For separate categories, label one scatter plot per group and call ax.legend(). For values represented by color or marker size in a single scatter plot, use the collection’s legend_elements() method and pass its handles and labels to the legend.
Choose the legend method that matches your plot
| What the markers represent | Recommended method |
|---|---|
| Distinct groups, such as product types or classes | Plot each group separately, assign a descriptive label, then call ax.legend(). Matplotlib’s scatter legend gallery demonstrates this pattern. |
| Numeric values mapped to color in one collection | Call points.legend_elements(prop="colors") and pass the returned handles and labels to ax.legend(). |
| Numeric values mapped to marker size in one collection | Call points.legend_elements(prop="sizes"). If you transformed the input values to calculate sizes, provide the inverse transformation with func so legend labels show the original values. |
| Both color and size mappings | Generate a separate legend for each mapping. Add the first legend back to the Axes with ax.add_artist() before creating the second. |
Show discrete groups with labeled scatter calls
Give each group its own scatter() call and a meaningful label. Matplotlib then connects each legend entry to the artist that plots that group.
fig, ax = plt.subplots()
for group, color in groups:
ax.scatter(group.x, group.y, color=color, label=group.name)
ax.legend(title="Group")The title clarifies what the entries represent. For example, a title such as “Group” distinguishes categorical labels from numeric color or size values.
Generate legend entries for color values
When a single scatter collection uses color to encode data, retain the object returned by scatter(). It provides legend_elements(), which generates handles and labels for the mapped colors.
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points = ax.scatter(x, y, c=values)
handles, labels = points.legend_elements(prop="colors")
ax.legend(handles, labels, title="Value")You can use the num and fmt options to control how many numeric entries appear and how their labels are formatted. These controls are useful when a plot has many possible values and a legend showing every value would be difficult to read. See the Matplotlib collections API for the method’s options.
Generate a size legend, including transformed values
For marker sizes that encode data, use prop="sizes":
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handles, labels = points.legend_elements(prop="sizes")
ax.legend(handles, labels, title="Size")If marker areas were calculated from a transformed version of the original quantity, pass a function that reverses that transformation with func. Otherwise, the generated labels may describe the transformed sizes rather than the values readers need to interpret. The collections API documentation describes legend_elements() and its parameters.
Keep color and size legends together
A single scatter collection can encode two quantities, one by color and another by size. Create separate, titled legends so readers can tell which visual property each legend explains. Add the first legend to the Axes before creating the second; otherwise, the second call to ax.legend() replaces the first.
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points = ax.scatter(x, y, c=classes, s=sizes)
color_legend = ax.legend(
*points.legend_elements(prop="colors"),
title="Class",
loc="upper left"
)
ax.add_artist(color_legend)
size_handles, size_labels = points.legend_elements(prop="sizes", alpha=0.6)
ax.legend(size_handles, size_labels, title="Size", loc="lower right")This sequence follows Matplotlib’s two-legend gallery example. Choose positions that do not obscure the plotted points.
Set the legend position
Use loc to choose a standard position, such as "upper left" or "lower right". Use bbox_to_anchor when you need more control over the anchor point or want to position a legend relative to the Axes or Figure. The Matplotlib figure API documents legend placement options.
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ax.legend(loc="upper left", bbox_to_anchor=(1.02, 1))When a plot has multiple legends, assign them different positions and check that neither covers important data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Fix an empty or incorrect legend
No entries appear
ax.legend() discovers entries from artists with labels. Artists whose labels begin with an underscore are excluded by default, so a legend call with no eligible labeled artists produces an empty legend; pyplot documents a warning for this case. Assign labels when creating the scatter artists or later with set_label(). See the pyplot legend reference.
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Entries do not match the plotted groups
When automatic discovery is not suitable, supply both handles and labels explicitly:
ax.legend(handles, labels)Keep the lists in matching order: each handle is paired with the label at the same position. The pyplot documentation discourages passing labels alone for existing artists because their association depends on order and can be mixed up.
Check the Matplotlib version
The cited stable documentation pages identified Matplotlib 3.11.2 for the scatter gallery, collections API, and figure API, and 3.11.1 for the pyplot legend reference; the pages were accessed on October 4, 2026. Stable documentation can change. If your code targets a materially older Matplotlib version, check that version’s API documentation for the options you plan to use.
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