Heatmap

Seaborn basics

2 min read

Published Oct 7 2025

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.heatmap() creates a colour-coded matrix (grid) to visualise two-dimensional data.
Each cell’s colour represents the value in that position — making it ideal for:

Correlation matrices
Pivot tables
Confusion matrices
Any 2D numerical array or DataFrame

Syntax:

sns.heatmap(

data,

vmin=None,

vmax=None,

cmap=None,

center=None,

robust=False,

annot=False,

fmt=".2g",

annot_kws=None,

linewidths=0,

linecolor="white",

cbar=True,

cbar_kws=None,

square=False,

xticklabels=True,

yticklabels=True,

mask=None,

ax=None,

**kwargs

)

Parameters:

data = 2D dataset (DataFrame, array, or matrix)
vmin, vmax = Value range for colour scaling
cmap = Colour map (e.g., "viridis", "coolwarm", "magma")
center = Value where colourmap is centered (useful for diverging data)
annot = Annotate cells with numeric values
fmt = Format string for annotations (e.g., ".1f")
linewidths, linecolor = Control grid lines between cells
cbar = Show/hide colour bar
cbar_kws = Arguments for colour bar customisation
square = Make cells square-shaped
xticklabels, yticklabels = Show or hide tick labels
mask = Boolean mask to hide specific cells

Basic example

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data)

plt.show()

Creates a heatmap of passengers per month per year. Brighter colours indicate higher values.

Add annotations

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, annot=True, fmt=".0f")

plt.show()

Displays each cell’s numeric value inside the heatmap. fmt controls the text formatting (here, integer).

seaborn heatmap plot annotations example

Change colour palette

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, cmap="YlGnBu") # Yellow-Green-Blue

plt.show()

Colour map options include:

"viridis" (default)
"coolwarm"
"magma"
"cividis"
"crest", "flare", "icefire", etc.

Try sns.color_palette() to preview available palettes.

Add lines between cells

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, linewidths=1, linecolor="white")

plt.show()

Adds visible grid lines between cells for clarity.

Centre the colourmap

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, cmap="coolwarm", center=data.mean().mean())

plt.show()

Centres colours around a specific value (e.g., the dataset mean). Useful for highlighting deviations above or below a midpoint.

Customise the colour scale range

sns.heatmap(data, vmin=100, vmax=600)

Fixes the colour scale between specific min and max values. Ensures consistent comparison across multiple heatmaps.

Hide the colour bar

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, cbar=False)

plt.show()

Removes the colour scale legend on the side.

seaborn heatmap plot no colourbar example

Square cells

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, square=True, cmap="YlOrBr")

plt.show()

Forces the cells to be square-shaped (1:1 aspect ratio). Helpful when displaying symmetric matrices (e.g., correlations).

Show only part of the matrix

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

# mask cells with value < 300

mask = data < 300

sns.heatmap(data, mask=mask, cmap="coolwarm")

plt.show()

Masks (hides) cells based on a condition. Useful for focusing on higher or lower values only.

Hide axis labels

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(data, xticklabels=False, yticklabels=False)

plt.show()

Removes axis tick labels for a cleaner look.

seaborn heatmap plot hide axis labels example

Correlation matrix example

import seaborn as sns

import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")

corr = iris.corr(numeric_only=True)

sns.heatmap(corr, annot=True, cmap="coolwarm", square=True)

plt.title("Correlation Matrix (Iris Dataset)")

plt.show()

Each cell shows the correlation between two numeric variables. Useful for identifying strong relationships or multicollinearity.

seaborn heatmap plot correlation example

Confusion matrix example

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

conf_matrix = pd.DataFrame(

[[50, 2, 1],

[3, 45, 5],

[0, 4, 48]],

index=["Class A", "Class B", "Class C"],

columns=["Pred A", "Pred B", "Pred C"]

)

sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues")

plt.title("Confusion Matrix")

plt.show()

Shows true vs. predicted labels for classification models. The diagonal cells represent correct predictions.

Diverging palette example

import seaborn as sns

import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")

corr = iris.corr(numeric_only=True)

sns.heatmap(corr, annot=True, cmap="RdBu_r", center=0)

plt.title("Centered Correlation Heatmap")

plt.show()

Colours:

Blue = negative correlations
Red = positive correlations
White = near zero (neutral relationship)

Horizontal colour bar

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")

sns.heatmap(

data,

cmap="YlGnBu",

cbar_kws={"orientation": "horizontal", "shrink": 0.8, "label": "Passenger Count"}

)

plt.show()

Moves the color bar below the heatmap and adds a label.

seaborn heatmap plot horizontal colourbar example

Heatmap

Seaborn basics

2 min read

Published Oct 7 2025

Guide Sections

Guide Comments

Basic example

Add annotations

Change colour palette

Add lines between cells

Centre the colourmap

Customise the colour scale range

Hide the colour bar

Square cells

Show only part of the matrix

Hide axis labels

Correlation matrix example

Confusion matrix example

Diverging palette example

Horizontal colour bar

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