Constrained layout vs tight layout in Matplotlib - Hands-On Comparison

Constrained layout vs tight layout in Matplotlib

📖 Scenario: You are creating a simple report with two plots side by side. You want to make sure the plots and their labels do not overlap and look neat.

🎯 Goal: Learn how to use constrained_layout and tight_layout in Matplotlib to improve plot spacing and appearance.

📋 What You'll Learn

Create a figure with two subplots side by side

Use constrained_layout=True in the figure creation

Create a variable use_tight_layout to switch layout methods

Apply tight_layout() if use_tight_layout is True

Print which layout method is used

💡 Why This Matters

🌍 Real World

When creating reports or presentations with multiple plots, good layout ensures the visuals are clear and labels are readable.

💼 Career

Data scientists and analysts often need to present data clearly. Knowing how to control plot layouts helps make professional and understandable charts.

1

Create a figure with two subplots using constrained_layout

Create a Matplotlib figure called fig with constrained_layout=True and two subplots side by side stored in ax1 and ax2. Plot a simple line on each subplot: ax1.plot([1, 2, 3], [1, 4, 9]) and ax2.plot([1, 2, 3], [2, 3, 5]).

Matplotlib

# Create figure with constrained_layout=True and two subplots ax1, ax2
# Plot lines on ax1 and ax2

Hint

Use plt.subplots(1, 2, constrained_layout=True) to create the figure and axes.

2

Add a variable to choose layout method

Create a variable called use_tight_layout and set it to False. This will control if we use tight_layout() later.

Matplotlib

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [2, 3, 5])

# Create variable use_tight_layout and set to False

Hint

Just create a variable use_tight_layout and assign False.

3

Apply tight_layout if use_tight_layout is True

Write an if statement that checks if use_tight_layout is True. If yes, call fig.tight_layout() to adjust the layout.

Matplotlib

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [2, 3, 5])

use_tight_layout = False

# If use_tight_layout is True, call fig.tight_layout()

Hint

Use an if statement to check use_tight_layout and call fig.tight_layout() inside it.

4

Print which layout method is used and show the plot

Print 'Using tight_layout' if use_tight_layout is True, otherwise print 'Using constrained_layout'. Then call plt.show() to display the figure.

Matplotlib

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [2, 3, 5])

use_tight_layout = False

if use_tight_layout:
    fig.tight_layout()

# Print which layout is used
# Show the plot

Hint

Use print() to show which layout is used based on use_tight_layout. Then call plt.show().

Practice

1. What is the main difference between constrained_layout and tight_layout in matplotlib?

easy

A. constrained_layout requires manual spacing input, tight_layout is fully automatic.

B. tight_layout is used only for 3D plots, constrained_layout is for 2D plots.

C. constrained_layout automatically adjusts subplot parameters to fit decorations, while tight_layout adjusts spacing based on subplot contents.

D. tight_layout disables axis labels, constrained_layout keeps them visible.

Solution

1. Step 1: Understand constrained_layout behavior

   constrained_layout automatically adjusts subplot parameters to make sure decorations like labels, titles, and ticks fit well without overlapping.

2. Step 2: Understand tight_layout behavior

   tight_layout adjusts spacing based on the size of subplot contents but may not handle complex decorations as well as constrained_layout.

3. Final Answer:

   constrained_layout automatically adjusts subplot parameters to fit decorations, while tight_layout adjusts spacing based on subplot contents. -> Option C

4. Quick Check:

   Difference in layout adjustment method = A [OK]

Hint: Remember: constrained_layout handles decorations better than tight_layout [OK]

Common Mistakes:

• Thinking tight_layout works only for 3D plots
• Believing constrained_layout needs manual spacing
• Assuming tight_layout disables axis labels

2. Which of the following is the correct way to enable constrained_layout when creating a figure in matplotlib?

easy

A. plt.figure(constrained_layout=True)

B. plt.figure(tight_layout=True)

C. plt.figure(layout='constrained')

D. plt.figure(layout='tight')

Solution

1. Step 1: Recall matplotlib figure creation syntax

   The plt.figure() function accepts a parameter constrained_layout which can be set to True or False.

2. Step 2: Identify correct parameter name

   The correct parameter to enable constrained layout is constrained_layout=True. Other options like tight_layout or layout are invalid parameters.

3. Final Answer:

   plt.figure(constrained_layout=True) -> Option A

4. Quick Check:

   Enable constrained_layout with parameter = A [OK]

Hint: Use constrained_layout=True in plt.figure() to enable it [OK]

Common Mistakes:

• Using tight_layout=True instead of constrained_layout
• Passing layout='constrained' which is invalid
• Confusing parameter names

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

import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, constrained_layout=True)
axs[0, 0].set_title('Top Left')
axs[1, 1].set_xlabel('X axis')
plt.show()

medium

A. Subplots will overlap because constrained_layout does not work with subplots.

B. Subplots will be spaced automatically to avoid overlap of titles and labels.

C. An error will occur because constrained_layout is not a valid argument for subplots.

D. Subplots will have default spacing ignoring titles and labels.

Solution

1. Step 1: Check usage of constrained_layout with subplots

   The constrained_layout=True argument is valid in plt.subplots() and automatically adjusts spacing to avoid overlap.

2. Step 2: Analyze subplot decorations

   Setting a title and xlabel adds decorations that need space. Constrained layout adjusts subplot positions to fit these without overlap.

3. Final Answer:

   Subplots will be spaced automatically to avoid overlap of titles and labels. -> Option B

4. Quick Check:

   constrained_layout adjusts subplot spacing = A [OK]

Hint: constrained_layout=True in subplots avoids overlap automatically [OK]

Common Mistakes:

• Thinking constrained_layout causes errors with subplots
• Assuming default spacing ignores labels
• Confusing constrained_layout with tight_layout

4. Identify the error in the following code that tries to use tight_layout:

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

medium

A. tight_layout is a method and does not take any arguments.

B. tight_layout should be called on ax, not fig.

C. The plot data is invalid for tight_layout.

D. Missing import for tight_layout function.

Solution

1. Step 1: Check method signature of tight_layout

   The tight_layout method of a figure does not accept any positional arguments; it is called as fig.tight_layout().

2. Step 2: Identify incorrect usage

   The code calls fig.tight_layout(True) passing True, which is invalid and will cause a TypeError.

3. Final Answer:

   tight_layout is a method and does not take any arguments. -> Option A

4. Quick Check:

   tight_layout() takes no arguments = A [OK]

Hint: Call fig.tight_layout() without arguments to avoid errors [OK]

Common Mistakes:

• Passing arguments to tight_layout()
• Calling tight_layout on axes instead of figure
• Assuming plot data affects tight_layout usage

5. You have a figure with multiple subplots and complex decorations like legends and colorbars. Which approach is best to ensure all elements fit well without overlap?

hard

A. Disable both layouts and rely on default spacing.

B. Use plt.tight_layout() after plotting and ignore constrained_layout.

C. Manually adjust subplot parameters with plt.subplots_adjust() only.

D. Use fig, axs = plt.subplots(constrained_layout=True) and avoid calling tight_layout.

Solution

1. Step 1: Consider complex decorations impact

   Legends and colorbars add extra space requirements that tight_layout may not handle well.

2. Step 2: Choose layout method for complex figures

   constrained_layout is designed to handle complex decorations automatically, making it the best choice here.

3. Step 3: Evaluate other options

   Using only tight_layout or manual adjustments may require trial and error and can miss some overlaps.

4. Final Answer:

   Use fig, axs = plt.subplots(constrained_layout=True) and avoid calling tight_layout. -> Option D

5. Quick Check:

   Best for complex decorations = A [OK]

Hint: Use constrained_layout=True for complex subplot decorations [OK]

Common Mistakes:

• Relying only on tight_layout for legends and colorbars
• Manually adjusting without layout helpers
• Ignoring layout settings and causing overlaps