Chapter 11 – Saving and Exporting Plots in Matplotlib

✅ Why Saving Plots Matters

Visualizations are often used in research papers, dashboards, and web reports. Saving your plots ensures they can be shared, reused, or embedded anywhere with consistent quality.

✅ What You’ll Learn

• How to save plots in different image formats
• How to adjust resolution (DPI) and figure size
• How to control transparent backgrounds and bounding boxes
• How to export vector graphics for print and high-quality reports

✅ 1. Basic Saving with savefig()

Matplotlib’s savefig() function saves the current figure to an image file. You can specify the file name and format.

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [10, 20, 25, 30], label="Growth")
plt.title("Simple Line Plot")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")
plt.legend()

# Save the figure
plt.savefig("plot.png")
plt.show()

By default, savefig() uses 100 DPI and saves the entire figure area.

✅ 2. Saving in Different File Formats

You can export your plots in several formats, including:

• PNG – Raster image (best for web)
• JPG – Compressed image (good for sharing)
• PDF – Vector image (best for printing)
• SVG – Scalable vector graphic (ideal for web and design tools)

Example:

plt.savefig("chart.pdf")   # Save as PDF
plt.savefig("chart.svg")   # Save as SVG
plt.savefig("chart.jpg")   # Save as JPG

✅ 3. Adjusting Resolution (DPI)

DPI (dots per inch) controls the image’s resolution.
Higher DPI produces sharper images, especially for printing.

plt.savefig("high_res_plot.png", dpi=300)

A DPI of 300–600 is recommended for print, while 100–150 works well for web.

✅ 4. Controlling Figure Size

You can set the figure’s size using the figsize parameter while creating it.

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot([1, 2, 3, 4], [2, 4, 6, 8])
ax.set_title("Custom Figure Size Example")

plt.savefig("figure_size.png", dpi=200)
plt.show()

✅ 5. Saving with Transparent Background

When placing plots on colored or patterned backgrounds (like websites), transparency helps blend them seamlessly.

plt.savefig("transparent_plot.png", transparent=True)

✅ 6. Tight Layout for Clean Margins

Sometimes labels or titles get cut off when saving.
Use bbox_inches='tight' to include all content neatly.

plt.savefig("tight_layout.png", bbox_inches='tight')

You can combine it with DPI and transparency:

plt.savefig("final_chart.png", dpi=300, bbox_inches='tight', transparent=True)

✅ 7. Exporting Vector Graphics

Vector formats like PDF and SVG maintain perfect quality at any zoom level — ideal for reports or design software.

plt.savefig("vector_output.pdf", format="pdf")
plt.savefig("vector_output.svg", format="svg")

✅ 8. Saving from Figure Object

If you’re using the object-oriented approach, call fig.savefig() instead of plt.savefig().

fig, ax = plt.subplots()
ax.plot([10, 20, 30], [3, 6, 9])
ax.set_title("Object-Oriented Save Example")

fig.savefig("oo_save.png", dpi=200, bbox_inches='tight')
plt.show()

✅ 9. File Paths and Automatic Folders

You can specify a full file path to save your plots into organized directories.

plt.savefig("C:/Projects/Plots/sales_chart.png", dpi=300)

Matplotlib will create the file if the directory exists.

✅ 10. Checking Supported Formats

To see which output formats Matplotlib supports:

import matplotlib.pyplot as plt
print(plt.gcf().canvas.get_supported_filetypes())

This prints a dictionary of all available formats such as PNG, PDF, EPS, JPG, SVG, and more.

✅ Best Practices

• Use vector formats (PDF/SVG) for high-quality publications.
• Always set dpi=300 or higher for professional reports.
• Include bbox_inches='tight' to avoid cutoff edges.
• Use descriptive filenames for better organization.
• Keep consistent figure sizes for multi-plot reports.

✅ Summary

In this chapter, you learned how to save and export Matplotlib plots in multiple formats, control their resolution and size, and ensure high-quality, publication-ready output. These tools make your visualizations ready for professional use.

In the next chapter, we’ll explore Interactive and Live Plots — learning how to make dynamic visualizations that update in real time.