How to Make Heatmaps with Seaborn (With Examples)

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A heatmap is a type of chart that uses different shades of colors to represent data values.

This tutorial explains how to create heatmaps using the Python visualization library with the following dataset:

#import seaborn
import seaborn as sns

#load "flights" dataset
data = sns.load_dataset("flights")
data = data.pivot("month", "year", "passengers")

#view first five rows of dataset
data.head()

Create a Basic Heatmap

We can use the following syntax to create a basic heatmap for this dataset:

sns.heatmap(data)

The x-axis displays the year, the y-axis displays the month, and the color of the squares within the heatmap represent the number of flights in those particular year-month combinations.

Adjust the Size of the Heatmap

We can use the figsize argument to adjust the overall size of the heatmap:

#set heatmap size
import matplotlib.pyplot as plt
plt.figure(figsize = (12,8))

#create heatmap
sns.heatmap(data)

Change the Colors of the Heatmap

We can use the cmap argument to change the colors used in the heatmap. For example, we could choose the “Spectral” color map:

sns.heatmap(data, cmap="Spectral")

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

Find a complete list of cmap options available .

Annotate the Heatmap

We can use the following syntax to annotate each cell in the heatmap with integer formatting and specify the font size:

sns.heatmap(data, annot=True, fmt="d", annot_kws={"size":13})

Modify the Colorbar of the Heatmap

Lastly, we can turn the colorbar off if we’d like using the cbar argument:

sns.heatmap(data, cbar=False)

Find more Seaborn tutorials on .