Differences Between plot, axes, and figure in Matplotlib

Matplotlib is an object-oriented Python library for creating visualizations. It uses three primary objects: the figure, axes, and plot.

The Figure

The figure represents the entire canvas or window in which the visualization will be displayed. It defines the overall size and layout of the canvas, including the margins, background color, and any other global properties.

The Axes

Axes represent a specific area within the figure where data is plotted. They define the coordinate system for plotting, including the axes labels, tick marks, and grid lines. Multiple axes can be created within a single figure to allow for multiple plots.

The Plot

The plot object is used to represent a specific data visualization within an Axes. It can be a line plot, scatter plot, histogram, or any other type of graphical representation. Each plot is associated with a specific Axes object.

Method Invocation

Now, let's examine how these objects interact when using different methods in Matplotlib:

• plt.plot(x, y): This method invokes the plot() method of the hidden Axes object and creates a new plot in the current figure.
• ax = plt.subplot() ax.plot(x, y): This method explicitly creates an Axes object using subplot() and then invokes its plot() method to create a plot in that Axes.
• figure = plt.figure() new_plot = figure.add_subplot(111) new_plot.plot(x, y): This method first creates a Figure object, then adds an Axes object to it using add_subplot(), and finally invokes the plot() method on the new Axes.

Method Selection

The choice of method depends on the requirements of the specific use case:

• plt.plot(): Suitable for quick and simple interactive plots.
• ax.plot(): Useful when you need to access and customize specific Axes properties.
• figure.add_subplot(): Provides more control over the layout and customization of the visualization.

Ultimately, the appropriate method selection depends on factors such as the number of plots, the desired layout, and the need for customizability.