Categorical plot

Seaborn basics

2 min read

Published Oct 7 2025

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.catplot() is a high-level figure-level function for creating categorical plots in Seaborn.
It combines the features of several categorical plotting functions — like:

sns.stripplot()
sns.swarmplot()
sns.boxplot()
sns.violinplot()
sns.barplot()
sns.countplot()

…and adds faceting support (multiple subplots based on data subsets).

Essentially, catplot() = categorical plot + easy subplotting (faceting).

Syntax:

sns.catplot(

data=None,

x=None,

y=None,

hue=None,

kind="strip",

col=None,

row=None,

order=None,

hue_order=None,

col_order=None,

row_order=None,

height=5,

aspect=1,

palette=None,

orient=None,

dodge=True,

legend=True,

margin_titles=False,

**kwargs

)

Parameters:

data = DataFrame containing your data
x, y = Categorical and numerical variables
hue = Adds subgroups (coloured)
kind = Type of categorical plot to draw ("strip", "swarm", "box", "violin", "bar", "count", "boxen")
col, row = Variables for faceting (creating subplots)
order, hue_order = Category ordering
palette = Colour scheme
height = Height (in inches) of each subplot
aspect = Width = height × aspect
orient = "v" (vertical) or "h" (horizontal)
dodge = Separate hue categories
legend = Show or hide legend
margin_titles = Add titles on the edges of facets

Basic example

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.catplot(data=tips, x="day", y="total_bill", kind="box")

plt.show()

Creates a boxplot showing the distribution of total_bill for each day.

Kinds of categorical plots

Kind	What It Shows	Equivalent Function
"strip"	Individual data points (with possible jitter)	sns.stripplot()
"swarm"	Non-overlapping points	sns.swarmplot()
"box"	Summary statistics (quartiles, median, outliers)	sns.boxplot()
"violin"	Distribution + density + quartiles	sns.violinplot()
"boxen"	Enhanced boxplot for large datasets	sns.boxenplot()
"bar"	Aggregated values (mean + CI)	sns.barplot()
"count"	Counts of each category	sns.countplot()