To draw multiple circles in Matplotlib, create a Circle patch for each center-and-radius pair and add the patches to an Axes. Use PatchCollection when you want to style a group together or color circles from numeric values. Set an equal aspect ratio to keep circles round, and use separate subplots only when the plots represent distinct datasets or conditions.
Plot several circles on one set of axes
A Matplotlib Circle is defined by a center coordinate and a radius. Add each patch to the same axes when the circles share a coordinate system and their locations or overlaps matter. The official Circles, Wedges and Polygons example demonstrates creating shape patches and placing them on an axes.
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="steelblue", edgecolor="black", alpha=0.6))
ax.set_aspect("equal")
ax.autoscale_view()
plt.show()The center coordinates and radii in this example are illustrative. Replace them with values from your data. ax.autoscale_view() adjusts the view after patches have been added; set explicit axis limits instead when the plot needs a fixed viewing range.
Group circles with a PatchCollection
For a group that should share styling or use numeric values to drive a color map, create the patches first and pass them to PatchCollection. Add the collection to the axes. The Matplotlib example uses this pattern for patches including circles, wedges, and polygons, so collections are not limited to circles.
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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=0.6, 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 here are made-up examples, not a dataset. In your plot, each value is associated with a circle in the same order as the patches. Label the colorbar with the meaning and units of the mapped values so readers can interpret the colors.
Keep circles geometrically round
Set ax.set_aspect("equal") when one unit on the x-axis should have the same screen length as one unit on the y-axis. Without equal scaling, unequal axis scaling can make circles appear elliptical. Matplotlib’s gallery includes examples on axis aspect and subplot layouts.
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Equal aspect can change how the axes fit within the figure, especially when the x and y limits differ substantially. Choose limits that show the full circles and preserve the coordinate range needed for comparison.
Choose face, edge, and line styling
For a small set of circles, set appearance on each patch as you add it, such as facecolor, edgecolor, and alpha. For a collection, the API supports shared face and edge colors as well as line styling such as linewidths and linestyles. See the collections API reference for collection properties.
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- Use distinct fixed colors when color identifies categories; include a legend or another clear key.
- Use a colormap and colorbar when color represents an ordered numeric value.
- Keep circle outlines visible when circles overlap or their boundaries carry meaning.
If circle size represents a quantity, specify whether the data controls radius or area. Area grows with the square of the radius, so mapping a quantity directly to radius does not make displayed area proportional to that quantity.
Decide between one axes and multiple panels
Use one axes when circles belong to the same coordinate space—for example, when their positions and overlaps are part of the comparison. Use separate panels when each circle plot represents a different dataset or condition. Create the panels with plt.subplots; Matplotlib’s gallery covers multiple-subplot layouts.
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When comparing size or position across panels, use consistent axis limits and aspect settings if the comparison depends on them. Otherwise, independent limits can make similarly sized circles look different, or differently sized circles look similar.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Matplotlib version context
The linked stable Matplotlib documentation displayed version 3.11.2 when consulted. The stable documentation is rolling, so labels or examples may change in later releases; the code uses the documented Circle, PatchCollection, and pyplot patterns.
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