Figure-level methods vs axes-level in Matplotlib - Visual Side-by-Side Comparison

Concept Flow - Figure-level methods vs axes-level

Start: Create Figure and Axes

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Use Figure-level method

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Plot affects whole Figure

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Use Axes-level method

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Plot affects only one Axes

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Show or save Figure

First, create a figure and axes. Figure-level methods affect the whole figure, while axes-level methods affect only one subplot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.suptitle('Figure Title')
ax.set_title('Axes Title')
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

This code creates a figure and one axes, sets a title for the figure and the axes, plots data on the axes, and shows the plot.

Execution Table

Step	Action	Target	Effect	Output
1	Create figure and axes	fig, ax	Figure and Axes objects created	fig and ax ready
2	Set figure title	fig.suptitle('Figure Title')	Title set for whole figure	Figure title displayed
3	Set axes title	ax.set_title('Axes Title')	Title set for single axes	Axes title displayed
4	Plot data	ax.plot([1, 2, 3], [1, 4, 9])	Line plot on axes	Line visible on axes
5	Show plot	plt.show()	Display figure window	Figure with titles and plot shown
6	End	-	All commands executed	Plot window open

💡 All plotting commands executed and figure displayed

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
fig	None	Figure object created	Figure title set	Figure title set	Figure title set	Figure with title
ax	None	Axes object created	Axes object unchanged	Axes title set	Line plot added	Axes with title and plot

Key Moments - 2 Insights

Why does fig.suptitle set the title for the whole figure but ax.set_title only sets the title for one subplot?

fig.suptitle applies to the entire figure container, affecting all subplots inside it, while ax.set_title applies only to the specific axes object, so it affects only that subplot. See execution_table steps 2 and 3.

If I call ax.plot multiple times, does it affect the whole figure or just one axes?

Each ax.plot call adds data only to that specific axes, not the whole figure. The figure contains all axes but plotting commands on axes are local. See execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the effect of step 2 (fig.suptitle)?

A Plots data on the axes

B Sets the title for the entire figure

C Sets the title for one axes only

D Creates a new axes

💡 Hint

Check the 'Effect' column in step 2 of the execution_table

At which step is the line plot added to the axes?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for the action 'Plot data' in the execution_table

If you want to add a title to only one subplot, which method should you use?

A ax.set_title()

B fig.suptitle()

C plt.title()

D fig.set_title()

💡 Hint

Refer to the difference between figure-level and axes-level methods in the key_moments

Concept Snapshot

Figure-level methods (like fig.suptitle) affect the whole figure and all subplots.
Axes-level methods (like ax.set_title, ax.plot) affect only one subplot.
Create figure and axes with plt.subplots().
Use fig.suptitle() for overall title.
Use ax.set_title() and ax.plot() for individual subplot control.

Full Transcript

This lesson shows the difference between figure-level and axes-level methods in matplotlib. First, you create a figure and axes using plt.subplots(). Then, figure-level methods like fig.suptitle() set titles or properties for the entire figure, affecting all subplots. Axes-level methods like ax.set_title() and ax.plot() affect only the specific subplot (axes) they are called on. The execution table traces each step: creating figure and axes, setting titles, plotting data, and showing the figure. Variable tracking shows how fig and ax objects change after each step. Key moments clarify common confusions about scope of titles and plotting. The visual quiz tests understanding of which methods affect figure vs axes. This helps beginners see how to control overall figure appearance versus individual subplot details.