How to Use Seaborn with Matplotlib\'s OOP Interface?

Plotting with Seaborn using the Matplotlib OOP Interface

Seaborn provides a convenient interface for creating statistical graphics in Python. However, some users prefer to work with matplotlib using an object-oriented (OOP) approach. This article aims to demonstrate how to achieve this in Seaborn.

Axes-Level Functions

Axes-level functions in Seaborn, such as regplot, boxplot, and kdeplot, can be directly passed an Axes object to plot on. This allows for easy integration with an OOP workflow:

<code class="python">import matplotlib.pyplot as plt
import seaborn as sns

f, (ax1, ax2) = plt.subplots(2)
sns.regplot(x, y, ax=ax1)
sns.kdeplot(x, ax=ax2)</code>

Figure-Level Functions

Figure-level functions in Seaborn, such as relplot, catplot, and lmplot, generate plots that can include multiple Axes. These functions cannot be passed an existing Axes object. However, once called, they return an object (e.g., FacetGrid) that exposes the underlying Figure and Axes.

<code class="python">import seaborn as sns

g = sns.lmplot(x, y)
g.fig  # Returns the Figure object
g.axes  # Returns an array of Axes objects</code>

Customization of figure-level plots must be done after calling the function.

Conclusion

By utilizing the OOP interfaces provided by both matplotlib and Seaborn, it's possible to achieve a high level of control and flexibility when creating statistical graphics in Python. Axes-level functions allow direct integration with matplotlib's OOP approach, while figure-level functions offer more complex and comprehensive plotting capabilities.

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