Shared axes between subplots in Matplotlib - Step-by-Step Execution

Concept Flow - Shared axes between subplots

Create Figure

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Create Subplots with shared axes

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Plot data on each subplot

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Axes share limits and ticks

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Display combined plot

This flow shows creating a figure with multiple subplots that share x or y axes, so zooming or panning one affects the others.

Execution Sample

Matplotlib

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

Creates two vertical subplots sharing the x-axis and plots different data on each.

Execution Table

Step	Action	Result	Notes
1	Call plt.subplots(2,1, sharex=True)	Figure with 2 subplots created	Subplots share x-axis limits and ticks
2	Plot on axs[0]	Line plotted on top subplot	X-axis range set to [1,3], y-axis auto-scaled
3	Plot on axs[1]	Line plotted on bottom subplot	X-axis shared with top subplot
4	Display plot with plt.show()	Window opens showing 2 plots stacked vertically	X-axis ticks aligned and zoom/pan synced
5	User zooms x-axis on one subplot	Both subplots update x-axis range	Shared axis behavior confirmed
6	User zooms y-axis on one subplot	Only that subplot y-axis changes	Y-axis not shared, independent scaling
7	Execution ends	Plot window remains open until closed	End of script

💡 Plot window stays open until user closes it, script ends after plt.show()

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
fig	None	Figure object created	Same	Same	Same
axs	None	Array of 2 Axes objects	axs[0] has line plotted	axs[1] has line plotted	Same
x-axis limits	None	[1,3]	[1,3]	[1,3]	[1,3] shared
y-axis limits axs[0]	None	Auto	Auto scaled to [1,9]	Same	Same
y-axis limits axs[1]	None	Auto	Auto	Auto scaled to [2,4]	Same

Key Moments - 2 Insights

Why do both subplots update their x-axis when I zoom one but not the y-axis?

Because sharex=True links the x-axes of both subplots, so zooming one updates both. The y-axes are independent since sharey was not set, so only the zoomed subplot's y-axis changes. See execution_table rows 5 and 6.

What happens if I set sharey=True instead of sharex=True?

Then the y-axes will be linked and zooming or panning vertically on one subplot updates both y-axes. The x-axes remain independent. This is the opposite of the current example where only x is shared.

Visual Quiz - 3 Questions

Look at the execution table, what is the x-axis limit after plotting on both subplots?

A [1, 9]

B [0, 10]

C [1, 3]

D Auto-scaled independently

💡 Hint

Check the 'x-axis limits' row in variable_tracker after Step 3

At which step does the bottom subplot get its line plotted?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look at the 'Action' column in execution_table for plotting on axs[1]

If sharex=False, how would the x-axis behavior change when zooming one subplot?

A Both subplots zoom together

B Only the zoomed subplot's x-axis changes

C Neither subplot zooms

D Both subplots zoom but y-axis stays fixed

💡 Hint

Recall that sharex=True links x-axes; without it, axes are independent

Concept Snapshot

Use plt.subplots(rows, cols, sharex=True/False, sharey=True/False) to create subplots that share axes.
Shared axes sync limits and ticks between subplots.
Zooming or panning one shared axis updates all linked subplots.
Unshared axes remain independent.
Common for comparing data with aligned scales.

Full Transcript

This lesson shows how to create multiple subplots in matplotlib that share axes. We start by creating a figure with two vertical subplots sharing the x-axis using plt.subplots(2,1,sharex=True). We plot different lines on each subplot. Because the x-axis is shared, zooming or panning the x-axis on one subplot updates the other automatically. The y-axes are independent, so zooming vertically affects only the subplot zoomed. The execution table traces each step: creating subplots, plotting lines, displaying the figure, and user interactions. The variable tracker shows how axes limits change. Key moments clarify why shared axes sync only the specified axis. The quiz tests understanding of axis limits, plotting steps, and behavior changes if sharing is disabled. This helps beginners see how shared axes work visually and practically.

Practice

1. What is the main benefit of using sharex or sharey when creating subplots in matplotlib?

easy

A. It changes the color of the axes to match across subplots.

B. It makes the x or y axes use the same scale across subplots for easier comparison.

C. It automatically adds titles to each subplot.

D. It increases the size of the figure window.

Solution

1. Step 1: Understand the purpose of shared axes

   Shared axes mean multiple plots use the same scale on x or y axis, making it easier to compare data visually.

2. Step 2: Identify what sharex and sharey do

   These parameters in plt.subplots() link the axes scales so they stay consistent across subplots.

3. Final Answer:

   It makes the x or y axes use the same scale across subplots for easier comparison. -> Option B

4. Quick Check:

   Shared axes = same scale for easy comparison [OK]

Hint: Shared axes keep scales same for easy visual comparison [OK]

Common Mistakes:

• Thinking shared axes change colors
• Assuming shared axes add titles automatically
• Believing shared axes affect figure size

2. Which of the following is the correct way to create two subplots sharing the y-axis using matplotlib?

easy

A. plt.subplots(1, 2, sharex=false)

B. plt.subplots(2, 1, sharex=true)

C. plt.subplots(1, 2, sharey=false)

D. plt.subplots(2, 1, sharey=true)

Solution

1. Step 1: Identify the parameter for sharing y-axis

   The parameter sharey=true shares the y-axis between subplots.

2. Step 2: Check the subplot layout

   2, 1 means 2 rows and 1 column, which is valid for vertical stacking with shared y-axis.

3. Final Answer:

   plt.subplots(2, 1, sharey=true) -> Option D

4. Quick Check:

   sharey=true shares y-axis [OK]

Hint: Use sharey=true for vertical subplots sharing y-axis [OK]

Common Mistakes:

• Using sharex instead of sharey
• Mixing rows and columns incorrectly
• Setting sharey=false when sharing is needed

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

import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, sharex=True)
axs[0].plot([1, 2, 3], [10, 20, 30])
axs[1].plot([1, 2, 3], [15, 25, 35])
print(axs[1].get_xlim())

medium

A. (0.9, 3.1)

B. (10.0, 35.0)

C. (1.0, 3.0)

D. (15.0, 35.0)

Solution

1. Step 1: Understand sharex=true effect

   With sharex=True, both subplots share the same x-axis limits.

2. Step 2: Check data x-values and default limits

   The x data is [1, 2, 3]. Matplotlib adds a small margin, so limits are slightly beyond 1 and 3, e.g., (0.9, 3.1).

3. Final Answer:

   (0.9, 3.1) -> Option A

4. Quick Check:

   Shared x-axis limits include margin around data [OK]

Hint: Shared axes use same limits with small margin [OK]

Common Mistakes:

• Confusing x and y limits
• Expecting exact data min/max as limits
• Ignoring margin matplotlib adds

4. Consider this code:

fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
axs[0, 0].plot([1, 2], [3, 4])
axs[1, 1].plot([1, 2], [5, 6])
axs[1, 0].set_xlim(0, 3)
print(axs[0, 1].get_xlim())

What is the likely output and why might it cause confusion?

medium

A. Error because set_xlim cannot be used with shared axes.

B. (1.0, 2.0) because each subplot keeps its own limits despite sharing.

C. (0.0, 3.0) because all x-axes are shared and limits set on one affect all.

D. (3.0, 0.0) because limits are reversed causing an error.

Solution

1. Step 1: Understand sharex and sharey effect on limits

   Sharing axes means setting limits on one subplot updates all linked subplots.

2. Step 2: Analyze set_xlim call and its effect

   Setting set_xlim(0, 3) on axs[1, 0] changes x-axis limits for all subplots sharing x-axis.

3. Final Answer:

   (0.0, 3.0) because all x-axes are shared and limits set on one affect all. -> Option C

4. Quick Check:

   Shared axes share limits changes [OK]

Hint: Changing limits on one shared axis updates all linked subplots [OK]

Common Mistakes:

• Thinking each subplot keeps independent limits
• Expecting error from set_xlim with shared axes
• Confusing reversed limits as error

5. You want to create a 2x2 grid of subplots where the top row shares the x-axis, the left column shares the y-axis, but the bottom row and right column do not share axes. Which code snippet correctly achieves this?

hard

A. Use plt.subplots(2, 2, sharex='row', sharey='col') to share x in rows and y in columns.

B. Create subplots with sharex='col' and sharey='row' then manually adjust axes sharing.

C. Use plt.subplots(2, 2, sharex='col', sharey='row') to share x in columns and y in rows.

D. Create subplots without sharing and link axes manually with ax.sharex() and ax.sharey().

Solution

1. Step 1: Understand sharex and sharey options

   In plt.subplots(), sharex='col' shares x-axis within each column, and sharey='row' shares y-axis within each row.

2. Step 2: Match requirement to parameters

   We want top row to share x-axis (same x in row), so sharex='row' is needed. Left column shares y-axis (same y in column), so sharey='col' is needed.

3. Step 3: Identify correct code

   Use plt.subplots(2, 2, sharex='col', sharey='row') to share x in columns and y in rows. uses sharex='col' and sharey='row', which is reversed. Use plt.subplots(2, 2, sharex='row', sharey='col') to share x in rows and y in columns. uses sharex='row' and sharey='col', which matches the requirement.

4. Final Answer:

   Use plt.subplots(2, 2, sharex='row', sharey='col') to share x in rows and y in columns. -> Option A

5. Quick Check:

   sharex='row' and sharey='col' matches sharing needs [OK]

Hint: sharex='row' shares x in rows; sharey='col' shares y in columns [OK]

Common Mistakes:

• Confusing 'row' and 'col' values for sharex/sharey
• Assuming sharex='col' shares x in rows
• Trying to manually link axes unnecessarily