To draw multiple circles on one Matplotlib plot, create a Circle patch for each center and radius, then add the patches to an Axes. For shared styling or numeric color mapping, group them in a PatchCollection. Set an equal aspect ratio when the circles must look geometrically round rather than stretched.
Plot multiple circles on one set of axes
Each matplotlib.patches.Circle is defined by a center coordinate and a radius. Add the patches individually when you want direct control over a modest number of circles:
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
circles = [
((0, 0), 0.4),
((1, 1), 0.7),
((2, 0), 0.5),
]
fig, ax = plt.subplots()
for center, radius in circles:
ax.add_patch(Circle(center, radius, facecolor="tab:blue", alpha=0.5))
ax.set_aspect("equal")
ax.autoscale_view()
plt.show()The tuple for each circle is (center, radius); the center is an (x, y) pair. Coordinates and radii use the axes’ data units. The official Matplotlib gallery demonstrates constructing circle patches from coordinates and adding shape patches to an axes: Circles, Wedges and Polygons.
Use a PatchCollection for shared styling or value-based colors
A PatchCollection groups patches so they can be styled together and, when supplied with numeric values, colored through a colormap. The following example assigns each circle a value and connects the mapped colors to a labeled colorbar:
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import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
centers = [(0, 0), (1, 1), (2, 0)]
radii = [0.4, 0.7, 0.5]
values = [10, 25, 15]
fig, ax = plt.subplots()
circles = [Circle(center, radius) for center, radius in zip(centers, radii)]
collection = PatchCollection(circles, alpha=0.5, cmap="viridis")
collection.set_array(values)
ax.add_collection(collection)
ax.set_aspect("equal")
ax.autoscale_view()
fig.colorbar(collection, ax=ax, label="Value")
plt.show()The values and coordinates above are illustrative; replace them with data of matching lengths. Matplotlib’s gallery example uses a collection for circles, wedges, and polygons, maps an array to colors, and adds a colorbar: Circles, Wedges and Polygons. Collections also expose shared face-color, edge-color, linewidth, and linestyle settings; see the collections API reference.
Choose color to match the data
- Categories: assign a distinct face color to each category and provide a legend or direct labels so the colors are interpretable.
- Numeric values: set the collection’s array and use a colormap; label the colorbar with the quantity and units, if applicable.
- Outline-focused plots: style edge colors and linewidths, and use transparent or unfilled faces where appropriate.
A circle’s radius and area do not encode quantities in the same way: area increases with the square of radius. If size represents a value, state whether the mapping is to radius or area so readers can interpret the plot correctly.
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Keep circles round with equal aspect
Matplotlib axes may scale the x and y directions differently to fit the available drawing area. When that happens, a circle can appear elliptical. Use ax.set_aspect("equal") so one data unit has the same displayed length on both axes. The Matplotlib gallery includes examples of equal axis aspect and other plotting layouts: Examples.
Choose one axes or separate panels
Use one axes when circles share coordinates
Plot circles together when their positions, overlap, or relative placement belong to one coordinate system. Keep them on the same axes and use consistent scales so spatial relationships remain meaningful.
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Use subplots for separate datasets or conditions
When each circle plot represents a different dataset or condition, create separate axes with plt.subplots. If size or position is meant to be compared across panels, use comparable axis limits and aspect settings:
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
for ax, centers in zip(axes, datasets):
for center, radius in circles_for(centers):
ax.add_patch(Circle(center, radius))
ax.set_aspect("equal")
ax.set_xlim(-1, 4)
ax.set_ylim(-1, 3)
plt.show()Here, datasets and circles_for stand for the data and conversion logic in your program. Matplotlib’s examples gallery covers subplot layouts and plt.subplots.
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Common plotting problems
- Circles look stretched: set equal aspect on the axes.
- Some circles are clipped: ensure the view limits include each center plus or minus its radius. After adding patches,
ax.autoscale_view()is a convenient way to update the view. - The colorbar is blank or misleading: confirm the collection has a numeric array via
set_array, and label the colorbar to identify the encoded variable. - Circle sizes are hard to compare across panels: align the relevant x/y limits and aspect settings across those axes.
The cited stable Matplotlib documentation displayed version 3.11.2 when reviewed; the stable pages are rolling documentation, so labels and examples may change over time.
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