A waterfall chart explains how a starting value becomes an ending value through a sequence of increases, decreases, subtotals, and totals. Plotly has a dedicated go.Waterfall trace, while Matplotlib requires you to calculate each bar’s baseline and draw it with Axes.bar(). This guide builds the same revenue bridge in both libraries, then covers labels, connectors, subtotals, validation, accessibility, and export choices.
What a waterfall chart shows
A waterfall chart is appropriate when the order and cumulative effect of changes matter. Common examples include revenue bridges, profit-and-loss analysis, budget versus actual variance, cash-flow movements, headcount changes, portfolio attribution, and conversion decomposition.
The basic relationship is:
ending value = starting value + all positive changes + all negative changes
It is usually the wrong choice for ranking unrelated categories or showing a trend over time. Use a sorted bar chart for ranking and a line chart for time series.
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Example revenue bridge
| Label | Change | Running total | Bar bottom | Bar height |
|---|---|---|---|---|
| Starting revenue | 100 | 100 | 0 | 100 |
| New sales | +60 | 160 | 100 | 60 |
| Consulting | +80 | 240 | 160 | 80 |
| Returns | -40 | 200 | 200 | 40 |
| Operating costs | -20 | 180 | 180 | 20 |
| Ending revenue | Total | 180 | 0 | 180 |
The unusual-looking negative rows are intentional: a decrease starts at the new, lower cumulative value and rises to the previous total.
Prepare waterfall data correctly
Keep three aligned fields: a label, a numeric value, and a measure type. Plotly recognizes three measure types:
- relative adds or subtracts from the current running total.
- absolute starts at a specified baseline or resets the running total.
- total displays the current cumulative total without changing it.
For the example, the first row is absolute, the middle rows are relative, and the final row is total.
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labels = [
"Starting revenue", "New sales", "Consulting",
"Returns", "Operating costs", "Ending revenue"
]
values = [100, 60, 80, -40, -20, 0]
measures = ["absolute", "relative", "relative", "relative", "relative", "total"]Using a pandas DataFrame
import pandas as pd
df = pd.DataFrame({
"label": labels,
"value": values,
"measure": measures,
})
if not (len(df["label"]) == len(df["value"]) == len(df["measure"])):
raise ValueError("All chart columns must have the same length")
allowed = {"absolute", "relative", "total"}
if not set(df["measure"]).issubset(allowed):
raise ValueError("Invalid waterfall measure")Do not silently turn missing values into zero. A missing value may mean zero, unavailable, not applicable, or suppressed data; choose that policy explicitly before plotting.
Create a waterfall chart with Matplotlib
Matplotlib’s standard API does not provide the same dedicated waterfall trace as Plotly. The normal approach is to calculate bar positions, draw rectangles with Axes.bar(bottom=...), and add connectors and labels separately. See the bar API, text API, and annotation API.
Reusable Matplotlib implementation
import matplotlib.pyplot as plt
import numpy as np
def waterfall_matplotlib(labels, values, measures=None, title=None):
if measures is None:
measures = ["absolute"] + ["relative"] * (len(values) - 1)
if not (len(labels) == len(values) == len(measures)):
raise ValueError("labels, values, and measures must have equal length")
bottoms, heights, colors, shown = [], [], [], []
running_total = 0
for value, measure in zip(values, measures):
if measure == "absolute":
running_total = value
bottoms.append(0)
heights.append(value)
colors.append("#4C78A8")
shown.append(value)
elif measure == "relative":
previous_total = running_total
running_total += value
bottoms.append(previous_total if value >= 0 else running_total)
heights.append(abs(value))
colors.append("#2CA02C" if value >= 0 else "#D62728")
shown.append(value)
elif measure == "total":
bottoms.append(0)
heights.append(running_total)
colors.append("#2F4B7C")
shown.append(running_total)
else:
raise ValueError(f"Unknown measure: {measure}")
x = np.arange(len(labels))
fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(x, heights, bottom=bottoms, color=colors, edgecolor="black", width=0.7)
for i in range(len(labels) - 1):
top = bottoms[i] + heights[i]
ax.plot([x[i] + 0.35, x[i + 1] - 0.35], [top, top],
color="gray", linestyle="--", linewidth=1)
for i, (bottom, height, value, measure) in enumerate(
zip(bottoms, heights, shown, measures)
):
if measure == "total":
y, text = height, f"{value:,.0f}"
elif measure == "absolute":
y, text = bottom + height, f"{value:,.0f}"
else:
y = bottom + height if value >= 0 else bottom
text = f"{value:+,.0f}"
ax.text(x[i], y + 4, text, ha="center", va="bottom", fontsize=9)
ax.set_xticks(x)
ax.set_xticklabels(labels, rotation=25, ha="right")
ax.set_ylabel("Value")
ax.axhline(0, color="black", linewidth=0.8)
ax.grid(axis="y", linestyle=":", alpha=0.5)
ax.set_axisbelow(True)
if title:
ax.set_title(title)
plt.tight_layout()
return fig, ax
fig, ax = waterfall_matplotlib(
labels, values, measures, title="Revenue Waterfall"
)
plt.show()Why the baseline calculation matters
- For a positive change, the bottom is the previous running total and the height is the change.
- For a negative change, the bottom is the new running total and the height is the absolute value of the change.
- For a total, the bottom is zero and the height is the running total.
Using a negative height with bottom=previous_total is a common error: it draws the bar downward from the old total instead of showing the interval between the old and new totals.
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Create a waterfall chart with Plotly
Plotly’s go.Waterfall trace handles relative, absolute, and total semantics through the measure array. The official syntax and attributes are documented in the waterfall tutorial and trace reference.
import plotly.graph_objects as go
fig = go.Figure(go.Waterfall(
name="Revenue",
orientation="v",
measure=measures,
x=labels,
y=values,
text=["100", "+60", "+80", "-40", "-20", "180"],
textposition="outside",
connector={"line": {"color": "gray", "width": 1, "dash": "dot"}},
increasing={"marker": {"color": "#2CA02C"}},
decreasing={"marker": {"color": "#D62728"}},
totals={"marker": {"color": "#2F4B7C"}},
))
fig.update_layout(
title="Revenue Waterfall",
yaxis_title="Value",
showlegend=False,
waterfallgap=0.35,
)
fig.show()When using a DataFrame, pass df["label"], df["value"], and df["measure"] directly. Plotly still depends on correctly ordered data and correctly classified measures; it does not infer business meaning from labels.
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fig.update_traces(
hovertemplate="<b>%{x}</b><br>Amount: $%{y:,.0f}<extra></extra>"
)
horizontal = go.Figure(go.Waterfall(
orientation="h",
measure=["absolute", "relative", "relative", "total"],
y=["Opening balance", "Sales", "Costs", "Closing balance"],
x=[100, 50, -30, 0],
connector={"line": {"color": "gray"}},
))
horizontal.show()With orientation="h", category labels are on the y-axis and numeric values are on the x-axis. textposition accepts inside, outside, auto, or none. Outside labels can be clipped on dense figures, so inspect the rendered result.
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Subtotals and multiple traces
Use "total" for intermediate subtotals as well as the final result:
measure = [
"absolute", "relative", "relative", "total",
"relative", "relative", "total"
]Plotly also supports multiple waterfall traces for years, regions, or scenarios. The official examples use grouped category labels and waterfallgroupgap to separate groups. Several traces can become dense; small multiples are often easier to read.
Matplotlib or Plotly?
| Criterion | Matplotlib | Plotly |
|---|---|---|
| Waterfall primitive | Compose bars, lines, and annotations | Dedicated go.Waterfall trace |
| Interactivity | Requires additional tooling | Built in |
| Static publishing | Excellent for PNG, SVG, and PDF workflows | Possible with export tooling |
| Cumulative bookkeeping | You calculate positions | measure controls semantics |
| Styling | Very granular | High-level and declarative |
| Best fit | Reports, papers, and print | Notebooks, web pages, and dashboards |
Choose Matplotlib when you need exact control over geometry and an established static publication style. Choose Plotly when readers need hover values, zooming, responsive sizing, or browser-based interaction. Plotly figures can be placed in a Dash Graph component for Python web applications; see Dash documentation.
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Production checks and troubleshooting
Validate the mathematics
- Mark the opening observation as
absolute. - Mark every true subtotal or ending balance as
total, notrelative. - Calculate labels from full-precision values and round only for display.
- If a report uses rounded inputs, calculate the bridge from those same rounded inputs.
- Check that units, currency, sign convention, and source period are visible in the title, axis, or caption.
Handle missing values explicitly
import pandas as pd
if pd.isna(value):
raise ValueError(
"Missing waterfall value; decide whether it means zero or unknown"
)Keep dense charts readable
- Group immaterial movements into an “Other” category.
- Use horizontal orientation when labels are long.
- Show labels only for material changes and provide a companion table for detail.
- Add y-axis headroom when labels sit outside bars.
- Use a distinct neutral color for totals and visible connectors between steps.
Make colors accessible
Green for increases and red for decreases is familiar but should not be the only encoding. Consider blue for increases, orange or gray for decreases, and a darker neutral for totals. Keep explicit plus and minus signs, labels, or hatching so the meaning survives grayscale and color-vision differences.
Starting below zero and multiple resets
The same positive/negative logic works when the opening value is negative, but test label positions and y-axis margins around the zero line. Absolute bars and subtotal markers can reset or report the running state, so the final total is not automatically the sum of every raw value unless the bridge is one continuous sequence of relative changes.
Export and deployment options
Matplotlib can save static output directly:
fig.savefig("revenue_waterfall.png", dpi=200, bbox_inches="tight")
fig.savefig("revenue_waterfall.svg", bbox_inches="tight")
fig.savefig("revenue_waterfall.pdf", bbox_inches="tight")Plotly can be shared as interactive HTML:
fig.write_html("revenue_waterfall.html", include_plotlyjs="cdn")Static Plotly image export may require an additional renderer such as Kaleido, and package requirements can change. Check the current Plotly export documentation and your installed version before relying on a static-export workflow. The API pages retrieved for this guide represent Plotly 6.8.0 documentation, while the Matplotlib reference PDF represents 3.10.7; these are documentation versions, not universal minimum-version requirements.
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When another chart is better
- Sorted bar chart: compare many independent categories.
- Stacked bar chart: show composition at several points.
- Line chart: show a value changing over time.
- Tornado chart: show sensitivity around a baseline.
- Sankey diagram: show flows between entities rather than a single cumulative bridge.
The Bottom Line
Use Matplotlib for highly controlled static reports and Plotly for interactive notebooks, web pages, and dashboards. In either library, correct ordering, explicit absolute/relative/total semantics, and careful negative-bar placement are what make the waterfall mathematically trustworthy.
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