Joint plot

Seaborn basics

2 min read

Published Oct 7 2025

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.jointplot() visualises the relationship between two variables along with their marginal distributions.

It combines:

Scatterplot / Hexbin / KDE / Regression in the centre, and
Histograms or density plots on the top and right axes (marginals).

It’s figure-level, meaning it creates its own figure with multiple axes.

Syntax:

sns.jointplot(

data=None,

x=None,

y=None,

hue=None,

kind="scatter",

palette=None,

height=6,

ratio=5,

marginal_ticks=False,

joint_kws=None,

marginal_kws=None,

dropna=True,

space=0.2,

xlim=None,

ylim=None,

color=None,

**kwargs

)

Parameters:

data = DataFrame containing the data
x, y = Numeric variables for the joint plot
hue = Grouping variable to colour points
kind = Type of central plot: "scatter", "kde", "hist", "hex", "reg"
palette = Colour palette for hue
height = Size (inches) of the joint plot (square)
ratio = Size ratio of joint axes to marginal axes
marginal_ticks = Show tick marks on marginal plots
joint_kws = Keyword arguments for the central plot
marginal_kws = Keyword arguments for the marginal plots
dropna = Drop missing values
space = Space between joint and marginal axes
xlim, ylim = Limits for x and y axes
color = Colour of points or lines if hue is not used

Basic example

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip")

plt.show()

Central scatterplot of total_bill vs tip, Marginal histograms show distributions of each variable

Central plot type `'reg'`

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", kind="reg")

plt.show()

Central plot type `'kde'`

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", kind="kde", fill=True)

plt.show()

Central plot type `'hex'`

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", kind="hex", gridsize=25)

plt.show()

Central plot type `'hist'`

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", kind="hist")

plt.show()

Add hue (categorical colouring)

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", hue="sex", kind="scatter", palette="Set1")

plt.show()

Colours points by category. Marginal distributions also coloured by hue.

Customise marginals

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(

data=tips,

x="total_bill",

y="tip",

kind="scatter",

marginal_kws=dict(bins=20, fill=True, alpha=0.5)

)

plt.show()

Adjust histogram bins, fill, transparency, etc.

seaborn joint plot customise marginals example

Control central plot appearance

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(

data=tips,

x="total_bill",

y="tip",

kind="scatter",

joint_kws=dict(alpha=0.6, s=50, color="green")

)

plt.show()

Change point size (s), transparency (alpha), and coluor.

seaborn joint plot customise central example

Adjust size and margins

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.jointplot(data=tips, x="total_bill", y="tip", height=8, ratio=2)

plt.show()

height → size of square joint plot, ratio → relative size of joint axes vs marginal axes

Joint plot

Seaborn basics

2 min read