Why multiple plots per figure matter in Matplotlib - Visual Breakdown

Concept Flow - Why multiple plots per figure matter

Start: Create Figure

↓

Add Subplot 1

→Plot Data 1

↓

Add Subplot 2

→Plot Data 2

↓

Add Subplot 3

→Plot Data 3

↓

Adjust Layout

↓

Show Figure with Multiple Plots

↓

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.

Practice

1. Why is it useful to have multiple plots in one figure when analyzing data?

easy

A. It automatically cleans the data for errors.

B. It reduces the total number of data points shown.

C. It allows easy comparison of different data sets side by side.

D. It makes the figure file size smaller.

Solution

1. Step 1: Understand the purpose of multiple plots

   Multiple plots in one figure let us see different data sets or views together.

2. Step 2: Compare data side by side

   This side-by-side view helps spot differences or similarities easily.

3. Final Answer:

   It allows easy comparison of different data sets side by side. -> Option C

4. Quick Check:

   Multiple plots = easy comparison [OK]

Hint: Multiple plots show data side by side for easy comparison [OK]

Common Mistakes:

• Thinking multiple plots reduce data points
• Believing multiple plots clean data automatically
• Assuming multiple plots reduce file size

2. Which of the following is the correct way to create a figure with 2 rows and 3 columns of plots using matplotlib?

easy

A. plt.subplots(3, 2)

B. plt.subplots(2, 3)

C. plt.subplot(2, 3)

D. plt.figure(2, 3)

Solution

1. Step 1: Recall the function for multiple plots

   Use plt.subplots(rows, columns) to create a grid of plots.

2. Step 2: Match rows and columns

   For 2 rows and 3 columns, call plt.subplots(2, 3).

3. Final Answer:

   plt.subplots(2, 3) -> Option B

4. Quick Check:

   plt.subplots(rows, cols) = plt.subplots(2, 3) [OK]

Hint: Use plt.subplots(rows, columns) to create grid [OK]

Common Mistakes:

• Confusing plt.subplot with plt.subplots
• Swapping rows and columns
• Using plt.figure for subplots

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2)
axs[0].plot([1, 2, 3], [4, 5, 6])
axs[1].plot([1, 2, 3], [6, 5, 4])
plt.show()

medium

A. Two separate plots side by side, each with its own line.

B. A blank figure with no plots.

C. An error because axs is not iterable.

D. A single plot with two lines overlapping.

Solution

1. Step 1: Understand plt.subplots(1, 2)

   This creates 1 row and 2 columns of plots, so axs is an array of two axes.

2. Step 2: Plot on each axis separately

   axs[0] and axs[1] each get their own line plot, so two plots appear side by side.

3. Final Answer:

   Two separate plots side by side, each with its own line. -> Option A

4. Quick Check:

   plt.subplots(1,2) + axs[0], axs[1] plots = two side-by-side plots [OK]

Hint: plt.subplots(1,2) creates two side-by-side plots [OK]

Common Mistakes:

• Thinking axs is a single axis, not an array
• Expecting one plot with two lines
• Assuming code causes error

4. Identify the error in this code that tries to create 2 plots in one figure:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(2)
ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. ax is an array but used as a single axis, causing an error.

B. plt.subplots(2) creates only one plot, so ax.plot is invalid.

C. The plot data lists have different lengths causing error.

D. plt.show() is missing and causes no output.

Solution

1. Step 1: Check plt.subplots(2) output

   It creates 2 subplots in one column, so ax is an array of axes.

2. Step 2: Using ax.plot() on array causes error

   ax.plot() expects a single axis, but ax is an array, so this causes an AttributeError.

3. Final Answer:

   ax is an array but used as a single axis, causing an error. -> Option A

4. Quick Check:

   Multiple subplots return array, must index before plot [OK]

Hint: Multiple subplots return array; index before plotting [OK]

Common Mistakes:

• Calling plot on array of axes instead of one axis
• Assuming plt.subplots(2) returns single axis
• Ignoring plt.show() is present

5. You want to compare sales data for 3 products over 4 quarters in one figure. Which approach best uses multiple plots per figure to achieve this?

hard

A. Plot all products on a single plot with overlapping lines and no labels.

B. Use plt.figure() once and call plt.plot() three times without subplots.

C. Create 3 separate figures, each with one product's sales plot.

D. Create a 1x3 grid of plots using plt.subplots(1, 3), plot each product's quarterly sales separately, and label each plot.

Solution

1. Step 1: Understand the goal

   We want to compare 3 products side by side in one figure for clarity.

2. Step 2: Use plt.subplots(1, 3) for side-by-side plots

   This creates 3 plots in one row, perfect for comparing products separately but together.

3. Step 3: Label each plot for clarity

   Clear labels help understand which plot shows which product's sales.

4. Final Answer:

   Create a 1x3 grid of plots using plt.subplots(1, 3), plot each product's quarterly sales separately, and label each plot. -> Option D

5. Quick Check:

   Multiple plots + labels = clear side-by-side comparison [OK]

Hint: Use plt.subplots(1,3) and label each plot for clear comparison [OK]

Common Mistakes:

• Plotting all lines on one plot without labels
• Creating separate figures instead of subplots
• Not labeling plots causing confusion