provide quick answers to many important questions. What range do the observations cover? What is their central tendency? Are they heavily skewed in one direction? Is there evidence for bimodality? Are there

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery

Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery