Plt.subplots with rows and columns in Matplotlib - Time & Space Complexity

Time Complexity: Plt.subplots with rows and columns

O(rows x columns)

Understanding Time Complexity

We want to understand how the time to create multiple plots grows when using plt.subplots with many rows and columns.

How does adding more rows and columns affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=3, ncols=4)
for ax in axes.flat:
    ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

This code creates a grid of 3 rows and 4 columns of plots, then draws a simple line plot in each subplot.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping over each subplot to draw a line plot.
• How many times: Once for each subplot, so total subplots = rows x columns.

How Execution Grows With Input

As the number of rows and columns increases, the total number of subplots grows by multiplying them.

Input Size (rows x columns)	Approx. Operations
3 x 4 = 12	12 plot operations
10 x 10 = 100	100 plot operations
30 x 30 = 900	900 plot operations

Pattern observation: The work grows directly with the total number of subplots, which is rows times columns.

Final Time Complexity

Time Complexity: O(rows x columns)

This means the time to create and draw all plots grows in direct proportion to how many subplots you have.

Common Mistake

[X] Wrong: "Adding more rows or columns only slightly increases the time because plots are fast."

[OK] Correct: Each subplot requires separate drawing work, so doubling rows or columns roughly doubles the total work.

Interview Connect

Understanding how plotting time grows helps you design efficient visualizations and manage resources well in real projects.

Self-Check

"What if we replaced the loop with a vectorized plotting method that draws all lines at once? How would the time complexity change?"

Practice

1. What does plt.subplots(2, 3) create in matplotlib?

easy

A. A figure with 2 rows and 3 columns of subplots

B. A figure with 3 rows and 2 columns of subplots

C. A single plot with size 2x3 inches

D. A figure with 6 rows and 1 column of subplots

Solution

1. Step 1: Understand plt.subplots parameters

   The first parameter is the number of rows, the second is columns.

2. Step 2: Apply to given code

   plt.subplots(2, 3) means 2 rows and 3 columns of subplots.

3. Final Answer:

   A figure with 2 rows and 3 columns of subplots -> Option A

4. Quick Check:

   rows=2, columns=3 = B [OK]

Hint: First number = rows, second = columns in plt.subplots [OK]

Common Mistakes:

• Confusing rows and columns order
• Thinking parameters set figure size
• Assuming single plot created

2. Which of the following is the correct syntax to create a 3x1 subplot grid using plt.subplots?

easy

A. fig, axes = plt.subplots(1, 3)

B. fig, axes = plt.subplots(3)

C. fig, axes = plt.subplots(3, 1)

D. fig, axes = plt.subplots(rows=3, cols=1)

Solution

1. Step 1: Recall plt.subplots parameter order

   The first argument is rows, second is columns.

2. Step 2: Match to 3 rows and 1 column

   plt.subplots(3, 1) creates 3 rows and 1 column.

3. Final Answer:

   fig, axes = plt.subplots(3, 1) -> Option C

4. Quick Check:

   3 rows, 1 column = C [OK]

Hint: Remember: plt.subplots(rows, columns) order matters [OK]

Common Mistakes:

• Swapping rows and columns
• Using single number for both dimensions
• Using keyword arguments incorrectly

3. What is the shape of axes returned by fig, axes = plt.subplots(2, 2)?

medium

A. A 1D array with 4 axes objects

B. A 2D array with shape (2, 2) of axes objects

C. A list of 2 axes objects

D. A single axes object

Solution

1. Step 1: Understand axes shape for multiple rows and columns

   When rows and columns are more than 1, axes is a 2D array matching shape.

2. Step 2: Apply to plt.subplots(2, 2)

   axes will be a 2x2 array of axes objects.

3. Final Answer:

   A 2D array with shape (2, 2) of axes objects -> Option B

4. Quick Check:

   Multiple rows and columns = 2D axes array [OK]

Hint: Multiple rows & columns -> axes is 2D array [OK]

Common Mistakes:

• Assuming axes is always 1D
• Confusing number of axes with shape
• Expecting a single axes object

4. Given the code:

fig, axes = plt.subplots(2, 2)
axes[2, 0].plot([1, 2, 3])

What error will occur?

medium

A. No error, plot will display

B. TypeError: axes object is not subscriptable

C. AttributeError: 'AxesSubplot' object has no attribute 'plot'

D. IndexError: index 2 is out of bounds for axis 0 with size 2

Solution

1. Step 1: Check axes shape from plt.subplots(2, 2)

   axes is a 2x2 array with indices 0 and 1 for rows and columns.

2. Step 2: Analyze axes[2, 0] access

   Index 2 for rows is out of bounds since max index is 1.

3. Final Answer:

   IndexError: index 2 is out of bounds for axis 0 with size 2 -> Option D

4. Quick Check:

   axes shape (2,2), index 2 invalid [OK]

Hint: Check axes array shape before indexing [OK]

Common Mistakes:

• Using invalid indices beyond shape
• Confusing axes as list instead of array
• Expecting no error on wrong index

5. You want to create a 2x3 grid of subplots and plot a line on each subplot with data from a list of 6 arrays. Which code correctly accesses each subplot to plot inside a nested loop?

hard

A. for i in range(2):
for j in range(3):
axes[i][j].plot(data[i*3 + j])

B. for i in range(2):
for j in range(3):
axes[i, j].plot(data[i + j])

C. for i in range(3):
for j in range(2):
axes[i][j].plot(data[i*2 + j])

D. for i in range(2):
for j in range(3):
axes[j][i].plot(data[i*3 + j])

Solution

1. Step 1: Understand axes shape and indexing

   plt.subplots(2, 3) creates axes as a 2x3 array, indexed as axes[row][col].

2. Step 2: Correctly map data list to subplot indices

   Data index = row * number_of_columns + column = i*3 + j matches subplot position.

3. Final Answer:

   for i in range(2): for j in range(3): axes[i][j].plot(data[i*3 + j]) -> Option A

4. Quick Check:

   axes[row][col], data index = row*cols + col [OK]

Hint: Use axes[row][col] and data index = row*cols + col [OK]

Common Mistakes:

• Swapping row and column loops
• Incorrect data indexing
• Using axes[j][i] instead of axes[i][j]