Memory management with large figures in Matplotlib

Introduction

Large figures can use a lot of computer memory. Managing memory well helps your computer run smoothly and avoids crashes.

When creating big or many plots in a loop.

When working with high-resolution images or detailed charts.

When running data analysis scripts that generate multiple figures.

When your computer slows down or runs out of memory during plotting.

When saving many figures to files in one program run.

Syntax

Matplotlib

import matplotlib.pyplot as plt

# Create a figure
fig, ax = plt.subplots()

# Plot your data
ax.plot(data)

# Show or save the figure
plt.show()  # or plt.savefig('file.png')

# Clear the figure to free memory
plt.close(fig)

Use plt.close() to free memory after you finish with a figure.

Closing figures is important when making many plots in a loop.

Examples

This example creates and shows 3 plots one by one, closing each to save memory.

Matplotlib

import matplotlib.pyplot as plt

for i in range(3):
    fig, ax = plt.subplots()
    ax.plot([1, 2, 3], [i, i*2, i*3])
    plt.show()
    plt.close(fig)

Here, the figure is saved to a file and then closed to free memory.

Matplotlib

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
plt.savefig('myplot.png')
plt.close(fig)

Sample Program

This program makes a large plot with 100,000 points, shows it, then closes the figure to free memory.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create large data
x = np.linspace(0, 100, 100000)
y = np.sin(x)

# Create a large figure
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)
ax.set_title('Large Figure Example')

# Show the figure
plt.show()

# Close the figure to free memory
plt.close(fig)

# Print confirmation
print('Figure created and closed to manage memory.')

Important Notes

Always close figures you no longer need to avoid using too much memory.

Using plt.close() is especially important in loops or long scripts.

If you forget to close figures, your program might slow down or crash.

Summary

Large figures use a lot of memory and can slow your computer.

Use plt.close() to free memory after showing or saving a figure.

Closing figures is very important when making many plots in one program.

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