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To add numeric labels to multiple bars in a Matplotlib chart, call ax.bar_label() on each BarContainer returned by ax.bar(). For grouped bars, keep the container from every bar call and label each separately. Use label_type="center" for the size of each stacked segment, or the default "edge" for its endpoint value.
Label every series in a grouped bar chart
Each call to ax.bar() returns a container for that series. Pass each container to ax.bar_label() to annotate its bars. This example uses explicit horizontal positions so the two series sit side by side within each category:
import matplotlib.pyplot as plt
categories = ["A", "B", "C"]
series_a = [4, 7, 5]
series_b = [6, 3, 8]
x = range(len(categories))
width = 0.38
fig, ax = plt.subplots()
bars_a = ax.bar([i - width / 2 for i in x], series_a, width, label="Series A")
bars_b = ax.bar([i + width / 2 for i in x], series_b, width, label="Series B")
ax.bar_label(bars_a, fmt="{:g}", padding=3)
ax.bar_label(bars_b, fmt="{:g}", padding=3)
ax.set_xticks(list(x), categories)
ax.legend()
fig.tight_layout()
plt.show()The first argument to bar_label is the relevant container, not the category list. The fmt option controls the text representation of values, while padding sets the distance between a label and its bar. Matplotlib’s grouped bar chart examples illustrate labeling the containers from separate bar calls.
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Choose between numeric values and custom labels
For a single series, one call labels the whole container. Supply labels when the text should be custom rather than derived from the bar values:
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bars = ax.bar(categories, values)
ax.bar_label(bars, labels=["four", "seven", "five"])Alternatively, use fmt to format the values automatically. The default format is %g; a formatter can also be callable. The bar_label API documents these options and their version history.
Use center labels for stacked bars
For stacked bars, label each component container. The default label_type="edge" places a label at the segment’s endpoint and reports the endpoint value. Set label_type="center" when the label should show the size of an individual segment instead. This distinction matters because a stacked segment’s endpoint can represent the cumulative total, while its length represents only that segment.
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bottom = [0, 0, 0]
for name, values in components.items():
bars = ax.bar(categories, values, bottom=bottom, label=name)
ax.bar_label(bars, label_type="center")
bottom = [b + v for b, v in zip(bottom, values)]Keep bar values, category names, and legend labels distinct
These three label types serve different purposes:
- Bar labels are text annotations on the bars, added with
bar_label. - Category labels identify positions along the category axis. In the example,
ax.set_xticks(list(x), categories)assigns them. Matplotlib’sAxes.bardocumentation also allows category strings as x values or category names viatick_label. - Legend labels name datasets. Set them with
labelin eachbarcall, then show the legend withax.legend().
Matplotlib 3.11 adds the higher-level Axes.grouped_bar interface for shared categories and multiple datasets. Its documented API is provisional, so explicit bar calls remain useful when you need direct control over positions or individual bars. See the grouped_bar API and the Matplotlib 3.11.0 release notes, dated June 11, 2026.
Check compatibility and prevent clipped labels
Formatting and padding options vary by Matplotlib version. Callable formatters and brace-style formatting such as "{:g}" were added in Matplotlib 3.7; per-label array padding was added in 3.11. Check the documentation for your installed version if an option is unavailable. The cited API pages do not provide a complete compatibility table for every release.
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Labels can extend beyond the axes limits, particularly above positive bars or below negative bars. Matplotlib notes that you may need to adjust axis limits to fit them. Inspect the rendered figure; if labels are clipped, expand the relevant axis range and use fig.tight_layout() to help fit the chart elements within the figure.
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