KDE - kernel density estimate

Seaborn basics

2 min read

Published Oct 7 2025, updated Aug 17 2026

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seaborn.kdeplot() draws a Kernel Density Estimate (KDE) plot — a smoothed version of a histogram.

Instead of showing discrete bins, it estimates the probability density function (PDF) of a continuous variable.
This makes it perfect for visualising:

The shape of a distribution
Comparisons between multiple distributions
Smoothed trends rather than raw counts

Syntax:

sns.kdeplot(    data=None,    x=None,    y=None,    hue=None,    fill=False,    multiple="layer",    common_norm=True,    common_grid=False,    bw_adjust=1,    cut=3,    clip=None,    gridsize=200,    thresh=0.05,    levels=10,    cmap=None,    shade=None,  # deprecated, use fill    ax=None,    **kwargs)

Parameters:

data = DataFrame containing the data
x, y = Variables for 1D or 2D density
hue = Adds separate KDEs for subgroups
fill = Fill the area under the curve (default False)
multiple = How multiple hues are displayed ("layer", "stack", "fill")
common_norm = Whether densities are normalised together or separately
bw_adjust = Bandwidth adjustment (controls smoothness)
cut = Extent of curve beyond data range
gridsize = Number of evaluation points (resolution)
cmap = Colourmap for 2D plots
levels = Number of contour levels (for 2D plots)

Basic example

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill")plt.show()

Shows a smooth density curve of the total_bill variable - similar to a histogram, but continuous.

Fill the area under the curve

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", fill=True, color="skyblue")plt.show()

Fills the area under the KDE curve — great for visual clarity.

Add multiple distributions with hue

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", hue="sex", fill=True, palette="Set2")plt.show()

Draws one curve per group (Male vs. Female), coloured separately.

Stack or normalise multiple distributions

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(    data=tips,    x="total_bill",    hue="day",    multiple="stack",     fill=True,    palette="coolwarm")plt.show()

Options for multiple:

"layer" = Overlapping curves (default)
"stack" = Stacked densities
"fill" = Stacked and normalised to 100% height

Adjust smoothness (bandwidth)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", bw_adjust=0.5, fill=True)plt.show()

Smaller bw_adjust → more detail (wigglier curve)
Larger bw_adjust → smoother (less detailed)

2D KDE plot (Bivariate Distribution)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", y="tip")plt.show()

Displays a contour plot showing where data points are most dense (darker = higher density).

2D KDE with filled contours

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(    data=tips,    x="total_bill",    y="tip",    fill=True,    cmap="mako")plt.show()

Adds filled contours, similar to a topographic heatmap.

Hue in 2D KDE

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(    data=tips,    x="total_bill",    y="tip",    hue="sex",    fill=True,    cmap="coolwarm")plt.show()

One filled contour per hue group — useful for comparison.

Clip KDE to data range

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", fill=True, clip=(0, 60))plt.show()

Restricts the KDE curve to a specific range.

Cumulative distribution (CDF)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", cumulative=True, fill=True, color="lightgreen")plt.show()

Shows how the cumulative probability increases across values.

Orientation (horizontal)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, y="total_bill", fill=True, color="tomato")plt.show()

Flip orientation by using y instead of x.

Control density extent (cut)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.kdeplot(data=tips, x="total_bill", cut=0, fill=True)plt.show()

Prevents the curve from extending beyond the actual data range.