Memory management with large figures in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a common cause of high memory usage when working with large figures in matplotlib?

Large figures with many data points or high-resolution images consume more memory because matplotlib stores all the drawing elements in memory until the figure is closed or cleared.

beginner

How does calling plt.close() help in memory management with large figures?

Calling plt.close() releases the memory used by the figure by closing it, which removes it from matplotlib's internal list of open figures.

intermediate

Why is it better to use fig.clf() instead of creating many new figures in a loop?

fig.clf() clears the current figure without closing it, allowing reuse of the same figure object and reducing memory overhead from creating multiple figures.

intermediate

What is the benefit of using agg backend in matplotlib for large figures?

The agg backend renders figures as raster images without displaying them on screen, which can reduce memory usage and speed up saving large figures.

beginner

How can reducing figure DPI help with memory management?

Lowering the DPI (dots per inch) reduces the resolution of the figure, which decreases the amount of memory needed to store the image data.

What does plt.close() do in matplotlib?

A Closes the current figure and frees its memory

B Clears the current figure but keeps it open

C Saves the figure to a file

D Increases the figure size

plt.close() closes the figure window and releases the memory used by that figure.

Which method clears the content of a figure but keeps the figure object open for reuse?

A fig.savefig()

B plt.close()

C plt.show()

D fig.clf()

fig.clf() clears the figure content but does not close the figure, allowing reuse.

Why might using the agg backend be helpful for large figures?

A It displays figures faster on screen

B It reduces memory by rendering figures off-screen

C It increases figure resolution

D It automatically closes figures

The agg backend renders figures as images off-screen, reducing memory and improving performance when saving large figures.

What effect does lowering the DPI of a figure have?

A Decreases memory usage

B Increases memory usage

C Changes figure color

D Closes the figure

Lower DPI means lower resolution, which reduces the memory needed to store the figure image.

What happens if you create many figures without closing them?

A Memory usage stays the same

B Memory usage decreases

C Memory usage increases and may cause slowdowns

D Figures automatically clear

Not closing figures keeps them in memory, increasing usage and possibly slowing down your program.

Explain how to manage memory effectively when creating multiple large figures in matplotlib.

Think about closing, clearing, backend choice, and resolution.

Describe why memory management is important when working with large figures and what problems can arise without it.

Consider the impact of memory on program performance.

Practice

1. Why is it important to use plt.close() after creating large figures in matplotlib?

easy

A. To change the color of the figure

B. To free up memory and prevent slowing down the computer

C. To save the figure automatically

D. To increase the size of the figure

Solution

1. Step 1: Understand memory use by large figures

   Large figures use a lot of computer memory which can slow down the system if not managed.

2. Step 2: Role of plt.close()

   Using plt.close() frees the memory used by the figure after it is shown or saved.

3. Final Answer:

   To free up memory and prevent slowing down the computer -> Option B

4. Quick Check:

   Memory management = Free memory [OK]

Hint: Always close large figures to save memory after use [OK]

Common Mistakes:

• Thinking plt.close() saves the figure
• Believing it changes figure appearance
• Ignoring memory impact of many open figures

2. Which of the following is the correct syntax to close a figure in matplotlib?

easy

A. plt.closeFigure()

B. plt.close_figure()

C. plt.closeFig()

D. plt.close()

Solution

1. Step 1: Recall matplotlib function names

   The official function to close a figure is plt.close().

2. Step 2: Check other options

   Other options like plt.close_figure() or plt.closeFig() do not exist in matplotlib.

3. Final Answer:

   plt.close() -> Option D

4. Quick Check:

   Correct function name = plt.close() [OK]

Hint: Use exact function names from matplotlib docs [OK]

Common Mistakes:

• Adding extra words to function name
• Using camelCase instead of snake_case
• Confusing with save or show functions

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

import matplotlib.pyplot as plt
for i in range(3):
    fig = plt.figure()
    plt.plot([1, 2, 3], [i, i+1, i+2])
plt.show()

medium

A. Three plots will be shown and memory will be freed automatically

B. Only one plot will be shown, others are overwritten

C. Three plots will be shown but memory is not freed, causing high usage

D. Code will raise an error because plt.close() is missing

Solution

1. Step 1: Analyze the loop creating figures

   The loop creates 3 separate figures and plots on each without closing them.

2. Step 2: Understand memory impact

   Since plt.close() is not called, all figures stay in memory, increasing usage.

3. Final Answer:

   Three plots will be shown but memory is not freed, causing high usage -> Option C

4. Quick Check:

   Figures open without close = high memory [OK]

Hint: Without plt.close(), memory stays used after plotting [OK]

Common Mistakes:

• Assuming memory frees automatically after plt.show()
• Thinking only one plot appears
• Expecting an error without plt.close()

4. Identify the error in this code that creates multiple large figures:

import matplotlib.pyplot as plt
for i in range(5):
    fig = plt.figure(figsize=(10,8))
    plt.plot([1,2,3], [i,i+1,i+2])
    plt.show()

medium

A. Missing plt.close() to free memory after each figure

B. plt.show() should be outside the loop

C. Figure size is too small for large data

D. Plot data lists have different lengths

Solution

1. Step 1: Check memory management in loop

   The code creates large figures repeatedly but never closes them, causing memory buildup.

2. Step 2: Identify missing memory freeing step

   Adding plt.close() after plt.show() frees memory for each figure.

3. Final Answer:

   Missing plt.close() to free memory after each figure -> Option A

4. Quick Check:

   Close figures in loops to avoid memory leaks [OK]

Hint: Always close figures inside loops after showing [OK]

Common Mistakes:

• Moving plt.show() outside loop without closing figures
• Changing figure size instead of closing
• Ignoring memory issues with many figures

5. You need to generate 100 large plots in a script without running out of memory. Which approach is best to manage memory efficiently?

hard

A. Create each figure, plot data, save it, then call plt.close() before next

B. Create all 100 figures first, then plot and save them all together

C. Plot all data on one figure without closing it

D. Use plt.show() after all figures are created without closing

Solution

1. Step 1: Understand memory use when creating many figures

   Creating many large figures without closing them uses too much memory and slows the system.

2. Step 2: Best practice for memory management

   Creating, saving, then closing each figure before the next frees memory and avoids overload.

3. Final Answer:

   Create each figure, plot data, save it, then call plt.close() before next -> Option A

4. Quick Check:

   Close each figure after saving to save memory [OK]

Hint: Save and close each figure before next to avoid memory issues [OK]

Common Mistakes:

• Creating all figures before saving causes memory overload
• Not closing figures after plotting
• Plotting all data on one figure when separate plots needed