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Use matplotlib.pyplot.bar() with an explicit bottom for each series. For mixed positive and negative data, maintain a separate running total for each sign and category: positive segments stack above zero, while negative segments stack below it.
Build a diverging stacked bar chart
This pattern uses NumPy arrays and one set of category labels. For each series, np.where() selects the appropriate starting point category by category; the two totals are then updated with only that series’ positive or negative portions.
import matplotlib.pyplot as plt
import numpy as np
labels = ["Jan", "Feb", "Mar", "Apr"]
data = {
"Series A": np.array([12, -5, 8, -3]),
"Series B": np.array([4, -7, -2, 6]),
"Series C": np.array([-3, 2, 5, -4]),
}
fig, ax = plt.subplots()
pos_bottom = np.zeros(len(labels))
neg_bottom = np.zeros(len(labels))
for name, values in data.items():
bottom = np.where(values >= 0, pos_bottom, neg_bottom)
ax.bar(labels, values, bottom=bottom, label=name)
pos_bottom += np.clip(values, 0, None)
neg_bottom += np.clip(values, None, 0)
ax.axhline(0, color="black", linewidth=0.8)
ax.set_ylabel("Value")
ax.legend()
plt.show()Matplotlib’s bar() function uses bottom as the baseline where each vertical bar begins. It does not automatically calculate cumulative baselines across separate calls. The official stacked-bar example demonstrates the cumulative-bottom approach for positive values; the separate positive and negative accumulators above extend that per-bar baseline behavior to mixed-sign data. See the Matplotlib 3.11.0 bar() API reference and the official stacked-bar gallery example.
Why separate totals are necessary
Each category needs its own positive and negative running totals. A positive value starts at the positive total accumulated so far for that category; a negative value starts at the negative total. Updating the totals with np.clip() keeps each side independent: positive values add only to pos_bottom, and negative values add only to neg_bottom.
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A single sign-blind running sum can move a later segment to the wrong side of zero or make it overlap another segment. Likewise, using only the immediately preceding series’ value as the next baseline does not account for all earlier segments on that side. The baseline must include the full accumulated height or depth for that category.
Read and adapt the chart correctly
- Keep the signs. Negative values represent downward contributions. Converting them to absolute values changes the data’s meaning unless the chart is intentionally about magnitude alone.
- Show zero clearly. The horizontal reference line makes the split between upward and downward contributions visible. Label the axis with the measure and units your data uses.
- Use matching shapes. Each series array must provide one value per category so that the baseline arrays and bar values align.
- Choose the chart for the question. Diverging stacks show signed composition and accumulated positive and negative contributions. Segments that do not start at zero are harder to compare precisely across categories; grouped bars may be a better choice when exact series-by-series comparison is the priority.
Matplotlib version and horizontal bars
The linked API reference is for Matplotlib 3.11.0, and the stable gallery page identifies its documentation as 3.11.2. Those official materials document the bottom baseline behavior and a conventional all-positive stacked example; they do not identify a separate negative-stacking API. The code here is an instructional pattern based on those documented semantics, not a claim that the gallery itself demonstrates mixed-sign stacking.
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For horizontal bars, barh() uses the analogous left baseline parameter rather than bottom. The sources linked above cover vertical bar(); consult the current barh() API before adapting the pattern.
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