Wie implementiert man Seaborn-Plotfunktionen mithilfe der objektorientierten Schnittstelle von Matplotlib in Python?

How to Utilize Seaborn with Matplotlib's Object-Oriented Interface

For those accustomed to the object-oriented approach in matplotlib, transitioning to seaborn may pose challenges in maintaining control over multiple figures and subplots. This article delves into how to utilize seaborn's plotting functions in an object-oriented manner.

Distinguishing Seaborn Plotting Functions

Seaborn's plotting functions fall into two categories:

• Axes-level: (e.g., regplot, boxplot, kdeplot) Accept an explicit ax argument and return an Axes object.
• Figure-level: (e.g., relplot, catplot, displot) Initialize their own figure and organize multiple Axes in a meaningful way.

Utilizing Axes-level Functions in OOP Style

Axes-level functions allow for direct customization of existing Axes objects:

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

Approaching Figure-level Functions with OOP

Figure-level functions return objects (e.g., FacetGrid for relplot) that provide methods and access to the underlying figure and axes:

<code class="python">g = sns.lmplot(..., ...)
g.fig  # Provides access to the figure
g.axes  # Provides access to the Axes array</code>

Customization Post-Initialization

While Figure-level functions do not allow the specification of an existing figure, customizations can still be applied after calling the function using methods such as g.set_axis_labels(...) and g.set_titles(...).

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