While working on a data visualization project in Python, I needed to save only one specific chart (axis) from a Matplotlib figure as a PNG file.
At first, I thought it would be simple, just use plt.savefig(), right? But I realized that saving a single axis instead of the entire figure needed a bit more control.
In this guide, I’ll show you how to save a specific Matplotlib axis as a PNG image using different methods. Each method is beginner-friendly and explained step-by-step, so you can easily choose the one that fits your workflow.
Method 1 – Save the Entire Figure as PNG Using savefig()
Before we get into saving a specific axis, let’s start with the basics. The most common way to save a Matplotlib figure in Python is by using the savefig() function.
This method saves the entire figure (including all axes) as a PNG image.
Here’s a simple example:
import matplotlib.pyplot as plt
# Create a figure with two subplots
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
# Plot data on each axis
axes[0].plot([1, 2, 3, 4], [10, 20, 25, 30], color='blue', label='Sales')
axes[0].set_title('Sales Growth')
axes[1].bar(['Q1', 'Q2', 'Q3', 'Q4'], [15, 25, 35, 45], color='green', label='Revenue')
axes[1].set_title('Quarterly Revenue')
# Save the entire figure as a PNG file
plt.savefig('full_figure.png', dpi=300, bbox_inches='tight')
# Show the figure
plt.show()You can see the output in the screenshot below.
In this example, the savefig() function saves the entire figure (both subplots) as a single PNG file named full_figure.png.
The dpi=300 ensures that the image is high-resolution, suitable for printing or professional reports. The bbox_inches=’tight’ ensures that labels and titles are not cut off.
Method 2 – Save a Specific Axis as PNG Using bbox_inches
Now, let’s move to the main goal, saving only one axis from a Matplotlib figure as a PNG image. This is useful when you have multiple subplots but only want to export one of them, such as a revenue chart for a presentation.
Here’s how you can do it:
import matplotlib.pyplot as plt
# Create a figure with two subplots
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
# Plot on each axis
axes[0].plot([1, 2, 3, 4], [100, 200, 250, 300], color='red', label='Profit')
axes[0].set_title('Profit Over Time')
axes[1].bar(['Jan', 'Feb', 'Mar', 'Apr'], [50, 70, 90, 120], color='orange', label='Expenses')
axes[1].set_title('Monthly Expenses')
# Save only the second axis as PNG
extent = axes[1].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
fig.savefig('expenses_axis.png', bbox_inches=extent, dpi=300)
plt.show()You can see the output in the screenshot below.
In this code, we use the get_window_extent() method to get the bounding box of the axis we want to save.
Then, we pass that bounding box to the bbox_inches parameter of savefig(). This ensures only the selected axis (in this case, the “Monthly Expenses” chart) is saved as a PNG image.
Method 3 – Save a Matplotlib Axis Using Figure.savefig() and Axes.figure
Another method I often use is calling the savefig() function directly from the axis’s figure object. This gives you more flexibility, especially when working with dynamic figures in large Python projects.
Here’s a practical example:
import matplotlib.pyplot as plt
# Create figure and axis
fig, ax = plt.subplots(figsize=(6, 4))
# Plot sample data
x = [1, 2, 3, 4, 5]
y = [10, 15, 25, 30, 45]
ax.plot(x, y, color='purple', marker='o', label='Growth Rate')
ax.set_title('Company Growth Rate')
ax.set_xlabel('Year')
ax.set_ylabel('Percentage')
# Save the axis using its figure reference
ax.figure.savefig('growth_rate_axis.png', dpi=300, bbox_inches='tight')
plt.show()You can see the output in the screenshot below.
In this example, I called ax.figure.savefig() instead of plt.savefig(). This approach is especially helpful when you’re working with multiple figures in the same Python script and want to save specific plots without confusion.
Method 4 – Save Multiple Axes Individually in a Loop
In some cases, you may want to save multiple axes separately, for example, when you’re generating multiple charts for a report or dashboard.
You can easily automate this process using Python for loop.
Here’s how I usually do it:
import matplotlib.pyplot as plt
import numpy as np
# Create multiple subplots
fig, axes = plt.subplots(2, 2, figsize=(10, 8))
# Generate sample data
x = np.linspace(0, 10, 100)
# Plot different functions
functions = [np.sin, np.cos, np.tan, np.exp]
titles = ['Sine Wave', 'Cosine Wave', 'Tangent', 'Exponential']
# Loop through each axis and save individually
for i, ax in enumerate(axes.flat):
y = functions[i](x)
ax.plot(x, y, label=titles[i])
ax.set_title(titles[i])
ax.legend()
# Define file name based on title
filename = f"{titles[i].lower().replace(' ', '_')}.png"
# Save each axis
extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
fig.savefig(filename, bbox_inches=extent, dpi=300)
plt.show()You can see the output in the screenshot below.
In this Python example, each subplot is automatically saved as a separate PNG file with a descriptive filename.
This method is extremely useful when you’re generating multiple visualizations programmatically, for example, saving daily charts for a U.S.-based sales dashboard.
Method 5 – Save Matplotlib Axis as PNG Using BytesIO (Without Saving to Disk)
Sometimes, you may want to save your Matplotlib axis as a PNG without writing it to disk, for example, when integrating with a web application or sending the image via email.
You can use Python’s built-in io.BytesIO module for this purpose.
Here’s a complete example:
import matplotlib.pyplot as plt
import io
# Create a sample plot
fig, ax = plt.subplots(figsize=(6, 4))
ax.plot([1, 2, 3, 4], [5, 7, 9, 12], color='teal', marker='s', label='Data')
ax.set_title('Online Data Visualization')
ax.legend()
# Save axis to a BytesIO object
buf = io.BytesIO()
extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
fig.savefig(buf, format='png', bbox_inches=extent, dpi=300)
buf.seek(0)
# You can now use 'buf' as an image stream (for web apps or APIs)
print("Axis saved to memory as PNG stream!")In this Python example, the axis is saved as a PNG image in memory. You can then use this image stream directly in Flask, Django, or FastAPI applications, perfect for real-time chart rendering in dashboards.
Additional Tips for Saving Matplotlib Axes as PNG
Here are some quick professional tips from my experience:
- Always use
dpi=300for print-quality images. - Use bbox_inches=’tight’ to ensure labels aren’t cut off.
- To save transparent backgrounds, add transparent=True to savefig().
- For consistent output, call plt.tight_layout() before saving.
- When saving multiple axes, ensure each has a unique filename to avoid overwriting.
These small tweaks can make a big difference in the final quality of your exported charts.
Saving a specific Matplotlib axis as a PNG in Python might seem tricky at first, but once you understand how bbox_inches and get_window_extent() work, it becomes second nature.
Whether you’re preparing a presentation, automating report generation, or building a web dashboard, the methods I shared above will help you export your plots cleanly and efficiently.
I personally use the bbox_inches approach most often because it gives me precise control over what part of the figure gets saved.
You may also like to read:
- Save Matplotlib Graph as PNG in Python
- Save a Matplotlib Plot as a Transparent PNG in Python
- Save NumPy Array as PNG Image in Python Matplotlib
- Save a Matplotlib Plot as PNG Without Borders in Python
I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.
