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How to Save Matplotlib Plots to Files: Complete Guide

Learn how to save matplotlib plots to files using plt.savefig() instead of displaying them. Export to PNG, PDF, SVG with customizable quality and formatting options.

#data-visualization#export-plots#matplotlib#plt-savefig#python-plotting
Question

Save plot to image file instead of displaying it

This displays the figure in a GUI:

python
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [1, 4, 9])
plt.show()

But how do I instead save the figure to a file (e.g. foo.png)?

NeuroAgent

To save a matplotlib plot to an image file instead of displaying it, use the plt.savefig() or fig.savefig() method after creating your plot. This approach allows you to export your visualization to various file formats like PNG, JPG, PDF, or SVG with customizable quality and formatting options.

Contents

Basic Savefig Method

The fundamental way to save a plot to a file is by using plt.savefig() after creating your plot. Here’s the basic approach:

python
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [1, 4, 9])
plt.savefig('foo.png')  # Saves the plot to a PNG file

Alternatively, you can work with figure objects directly:

python
import matplotlib.pyplot as plt
fig, ax = plt.subplots()  # Create figure and axes
ax.plot([1, 2, 3], [1, 4, 9])
fig.savefig('foo.png')  # Save the specific figure

As noted in the Stack Overflow discussion, this is the standard approach used by most matplotlib users.

File Format Options

Matplotlib supports multiple output formats that are automatically detected based on the file extension. According to the official documentation, the available formats depend on the backend being used:

python
# Common image formats
plt.savefig('plot.png')      # PNG format
plt.savefig('plot.jpg')      # JPEG format  
plt.savefig('plot.svg')      # SVG vector format
plt.savefig('plot.pdf')      # PDF vector format
plt.savefig('plot.eps')      # EPS vector format
plt.savefig('plot.tiff')     # TIFF format
plt.savefig('plot.bmp')      # BMP format

The GeeksforGeeks tutorial demonstrates saving plots in various formats, showing how matplotlib automatically determines the format from the file extension.

Key Parameters Explained

DPI (Dots Per Inch)

Controls the resolution and quality of the saved image:

python
plt.savefig('high_quality.png', dpi=300)  # High resolution
plt.savefig('low_quality.png', dpi=72)    # Low resolution

According to the Medium article, DPI significantly impacts the quality - higher DPI means larger file size but better quality.

Bounding Box and Padding

Prevents axis labels and titles from being cut off:

python
plt.savefig('plot.png', bbox_inches='tight', pad_inches=0.1)

The official documentation shows that bbox_inches='tight' ensures all plot elements are included, while pad_inches controls the padding around the content.

Transparency and Background

Control the background appearance:

python
plt.savefig('transparent.png', transparent=True)  # Transparent background
plt.savefig('colored_bg.png', facecolor='yellow')  # Custom background color

As explained in the Python Pool guide, the transparent parameter makes the background transparent, which is useful for overlays.

Format-Specific Parameters

Some formats support additional parameters:

python
# For JPEG files, you can control quality
plt.savefig('photo.jpg', quality=95)  # Quality from 0-100

# For vector formats, you can control metadata
plt.savefig('report.pdf', metadata={'Title': 'My Analysis'})

Best Practices and Common Issues

Closing Figures to Prevent Memory Issues

When working in loops or scripts, it’s important to close figures to prevent memory leaks:

python
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
fig.savefig('plot.png')
plt.close(fig)  # Close the figure to free memory

As mentioned in the Stack Overflow solution, this prevents having multiple open figures during large loops.

Handling Interactive Mode

In some environments like Spyder with interactive mode enabled, figures might always display. The workaround is to close them explicitly:

python
import matplotlib.pyplot as plt

# Set up for interactive mode if needed
plt.ion()  # Interactive mode on

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
fig.savefig('plot.png')
plt.close(fig)  # Force close to prevent display

Avoiding Empty Files

A common issue occurs when trying to save a figure after calling plt.show():

python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 4, 9])
plt.savefig('before_show.png')  # This works
plt.show()                      # This clears the figure
plt.savefig('after_show.png')   # This creates an empty file!

As discussed in this Stack Overflow thread, you should save before showing if you want both.

Complete Examples

Basic Line Plot

python
import matplotlib.pyplot as plt
import numpy as np

# Create sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create and save plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2, label='sin(x)')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Sine Wave')
plt.legend()
plt.grid(True, alpha=0.3)

# Save with high quality
plt.savefig('sine_wave.png', dpi=300, bbox_inches='tight', facecolor='white')
plt.close()

Multiple Subplots

python
import matplotlib.pyplot as plt
import numpy as np

# Create figure with subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))

# Plot different functions
x = np.linspace(0, 10, 100)

ax1.plot(x, np.sin(x), 'r-')
ax1.set_title('Sine')
ax1.grid(True)

ax2.plot(x, np.cos(x), 'g-')
ax2.set_title('Cosine')
ax2.grid(True)

ax3.plot(x, np.exp(-x/5), 'b-')
ax3.set_title('Exponential Decay')
ax3.grid(True)

ax4.plot(x, np.log(x + 1), 'm-')
ax4.set_title('Logarithmic')
ax4.grid(True)

plt.tight_layout()
plt.savefig('multi_plots.png', dpi=150, bbox_inches='tight')
plt.close()

Bar Chart with Custom Styling

python
import matplotlib.pyplot as plt

# Sample data
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]

# Create bar chart
plt.figure(figsize=(8, 6))
bars = plt.bar(categories, values, color='skyblue', alpha=0.7, edgecolor='navy', linewidth=2)

# Add value labels on bars
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height}', ha='center', va='bottom', fontsize=12, fontweight='bold')

plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Sample Bar Chart')
plt.grid(axis='y', alpha=0.3)

# Save as both PNG and PDF
plt.savefig('bar_chart.png', dpi=300, bbox_inches='tight', facecolor='white')
plt.savefig('bar_chart.pdf', bbox_inches='tight', facecolor='white')
plt.close()

Advanced Techniques

Saving with Different Backgrounds

python
import matplotlib.pyplot as plt
import numpy as np

# Create plot
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(x, y, 'r-', linewidth=3)
ax.set_title('Sine Wave with Different Backgrounds')

# Save with white background
fig.savefig('white_bg.png', facecolor='white', bbox_inches='tight')

# Save with transparent background
fig.savefig('transparent_bg.png', transparent=True, bbox_inches='tight')

# Save with custom colored background
fig.savefig('colored_bg.png', facecolor='lightblue', bbox_inches='tight')
plt.close()

Batch Processing Multiple Plots

python
import matplotlib.pyplot as plt
import numpy as np
import os

# Create output directory if it doesn't exist
output_dir = 'plots'
os.makedirs(output_dir, exist_ok=True)

# Generate multiple plots for different functions
functions = [
    ('sin(x)', np.sin, 'red'),
    ('cos(x)', np.cos, 'blue'),
    ('tan(x)', np.tan, 'green'),
    ('exp(-x)', lambda x: np.exp(-x), 'purple')
]

x = np.linspace(0, 10, 100)

for name, func, color in functions:
    plt.figure(figsize=(8, 6))
    plt.plot(x, func(x), color=color, linewidth=2, label=name)
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.title(f'Plot of {name}')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Save with high quality
    filename = os.path.join(output_dir, f'{name.replace("(", "").replace(")", "")}.png')
    plt.savefig(filename, dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()

print(f"Saved {len(functions)} plots to {output_dir}/")

Saving with Custom Metadata

python
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'bo-')
plt.title('Custom Metadata Example')
plt.xlabel('X values')
plt.ylabel('Y values')

# Save with metadata
metadata = {
    'Title': 'My Plot',
    'Author': 'Data Scientist',
    'Description': 'Example plot with custom metadata',
    'Copyright': '2024 My Company'
}

plt.savefig('with_metadata.png', dpi=300, metadata=metadata, bbox_inches='tight')
plt.close()

These examples demonstrate various ways to save matplotlib plots to files while controlling quality, format, and appearance. The key is to use plt.savefig() with appropriate parameters for your specific needs, and always remember to close figures when working in scripts to prevent memory issues.

Sources

  1. Stack Overflow - Save plot to image file instead of displaying it
  2. Atlassian - How to Save a Plot to a File Using Matplotlib
  3. Sentry - Save a Matplotlib plot to a file in Python
  4. GeeksforGeeks - How to Save a Plot to a File Using Matplotlib
  5. Matplotlib Documentation - matplotlib.pyplot.savefig
  6. Python Pool - Matplotlib Savefig() For Different Parameters
  7. Medium - Using matplotlib savefig Effectively
  8. Problem Solving 101 with Python - Saving plots

Conclusion

Saving matplotlib plots to files instead of displaying them is straightforward using the plt.savefig() method. The key takeaways are:

  1. Use plt.savefig('filename.png') or fig.savefig('filename.png') to save your plot
  2. Control quality with the dpi parameter (300 for high quality, 72 for web)
  3. Use bbox_inches='tight' to prevent labels from being cut off
  4. Set transparent=True for transparent backgrounds or facecolor for custom backgrounds
  5. Support multiple formats (PNG, JPG, PDF, SVG, etc.) based on file extension
  6. Always close figures with plt.close(fig) in scripts to prevent memory leaks
  7. Save before calling plt.show() to avoid empty files

By mastering these techniques, you can efficiently generate and save professional-quality visualizations for reports, presentations, and web applications.

How to control the quality and resolution when saving matplotlib plots?What are the differences between saving matplotlib plots as PNG vs PDF vs SVG?How to save matplotlib plots with transparent background?How to batch save multiple matplotlib plots to files?Why does my saved matplotlib plot appear empty after calling plt.show()?How to add custom metadata to saved matplotlib plots?
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