Figure-level methods vs axes-level in Matplotlib - Hands-On Comparison

Figure-level methods vs axes-level in matplotlib

📖 Scenario: You are working with sales data for three products over four months. You want to visualize this data using bar charts to compare sales.

🎯 Goal: Build two bar charts: one using figure-level methods and one using axes-level methods in matplotlib. Understand the difference between these two approaches.

📋 What You'll Learn

Create a dictionary called sales_data with product names as keys and lists of monthly sales as values

Create a list called months with the names of the four months

Create a figure and axes using plt.subplots()

Use axes-level method ax.bar() to plot sales for one product

Use figure-level method plt.bar() to plot sales for another product

Add titles and labels to both plots

Print the figure and axes objects to see their types

💡 Why This Matters

🌍 Real World

Data scientists often need to create clear and informative charts. Knowing when to use figure-level or axes-level methods helps in customizing plots effectively.

💼 Career

Understanding matplotlib's figure and axes concepts is essential for data visualization tasks in data science and analytics jobs.

1

Create the sales data and months list

Create a dictionary called sales_data with these exact entries: 'Product A': [10, 15, 7, 12], 'Product B': [5, 8, 12, 9], and 'Product C': [12, 9, 11, 14]. Also create a list called months with these exact values: 'Jan', 'Feb', 'Mar', 'Apr'.

Matplotlib

# Create sales_data dictionary and months list
# Your code here

Hint

Use curly braces {} to create the dictionary and square brackets [] for the lists.

2

Create a figure and axes with plt.subplots()

Import matplotlib.pyplot as plt. Then create a figure and axes by calling plt.subplots() and assign them to variables fig and ax.

Matplotlib

import matplotlib.pyplot as plt

# Create figure and axes using plt.subplots()
# Your code here

Hint

Use import matplotlib.pyplot as plt and then fig, ax = plt.subplots().

3

Plot sales for Product A using axes-level method and Product B using figure-level method

Use the axes-level method ax.bar() to plot sales for 'Product A' with months on the x-axis and sales on the y-axis. Then use the figure-level method plt.bar() to plot sales for 'Product B' with the same x-axis. Add titles to both plots using ax.set_title() and plt.title() respectively.

Matplotlib

# Plot Product A sales using ax.bar()
# Plot Product B sales using plt.bar()
# Add titles to both plots
# Your code here

Hint

Use ax.bar() for Product A and plt.bar() for Product B. Add titles with ax.set_title() and plt.title().

4

Print the figure and axes objects and show the plots

Print the variables fig and ax to see their types. Then call plt.show() to display the plots.

Matplotlib

# Print fig and ax
# Show the plots
# Your code here

Hint

Use print(fig) and print(ax) to see the figure and axes objects. Use plt.show() to display the plots.

Practice

1. Which method would you use to set the main title for the entire figure in matplotlib?

easy

A. plt.title()

B. ax.set_title()

C. ax.set_xlabel()

D. fig.suptitle()

Solution

1. Step 1: Understand figure-level vs axes-level

   Figure-level methods control the whole figure, axes-level methods control individual plots.

2. Step 2: Identify method for main figure title

   The method fig.suptitle() sets the main title for the entire figure, while ax.set_title() sets title for one subplot.

3. Final Answer:

   fig.suptitle() -> Option D

4. Quick Check:

   Figure main title = fig.suptitle() [OK]

Hint: Main figure title uses fig.suptitle(), not ax.set_title() [OK]

Common Mistakes:

• Using ax.set_title() for main figure title
• Using plt.title() which affects current axes
• Confusing xlabel with title

2. Which of the following is the correct syntax to set the x-axis label for a single plot (axes) in matplotlib?

easy

A. plt.set_xlabel('X Label')

B. fig.set_xlabel('X Label')

C. ax.set_xlabel('X Label')

D. fig.xlabel('X Label')

Solution

1. Step 1: Identify the object controlling axis labels

   Axis labels belong to individual plots, so axes-level methods are used.

2. Step 2: Choose correct method and object

   The method set_xlabel() is called on an axes object, so ax.set_xlabel('X Label') is correct.

3. Final Answer:

   ax.set_xlabel('X Label') -> Option C

4. Quick Check:

   Axis label = ax.set_xlabel() [OK]

Hint: Use ax.set_xlabel() for x-axis label, not fig or plt [OK]

Common Mistakes:

• Calling set_xlabel() on figure object
• Using plt.set_xlabel() which does not exist
• Confusing axis label with figure title

3. What will the following code display?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.suptitle('Main Title')
ax.set_title('Plot Title')
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis')
plt.show()

medium

A. A figure with main title 'Main Title' and one plot titled 'Plot Title' with labeled axes

B. Only a plot titled 'Main Title' with axes labeled 'X Axis' and 'Y Axis'

C. A figure with no titles but axes labeled 'X Axis' and 'Y Axis'

D. Error because fig.suptitle() cannot be used with ax.set_title()

Solution

1. Step 1: Understand figure and axes titles

   fig.suptitle() sets the main figure title; ax.set_title() sets the subplot title.

2. Step 2: Analyze axis labels and display

   ax.set_xlabel() and ax.set_ylabel() label the x and y axes of the plot. The code will show all these correctly.

3. Final Answer:

   A figure with main title 'Main Title' and one plot titled 'Plot Title' with labeled axes -> Option A

4. Quick Check:

   fig.suptitle + ax.set_title + ax labels = A figure with main title 'Main Title' and one plot titled 'Plot Title' with labeled axes [OK]

Hint: fig.suptitle is main title; ax.set_title is subplot title [OK]

Common Mistakes:

• Thinking fig.suptitle replaces ax.set_title
• Expecting error when using both titles
• Ignoring axis labels

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.set_title('Main Title')
ax.set_xlabel('X Label')
plt.show()

medium

A. fig.set_title() is not a valid method for figure objects

B. ax.set_xlabel() should be called on fig, not ax

C. plt.show() must be called before setting titles

D. fig, ax = plt.subplots() returns only one object

Solution

1. Step 1: Check figure method usage

   The figure object does not have a method called set_title(). The correct method for figure title is suptitle().

2. Step 2: Verify other method calls

   ax.set_xlabel() is correct for labeling x-axis. plt.show() is correctly placed at the end.

3. Final Answer:

   fig.set_title() is not a valid method for figure objects -> Option A

4. Quick Check:

   Figure title method = fig.suptitle() not fig.set_title() [OK]

Hint: Figure title uses suptitle(), not set_title() [OK]

Common Mistakes:

• Using fig.set_title() instead of fig.suptitle()
• Confusing axes methods with figure methods
• Calling plt.show() too early

5. You want to create a figure with 2 subplots side by side. You want a main title for the figure and individual titles for each subplot. Which code correctly achieves this?

hard

A. fig, axs = plt.subplots(1, 2)
fig.title('Main Title')
axs[0].title('Left Plot')
axs[1].title('Right Plot')
plt.show()

B. fig, axs = plt.subplots(1, 2)
fig.suptitle('Main Title')
axs[0].set_title('Left Plot')
axs[1].set_title('Right Plot')
plt.show()

C. fig, axs = plt.subplots(2, 1)
fig.set_title('Main Title')
axs[0].set_xlabel('Left Plot')
axs[1].set_xlabel('Right Plot')
plt.show()

D. fig, axs = plt.subplots(1, 2)
axs.set_title('Main Title')
fig.set_title('Left Plot')
fig.set_title('Right Plot')
plt.show()

Solution

1. Step 1: Create subplots and set main figure title

   plt.subplots(1, 2) creates two side-by-side plots. fig.suptitle() sets the main figure title.

2. Step 2: Set individual subplot titles correctly

   Each subplot is accessed by axs[0] and axs[1]. Use set_title() to set their titles.

3. Step 3: Verify other options are incorrect

   The other options misuse methods like fig.set_title() or fig.title() which do not exist or use wrong subplot layout.

4. Final Answer:

   fig, axs = plt.subplots(1, 2) fig.suptitle('Main Title') axs[0].set_title('Left Plot') axs[1].set_title('Right Plot') plt.show() -> Option B

5. Quick Check:

   Figure main title = fig.suptitle(), subplot titles = axs[i].set_title() [OK]

Hint: Use fig.suptitle() for main title, axs[i].set_title() for subplots [OK]

Common Mistakes:

• Using fig.set_title() instead of fig.suptitle()
• Calling title() instead of set_title()
• Wrong subplot layout dimensions