Matplotlib Plot Hierarchies: When to Use `Plot`, `Axes`, and `Figure`?

Understanding Plot Hierarchies in Matplotlib: Plot vs Axes vs Figure

In Matplotlib, the hierarchy of plot elements includes three primary components: plot, axes, and figure. While all three contribute to the visualization, they serve distinct purposes and offer different levels of control.

Behind the Scenes: How Plots Are Created

When calling the plot function, matplotlib internally creates a figure and axes object. By default, matplotlib maintains a single global figure and associated axes, visible on the screen during interactive sessions.

The axes object, often referred to as the "plot canvas," hosts the plotted data. It houses methods for setting x- and y-axes limits, labels, grid lines, and adding annotations.

The figure object encapsulates the entire visualization, including all the axes objects. It serves as the container for multiple axes and overall figure properties such as size, background color, and margins.

When to Use Each Method?

1. Plot:

• Pros: Convenient and straightforward for basic plots.
• Cons: Limited control over fine-grained plot elements.

2. Axes (with Subplot Function):

• Pros: Provides more control over individual axes elements like labels, scales, and appearance.
• Cons: Requires more explicit handling of multiple axes.

3. Figure:

• Pros: Maximum flexibility and fine-tuning options.
• Cons: More complex code and requires understanding the hierarchy.

Pros and Cons of Each Method

Plot:

• Quick and easy for single plots without advanced customization.

Axes with Subplot:

• Greater control over axes-specific attributes.
• Suitable for subplots where different axes properties are required.

Figure:

• Full control over all aspects of the plot, including figure margins, layout, and subplots.
• Essential for advanced customization or embedding plots into larger applications.

Conclusion

The choice between plot, axes, and figure depends on the desired level of customization and complexity of the plot. Plot is suitable for simple and exploratory data visualization, while axes with subplot offers more control for subplots. Figure provides maximum flexibility for highly customized and complex plots. Understanding the relationships between these elements allows for precise and tailored data visualizations in Matplotlib.

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