Use Matplotlib’s scatter() arguments to control marker shape, size, and color: marker sets the shape, s sets marker area in points squared, and c sets a fixed color or values to map through a colormap.
Set a marker’s shape, size, and color
Pass the options to Axes.scatter():
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(x, y, marker="^", s=50, c="tab:blue")This draws upward triangles in blue. The complete list of supported marker styles and symbols is in the Matplotlib marker reference.
Choose a marker shape with marker
The marker argument accepts a marker style or shorthand. Common choices include "o" for circles, "s" for squares, "^" and "v" for upward and downward triangles, "D" for diamonds, and "*" for stars. Use the marker reference for the full catalog.
Set marker size with s
s is marker area measured in points squared, not a diameter. It accepts one value for all points or an array-like value for individual points. If omitted, its default is rcParams['lines.markersize'] ** 2, as specified by the scatter API.
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sizes = [20, 60, 120]
ax.scatter(x, y, s=sizes)When size represents data, map values to a visible range and explain the encoding in a legend, caption, or surrounding text. The Matplotlib scatter example demonstrates varying point sizes.
Set fixed colors or map values to colors
Use c for a single named color, a sequence of colors, or numeric values that Matplotlib maps to colors. For numeric data, set a colormap with cmap; norm controls how values are normalized before mapping. This example fixes the color range from 0 to 1 and adds a colorbar:
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values = [0.1, 0.5, 0.9]
points = ax.scatter(x, y, c=values, cmap="viridis", vmin=0, vmax=1)
fig.colorbar(points, ax=ax, label="Value")Use vmin and vmax with the default normalization; when supplying a custom norm, configure the limits on that normalization instead. A colorbar makes a quantitative color mapping interpretable. See the scatter example with color values and the API reference.
Avoid ambiguous RGB(A) input
A one-dimensional sequence of three or four numbers can be interpreted as scalar values to map, rather than one RGB or RGBA color. To specify one color explicitly, use a color string or provide RGB(A) colors as a two-dimensional array, with one row per color. This distinction is documented in the scatter API.
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Adjust outlines and transparency
Set marker outlines with edgecolors and linewidths, and use alpha to control transparency. Matplotlib ignores edgecolors for non-filled markers, so an outline option may have no visible effect for those shapes. Check the API documentation for the behavior and available options.
Use different marker shapes for groups
For groups that need different shapes, make a separate scatter() call for each group and set its marker there. A 2016 Matplotlib Discourse answer recommends this approach; because that advice is historical rather than a current API guarantee, check behavior on the Matplotlib version you use. If multiple calls encode numeric values with color, give them the same colormap and normalization so the colors remain comparable. See the Discourse discussion.
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Make the encodings readable
- Choose marker sizes and shapes that remain distinguishable at the plot’s final display or print size.
- Use color for a fixed category or a quantitative scale, and explain which one it represents.
- For numeric colors, keep the range consistent when comparing plots and provide a colorbar or other clear key.
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