Shared axes between subplots in Matplotlib - Mini Project: Build & Apply

Shared axes between subplots

📖 Scenario: You are analyzing sales data for two different products over a week. You want to compare their daily sales side by side using charts that share the same vertical scale to make comparison easier.

🎯 Goal: Create two line charts side by side using matplotlib that share the same y-axis. This will help you visually compare the sales trends of the two products on the same scale.

📋 What You'll Learn

Create a dictionary called sales_data with daily sales for two products.

Create a variable called days listing the days of the week.

Create two subplots side by side that share the y-axis.

Plot sales for each product on its own subplot.

Add titles to each subplot and label the x-axis with days.

💡 Why This Matters

🌍 Real World

Sharing axes between subplots is useful when comparing related data sets visually, such as sales trends, temperature changes, or stock prices over time.

💼 Career

Data scientists and analysts often create shared-axis plots to make comparisons clearer and to present data insights effectively to stakeholders.

1

Create the sales data and days list

Create a dictionary called sales_data with these exact entries: 'Product A': [10, 15, 13, 17, 20, 22, 19] and 'Product B': [12, 14, 16, 15, 18, 21, 20]. Also create a list called days with these exact values: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'].

Matplotlib

# Create sales_data dictionary and days list
# Your code here

Hint

Use a dictionary for sales_data with two keys and lists of numbers as values. Use a list for days.

2

Create subplots with shared y-axis

Import matplotlib.pyplot as plt. Then create two subplots side by side using plt.subplots with nrows=1, ncols=2, and sharey=True. Store the result in variables fig and axes.

Matplotlib

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
# Import matplotlib.pyplot as plt and create two subplots side by side sharing y-axis
# Your code here

Hint

Use plt.subplots(nrows=1, ncols=2, sharey=True) to create the subplots.

3

Plot sales data on each subplot

Use axes[0].plot(days, sales_data['Product A']) to plot Product A sales on the first subplot. Use axes[1].plot(days, sales_data['Product B']) to plot Product B sales on the second subplot. Add titles to each subplot with axes[0].set_title('Product A Sales') and axes[1].set_title('Product B Sales'). Label the x-axis on both subplots with axes[0].set_xlabel('Day') and axes[1].set_xlabel('Day').

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True)
# Plot sales for Product A on axes[0] and Product B on axes[1]
# Add titles and x-axis labels
# Your code here

Hint

Use axes[0] and axes[1] to plot and label each subplot separately.

4

Display the plot

Use plt.show() to display the figure with the two subplots.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True)

axes[0].plot(days, sales_data['Product A'])
axes[0].set_title('Product A Sales')
axes[0].set_xlabel('Day')

axes[1].plot(days, sales_data['Product B'])
axes[1].set_title('Product B Sales')
axes[1].set_xlabel('Day')
# Display the plot
# Your code here

Hint

Call plt.show() to display the figure.

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