Individual subplot customization in Matplotlib - Time & Space Complexity

Time Complexity: Individual subplot customization

O(n)

Understanding Time Complexity

When customizing individual subplots in matplotlib, we want to know how the time to draw changes as we add more subplots.

How does the work grow when we customize each subplot separately?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

fig, axs = plt.subplots(3, 3)

for i, ax in enumerate(axs.flat):
    ax.plot([0, 1, 2], [i, i*2, i*3])
    ax.set_title(f"Plot {i+1}")

plt.show()

This code creates a 3x3 grid of subplots and customizes each one by plotting data and setting a title.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping over each subplot to customize it.
• How many times: Once for each subplot, here 9 times (3 rows x 3 columns).

How Execution Grows With Input

Each subplot requires a fixed amount of work to customize. As the number of subplots grows, the total work grows proportionally.

Input Size (n subplots)	Approx. Operations
10	10 customization steps
100	100 customization steps
1000	1000 customization steps

Pattern observation: Doubling the number of subplots roughly doubles the work needed.

Final Time Complexity

Time Complexity: O(n)

This means the time to customize grows directly in proportion to the number of subplots.

Common Mistake

[X] Wrong: "Customizing multiple subplots is done all at once, so time stays the same no matter how many subplots there are."

[OK] Correct: Each subplot needs its own commands, so the total time adds up with more subplots.

Interview Connect

Understanding how work grows when customizing plots helps you write efficient code and explain your approach clearly in real projects.

Self-Check

"What if we customized only a fixed number of subplots regardless of total? How would the time complexity change?"

Practice

1. What is the correct way to change the title of the second subplot when using fig, axs = plt.subplots(2, 2)?

easy

A. Use plt.title('New Title')

B. Use axs[1].set_title('New Title')

C. Use fig.set_title('New Title')

D. Use axs[0, 1].set_title('New Title')

Solution

1. Step 1: Understand subplot indexing

   With plt.subplots(2, 2), axs is a 2x2 array. The second subplot in the first row is at position [0, 1].

2. Step 2: Set title for specific subplot

   Use axs[0, 1].set_title('New Title') to change only that subplot's title.

3. Final Answer:

   Use axs[0, 1].set_title('New Title') -> Option D

4. Quick Check:

   Indexing subplots by row and column = A [OK]

Hint: Use axs[row, col] to target specific subplot [OK]

Common Mistakes:

• Using axs[1] instead of axs[0, 1]
• Setting title on fig instead of axs
• Using plt.title affects whole figure, not subplot

2. Which of the following is the correct syntax to change the x-axis label of the first subplot in a 1D array of subplots fig, axs = plt.subplots(3)?

easy

A. axs[1].set_xlabel('X Label')

B. axs[0].set_xlabel('X Label')

C. axs.set_xlabel('X Label')

D. fig.set_xlabel('X Label')

Solution

1. Step 1: Identify subplot indexing in 1D array

   With plt.subplots(3), axs is a 1D array with indices 0, 1, 2. The first subplot is at index 0.

2. Step 2: Use set_xlabel on the correct subplot

   Use axs[0].set_xlabel('X Label') to set the x-axis label of the first subplot.

3. Final Answer:

   axs[0].set_xlabel('X Label') -> Option B

4. Quick Check:

   Index 0 for first subplot in 1D axs = D [OK]

Hint: Remember 1D axs uses zero-based indexing [OK]

Common Mistakes:

• Using axs[1] for first subplot
• Calling set_xlabel on fig or axs directly
• Confusing 1D and 2D axs indexing

3. What will be the color of the line in the third subplot after running this code?

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

medium

A. Blue

B. Red

C. Green

D. Black

Solution

1. Step 1: Identify subplot position

   With plt.subplots(2, 2), subplots are indexed as [0,0], [0,1], [1,0], [1,1]. The third subplot is at [1,0].

2. Step 2: Check plot color argument

   The code sets color='green' for the line in subplot [1,0].

3. Final Answer:

   Green -> Option C

4. Quick Check:

   Color set explicitly to green = B [OK]

Hint: Check subplot index and color argument carefully [OK]

Common Mistakes:

• Assuming default blue color
• Confusing subplot index order
• Ignoring color parameter in plot

4. Identify the error in this code that tries to set the y-axis label of the last subplot:

fig, axs = plt.subplots(2, 2)
axs[2, 1].set_ylabel('Y Label')

medium

A. IndexError because axs has shape (2,2) and index (2,1) is out of range

B. AttributeError because set_ylabel is not a valid method

C. SyntaxError due to wrong indexing syntax

D. No error, code runs fine

Solution

1. Step 1: Check axs shape and indexing

   With plt.subplots(2, 2), axs shape is (2, 2), valid indices are 0 or 1 for each dimension.

2. Step 2: Identify invalid index

   Indexing axs[2, 1] tries to access third row (index 2), which does not exist, causing IndexError.

3. Final Answer:

   IndexError because axs has shape (2,2) and index (2,1) is out of range -> Option A

4. Quick Check:

   Index out of range error = C [OK]

Hint: Check subplot array shape before indexing [OK]

Common Mistakes:

• Assuming axs has more rows than created
• Confusing AttributeError with IndexError
• Using wrong indexing syntax

5. You want to create a 2x2 grid of subplots and set a different background color for each subplot: red, blue, green, and yellow respectively. Which code snippet correctly applies these colors?

hard

A. colors = ['red', 'blue', 'green', 'yellow']
fig, axs = plt.subplots(2, 2)
for ax, color in zip(axs.flat, colors):
ax.set_facecolor(color)

B. colors = ['red', 'blue', 'green', 'yellow']
fig, axs = plt.subplots(2, 2)
for i in range(4):
axs[i].set_facecolor(colors[i])

C. colors = ['red', 'blue', 'green', 'yellow']
fig, axs = plt.subplots(2, 2)
for i, j in range(2):
axs[i, j].set_facecolor(colors[i+j])

D. colors = ['red', 'blue', 'green', 'yellow']
fig, axs = plt.subplots(2, 2)
for ax in axs:
ax.set_facecolor(colors)

Solution

1. Step 1: Understand axs structure and iteration

   With plt.subplots(2, 2), axs is a 2D array. Using axs.flat flattens it to 1D for easy iteration.

2. Step 2: Apply colors correctly

   Using zip(axs.flat, colors) pairs each subplot with a color. Calling ax.set_facecolor(color) sets background color individually.

3. Final Answer:

   Use axs.flat with zip and set_facecolor for each subplot -> Option A

4. Quick Check:

   Flatten axs and zip with colors = A [OK]

Hint: Use axs.flat to loop all subplots in order [OK]

Common Mistakes:

• Trying to index axs as 1D without flattening
• Using axs as 2D but wrong color indexing
• Passing full color list to set_facecolor