How to Easily Create Heatmaps in Python


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

This tutorial explains how to easily create heatmaps in Python using the seaborn.heatmap function.

Related: How to Create Heatmaps in R

Heatmaps in Python

Suppose we have the following dataset in Python that displays the number of sales a certain shop makes during each weekday for five weeks:

import numpy as np
import pandas as pd 
import seaborn as sns

#create a dataset
np.random.seed(0)
data = {'day': np.tile(['Mon', 'Tue', 'Wed', 'Thur', 'Fri'], 5),
        'week': np.repeat([1, 2, 3, 4, 5], 5),
        'sales': np.random.randint(0, 50, size=25)
        }

df = pd.DataFrame(data,columns=['day','week','sales'])
df = df.pivot('day', 'week', 'sales')

view first ten rows of dataset
df[:10]

week	 1	 2	 3	 4	 5
day					
Fri	 3	36	12	46	13
Mon	44	39	23	 1	24
Thur	 3	21	24	23	25
Tue	47	 9	 6	38	17
Wed	 0	19	24	39	37

Basic heatmap:

We can create a basic heatmap using the sns.heatmap() function:

sns.heatmap(df)

Heatmap in Python

The colorbar on the righthand side displays a legend for what values the various colors represent. 

Add lines:

You can add lines between the squares in the heatmap using the linewidths argument:

sns.heatmap(df, linewidths=.5)

Heatmap in seaborn Python

Add annotations:

You can also add annotations to the heatmap using the annot=True argument:

sns.heatmap(df, linewidths=.5, annot=True)

Annotated heatmap in Python

Hide colorbar:

You can also hide the colorbar entirely using the cbar=False option:

sns.heatmap(df, linewidths=.5, annot=True, cbar=False)

Example of a heatmap in Python

Change color theme:

You can also change the color theme using the cmap argument. For example, you could set the colors to range from yellow to green to blue:

sns.heatmap(df, cmap='YlGnBu')

Heatmap python

Or you could have the colors range from red to blue:

sns.heatmap(df, cmap='RdBu')

heatmap with different cmap in Python

For a complete list of colormaps, refer to the matplotlib documentation.

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