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Data Visualization and Analysis With Seaborn Library

Figure Scaling and Styling

Explore how to adjust figure sizes and plot elements using Seaborn's set_context function for different preset scales like paper or poster. Learn to apply temporary styles and customize axis spines and color palettes to create distinctive plots suited for varied presentation needs.

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Scaling plot elements

Seaborn is a rich visualization library that allows various plot customization options. One such option is a set of parameters that allows us to scale the plot elements. Using this option, we can use the same code to create plots suitable for applications requiring smaller or larger plots.

The seaborn library provides four plot contexts in different sizes. The four preset contexts are as follows:

  • Paper
  • Notebook
  • Talk
  • Poster

By default, seaborn uses the notebook style for its plot, as shown in the plot below:

Python
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
tips_df = sns.load_dataset('tips')
sns.pairplot(hue = 'sex' , data = tips_df)
plt.savefig('output/graph.png')

We can change the preset value by specifying it in the sns.set_context() function. Let’s change it to paper style. We can observe from the plot below that the fonts and figure elements in the paper style are relatively smaller than in the notebook style:

Python
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
sns.set_context('paper')
tips_df = sns.load_dataset('tips')
sns.pairplot(hue = 'sex' , data = tips_df)
plt.savefig('output/graph.png')

Similarly, as shown in the code below, we set the talk context in the sns.set_context() ...