To overlay two bar charts, plot both datasets on the same Matplotlib Axes using the same category positions. The second set is drawn on top of the first, so use distinct colors and partial transparency if you need to see both. If you want to compare values without one set covering the other, use grouped bars instead.
Overlay two bar charts at the same positions
Each call to ax.bar() draws bars at the x positions you provide. Reusing those positions puts the datasets over the same categories; bars from the later call can obscure bars from the earlier call. Labels feed the legend, while alpha controls transparency.
import matplotlib.pyplot as plt
categories = ["A", "B", "C"]
values_one = [12, 18, 14]
values_two = [10, 21, 16]
fig, ax = plt.subplots()
ax.bar(categories, values_one, color="tab:blue", alpha=0.55, label="Series one")
ax.bar(categories, values_two, color="tab:orange", alpha=0.55, label="Series two")
ax.set_ylabel("Value")
ax.set_title("Overlaid bar charts")
ax.legend()
plt.show()This uses the documented bar interface, which supports category positions, labels, colors, and rectangle properties such as transparency. See the Matplotlib bar API.
When overlaying works well
- Use the same positions when the categories correspond exactly in both datasets.
- Choose contrasting colors and keep the legend so readers can identify each series.
- Use partial transparency when the front bars would otherwise hide the bars behind them. Transparency reveals some of the lower bars, but the blended colors can make values harder to distinguish.
For direct comparison, keep both datasets on a compatible scale. If exact values or clear color distinction matter more than showing overlap, grouped bars are usually easier to read.
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Make a grouped chart for side-by-side comparison
Grouped bars shift each series a little to either side of the category center. This avoids one dataset covering the other.
import numpy as np
import matplotlib.pyplot as plt
categories = ["A", "B", "C"]
values_one = [12, 18, 14]
values_two = [10, 21, 16]
x = np.arange(len(categories))
width = 0.38
fig, ax = plt.subplots()
ax.bar(x - width / 2, values_one, width, label="Series one")
ax.bar(x + width / 2, values_two, width, label="Series two")
ax.set_xticks(x, categories)
ax.legend()
plt.show()The key is the offset: x - width / 2 and x + width / 2 place the two bars around each category’s center. Matplotlib’s grouped bar chart example uses this approach.
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The stable documentation also includes pyplot.grouped_bar, a higher-level categorical API added in Matplotlib 3.11. The 3.11.2 documentation marks it provisional. Check that the installed version provides it before using it; explicit positions with bar() give you fine control and work across more versions. See the grouped_bar API documentation.
Use stacked bars only for additive components
Stack bars when each series represents part of a combined total, not when two independent values should be compared. Pass the earlier values as the later bars’ bottom:
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fig, ax = plt.subplots()
ax.bar(categories, values_one, label="Series one")
ax.bar(categories, values_two, bottom=values_one, label="Series two")
ax.legend()
plt.show()With bottom=values_one, the second series starts at the top of the first, so the bar height represents their sum. Matplotlib’s stacked bar example demonstrates this pattern. The Matplotlib bars gallery treats grouped and stacked charts as distinct designs.
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Choose the layout that matches the data
| Layout | Use it when | Main trade-off |
|---|---|---|
| Overlay | You want to see overlap at shared category positions. | The later bars can obscure earlier ones; transparency can blend colors. |
| Grouped | You want to compare independent values side by side. | Bars take more horizontal space. |
| Stacked | Values are components that add to a total. | Individual components are harder to compare across categories than in a grouped chart. |
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