Learn Heatmap | Plotting with Seaborn

Definition

A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value.

This example uses a heatmap to visualize pairwise correlations between variables.

Creating a Simple Heatmap

seaborn.heatmap() takes a 2D dataset. A common use case is plotting a correlation matrix: given a DataFrame, call .corr() to compute correlations, then pass the resulting matrix to heatmap().


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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
countries_df = pd.read_csv(url, index_col=0)

correlation_matrix = countries_df.corr(numeric_only=True)

sns.heatmap(correlation_matrix)
plt.show()

The correlation matrix is created from numeric columns only (numeric_only=True).

Annotation and Colors

Setting annot=True writes the correlation values inside each cell. We can also pick a colormap using cmap.

Note

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore it in the "Choosing color palettes" article).


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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
countries_df = pd.read_csv(url, index_col=0)

correlation_matrix = countries_df.corr(numeric_only=True)

sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.show()

The color bar on the right can be removed by setting cbar=False.

Study More

In most of the cases that's all you will need from a heatmap customization, however, you can always explore more in heatmap() documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions:


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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/countries_data.csv'
countries_df = pd.read_csv(url, index_col=0)

correlation_matrix = countries_df.corr(numeric_only=True)

sns.heatmap(correlation_matrix, annot=True, cmap='viridis')
plt.xticks(rotation=20)
plt.yticks(rotation=20)

plt.show()

Task

Swipe to start coding

Use the correct method to create a correlation matrix.
Set the argument of the method to include only numeric variables.
Use the correct function to create a heatmap.
Set correlation_matrix to be the data for the heatmap via specifying the first argument.
Add the values in each cell of the matrix via specifying the second argument.
Set the palette (color map) of the heatmap to 'crest' via specifying the third (rightmost) argument.
Rotate x-axis and y-axis ticks by 15 degrees counterclockwise via specifying a keyword argument in xticks() and yticks().

Solution

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Section 5. Chapter 7

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