Memory management with large figures in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the memory usage behavior of this matplotlib code?

Consider the following Python code that creates and closes a large figure repeatedly. What will be the output regarding memory usage?

Matplotlib

import matplotlib.pyplot as plt
import tracemalloc

tracemalloc.start()

for i in range(3):
    fig, ax = plt.subplots(figsize=(20, 20))
    ax.plot(range(100000))
    plt.close(fig)

current, peak = tracemalloc.get_traced_memory()
print(f"Current memory usage: {current / 10**6:.2f} MB")
print(f"Peak memory usage: {peak / 10**6:.2f} MB")
tracemalloc.stop()

A Current memory usage: less than 10 MB; Peak memory usage: around 50 MB

B Current memory usage: more than 100 MB; Peak memory usage: more than 150 MB

C Current memory usage: less than 10 MB; Peak memory usage: less than 10 MB

D Current memory usage: more than 100 MB; Peak memory usage: less than 10 MB

💡 Hint

Think about how closing figures affects memory in matplotlib.

Closing figures with plt.close() releases memory, so current usage stays low. Peak memory usage reflects the temporary memory used during plotting.

🧠 Conceptual

intermediate

Why is it important to close figures in matplotlib when working with large data?

When plotting large datasets repeatedly, why should you close figures explicitly in matplotlib?

A To automatically save the figures to disk

B To free up memory and avoid memory leaks that slow down the program

C To make the plots display faster on the screen

D To reduce the size of the saved image files

💡 Hint

Think about what happens if figures stay open in memory.

Open figures consume memory. Not closing them causes memory to fill up, slowing down or crashing the program.

🔧 Debug

advanced

Identify the cause of increasing memory usage in this plotting loop

Look at this code snippet that plots data in a loop. Memory usage increases after each iteration. What is the cause?

Matplotlib

import matplotlib.pyplot as plt

for i in range(5):
    fig, ax = plt.subplots()
    ax.plot(range(10000))
    # Missing plt.close(fig)
    print(f"Iteration {i} done")

A Figures are not closed, so memory accumulates with each iteration

B The plot data is too large to fit in memory

C The loop variable i is not used inside the loop

D The print statement causes memory leaks

💡 Hint

Think about what happens to figures that are not closed.

Not closing figures keeps them in memory, causing memory usage to grow with each loop iteration.

❓ data_output

advanced

What is the number of open figures after this code runs?

After running the following code, how many figures remain open in matplotlib?

Matplotlib

import matplotlib.pyplot as plt

for _ in range(3):
    plt.figure()
plt.close(1)
plt.close(3)

A 2

B 0

C 3

D 1

💡 Hint

Figure numbers start at 1 and increase with each new figure.

Three figures are created with numbers 1, 2, and 3. Figures 1 and 3 are closed, so only figure 2 remains open.

🚀 Application

expert

Optimize memory usage when generating many large plots

You need to generate 100 large plots in a script. Which approach best manages memory to avoid crashes?

A Create all 100 plots first, then save and close them all at the end

B Use plt.show() after each plot to clear memory automatically

C Create each plot with plt.subplots(), save it, then call plt.close() on the figure before next iteration

D Create each plot and save it without closing figures, relying on Python garbage collection

💡 Hint

Think about when memory is freed during plotting.

Closing each figure immediately after saving frees memory, preventing accumulation and crashes.