Why multiple plots per figure matter in Matplotlib - Visual Breakdown

Concept Flow - Why multiple plots per figure matter

Start: Create Figure

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Add Subplot 1

→Plot Data 1

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Add Subplot 2

→Plot Data 2

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Add Subplot 3

→Plot Data 3

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Adjust Layout

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Show Figure with Multiple Plots

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End

We start by creating a figure, then add multiple subplots to it, each with its own data. Finally, we adjust layout and display all plots together.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [1, 2, 3])
plt.show()

This code creates a figure with 4 subplots arranged in 2 rows and 2 columns, then plots different data on two of them and shows the figure.

Execution Table

Step	Action	Result	Figure State
1	Create figure and 2x2 subplots	Figure with 4 empty subplots created	4 empty plots in grid (2 rows x 2 cols)
2	Plot data [1,4,9] on subplot (0,0)	Line plot appears on top-left subplot	Top-left subplot has line plot, others empty
3	Plot data [1,2,3] on subplot (0,1)	Line plot appears on top-right subplot	Top-left and top-right subplots have plots, bottom two empty
4	Show figure	Figure window opens showing 2 plots in 4-subplot grid	All subplots visible; 2 with data, 2 empty
5	End	Execution complete	Figure displayed with multiple plots

💡 All plotting commands executed; figure shown with multiple plots in one window

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
fig	None	Figure object with 4 subplots	Same	Same	Same
axs	None	2x2 array of subplot axes	Top-left subplot has plot	Top-left and top-right subplots have plots	Same

Key Moments - 2 Insights

Why do we use multiple subplots instead of separate figures?

Using multiple subplots in one figure groups related plots together for easy comparison, as shown in execution_table steps 2 and 3 where plots appear side by side.

What happens if we plot on the wrong subplot index?

Plotting on a wrong subplot index will place the plot in an unexpected location or cause an error; execution_table step 2 and 3 show correct indexing for top-left and top-right plots.

Visual Quiz - 3 Questions

Look at the execution_table, what does the figure contain after Step 3?

A Two plots on the top row subplots

B Only one plot on the top-left subplot

C Four plots, one in each subplot

D No plots, all subplots empty

💡 Hint

Refer to the 'Figure State' column in Step 3 of execution_table

At which step does the figure window open to show the plots?

A Step 1

B Step 2

C Step 4

D Step 5

💡 Hint

Check the 'Action' column for when plt.show() is called in execution_table

If we add a plot to axs[1,1], how would the variable 'axs' change after Step 3?

A It would cause an error

B It would remain unchanged with two plots

C It would have three subplots with plots

D It would remove existing plots

💡 Hint

Look at variable_tracker for how 'axs' updates when plots are added

Concept Snapshot

Use plt.subplots(rows, cols) to create multiple plots in one figure.
Each subplot is accessed by axs[row, col].
Plot data on each subplot separately.
Shows related data together for easy comparison.
Call plt.show() once to display all plots.

Full Transcript

This visual execution shows how to create multiple plots in one figure using matplotlib. First, a figure with a grid of subplots is created. Then, data is plotted on specific subplots by indexing the axes array. The figure is displayed with all plots visible together. This helps compare data side by side in one window. Variables 'fig' and 'axs' track the figure and subplot axes. Key moments include why grouping plots matters and how subplot indexing works. The quiz tests understanding of figure state and plotting steps.