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Call savefig on the Matplotlib figure you want to export. For a pyplot chart, use plt.savefig("chart.png"); for a figure object, use fig.savefig("chart.png"). The .png extension normally tells Matplotlib which format to write. Add options such as DPI, tight cropping, or transparency only when you need them.
Save a Matplotlib figure as a PNG
After creating your chart, save the figure with savefig. The pyplot function saves the current figure; the Figure method saves the specific object you call it on. See the Matplotlib 3.11.0 savefig API reference.
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [2, 4, 3])
plt.title("Example chart")
plt.savefig("chart.png")With the object-oriented API, keep and save the figure object directly:
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 3])
ax.set_title("Example chart")
fig.savefig("chart.png")Both examples save a PNG because the filename ends in .png. Matplotlib infers the format from a filename extension when format is not specified. If you explicitly set format="png", Matplotlib uses the filename exactly as supplied; it does not add or correct the extension. This is useful with file-like objects or filenames without a format-identifying suffix.
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Choose the output resolution
The dpi setting controls the raster resolution. By default, savefig uses the figure’s DPI (dpi="figure"); specify a number when a destination requires a particular resolution:
fig.savefig("chart.png", dpi=300)Here, 300 is an explicit choice, not a Matplotlib requirement. The resulting pixel dimensions depend on both the figure’s physical dimensions and the DPI, so increasing DPI increases the raster dimensions for a given figure size. Check the target requirements for a report, slide, or web asset rather than assuming one DPI fits every use.
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Control cropping and margins
Use bbox_inches="tight" to make the saved bounds fit the figure’s tight extent, which can include labels and other elements beyond the axes. Use pad_inches to control the margin around those bounds:
fig.savefig("chart.png", bbox_inches="tight", pad_inches=0.15)For Matplotlib’s documented configuration defaults, the padding value is 0.1 inch. A tight crop changes the output bounds; if the result feels cramped, increase the padding or omit the tight bounding box.
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Save with a transparent background
Set transparent=True when the PNG needs to blend into a page or slide background:
fig.savefig("chart.png", transparent=True)This makes the Axes patches transparent and also the Figure patch unless face or edge colors are supplied. Matplotlib restores the transparency settings after saving. Transparency is off by default in the documented configuration; it is different from explicitly saving with an opaque white background.
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Use the right API for your data
Use savefig for a chart containing axes, titles, labels, legends, or other figure artists. Use matplotlib.image.imsave when you have image-array data to write, not as a substitute for exporting a plotted figure.
| What you are saving | API | Behavior |
|---|---|---|
| A Matplotlib figure or plot | plt.savefig(...) or fig.savefig(...) |
Writes the rendered figure, with options for format, resolution, bounds, padding, and background. |
| A numeric image array | matplotlib.image.imsave(...) |
Colormaps scalar data or writes RGB/RGBA image values. The documented array forms include grayscale (M, N), RGB (M, N, 3), and RGBA (M, N, 4). See the Matplotlib image API. |
Set PNG metadata when needed
The Agg PNG writer accepts metadata as key/value pairs. Documented keys include Title, Author, Description, Copyright, Creation Time, Software, Disclaimer, Warning, Source, and Comment. If you omit Software, Matplotlib supplies an autogenerated value; set it to None to remove it. Consult the Agg backend API for the metadata interface.
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Why an output may differ from the example
Matplotlib configuration parameters (rcParams) can affect defaults such as DPI, bounding box, padding, and transparency. If your output differs from an example that omits options, check your configuration and the documentation for your installed Matplotlib version. The configuration documentation lists the current stable defaults.
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