Use df.plot.bar(stacked=True) to make a vertical stacked bar plot from a DataFrame: each row becomes a category, and each numeric column becomes a colored segment. For horizontal bars, use df.plot.barh(stacked=True).
Make a basic stacked bar plot
Arrange the DataFrame with one category per row and one series per numeric column. Then pass stacked=True explicitly; stacking is not the default for bar plots. The Pandas visualization guide shows both vertical and horizontal stacked bars.
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ax = df.plot.bar(stacked=True)Here is a complete example with regions as categories and products as the stacked series:
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import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(
{
"Product A": [12, 18, 10],
"Product B": [8, 7, 14],
"Product C": [5, 9, 6],
},
index=["North", "South", "West"],
)
ax = df.plot.bar(stacked=True, figsize=(8, 5))
ax.set(title="Sales by region", xlabel="Region", ylabel="Units")
ax.legend(title="Product")
plt.tight_layout()
plt.show()Because no x or y is specified, Pandas uses the index for category labels and plots the numeric columns. This is an illustrative example; replace the sample values and labels with your data.
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Choose the categories and values
If your category labels are stored in a column rather than the index, name that column with x and specify the value columns with y. The DataFrame plotting API accepts a column label or position for x and y.
ax = df.plot.bar(
x="region",
y=["Product A", "Product B", "Product C"],
stacked=True,
)Keep each category on a row and each component in a separate column. If the data is oriented the other way around, transpose it before plotting with df.T, then check that the resulting index and column labels correspond to the categories and series you intend to show.
Use horizontal bars for long labels
Switch to barh when category names are long or are easier to scan vertically:
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ax = df.plot.barh(stacked=True)The same row-and-column arrangement applies. The orientation changes the axis layout, not which values are stacked.
Customize colors, labels, and layout
For the ordinary single-plot case, Pandas returns a Matplotlib Axes, so you can keep the returned object and use Matplotlib methods to adjust the chart. The API documents the return type and plotting options in its plotting reference.
- Set series colors: pass a mapping so colors remain tied to column names, for example
color={"Product A": "#4472C4", "Product B": "#ED7D31", "Product C": "#70AD47"}. - Clarify the axes: use
ax.set_title(),ax.set_xlabel(), andax.set_ylabel()to describe the category and units. - Clarify the legend: use
ax.legend(title="Product")to identify the stacked series. - Improve spacing: call
plt.tight_layout()before displaying or saving the figure when labels or the legend need room.
For example, add color={"Product A": "#4472C4", "Product B": "#ED7D31", "Product C": "#70AD47"} to the df.plot.bar() call in the worked example. The plotting API also permits a single color or a sequence of colors. With subplots=True, the API returns an array of Axes rather than one Axes, so customization must target the appropriate subplot.
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Know when a stacked chart is the right choice
A stacked bar chart shows each category’s combined total and how its components contribute to that total. The bottom series shares a baseline across categories, but the interior segments do not; that makes precise comparisons between those interior values harder.
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- Use a stacked bar when the overall total and its composition both matter.
- Use grouped bars when readers need to compare each series’ values across categories more precisely.
- Use a 100% normalized stacked chart when the main question is each series’ share of a whole rather than the absolute totals. State how the values were normalized so readers do not mistake shares for raw amounts.
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