A horizontal bar chart where bars are sorted by influence magnitude, extending left and right from a base case vertical reference line. Each bar represents one input parameter and shows how varying that parameter between its low and high values affects the output, creating a characteristic tornado shape (widest bars at top, narrowest at bottom). Dual colors distinguish low-input from high-input effects, making it immediately clear which parameters drive the most uncertainty.
""" anyplot.ai
bar-tornado-sensitivity: Tornado Diagram for Sensitivity Analysis
Library: letsplot 4.10.1 | Python 3.13.13
Quality: 90/100 | Updated: 2026-06-02
"""
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
flavor_high_contrast_dark,
flavor_high_contrast_light,
geom_bar,
geom_text,
geom_vline,
ggplot,
ggsave,
ggsize,
labs,
layer_tooltips,
scale_fill_manual,
scale_x_continuous,
theme,
theme_minimal,
)
LetsPlot.setup_html()
# Theme tokens — Imprint palette, theme-adaptive chrome
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — semantic exception: gain/loss for financial sensitivity
# High Scenario (upper NPV bound) → #009E73 brand green (profit/gain)
# Low Scenario (lower NPV bound) → #AE3030 matte red (loss/downside)
COLOR_HIGH = "#009E73"
COLOR_LOW = "#AE3030"
# Data — NPV sensitivity analysis for a capital investment project
base_npv = 12.5 # Base case NPV in $M
parameters = [
"Discount Rate",
"Revenue Growth",
"Material Cost",
"Labor Cost",
"Sales Volume",
"Tax Rate",
"Salvage Value",
"Operating Expenses",
"Inflation Rate",
"Capacity Utilization",
]
low_values = [16.8, 8.2, 14.9, 13.8, 9.1, 14.2, 11.6, 13.9, 13.4, 10.8]
high_values = [9.1, 17.3, 10.4, 11.0, 16.2, 10.9, 13.5, 11.2, 11.7, 14.1]
df = pd.DataFrame({"parameter": parameters, "low_value": low_values, "high_value": high_values})
# Sort by total range (widest bar at top)
df["total_range"] = abs(df["high_value"] - df["low_value"])
df = df.sort_values("total_range", ascending=True).reset_index(drop=True)
# Build long-form data: each parameter gets two bars (low side and high side)
rows = []
for _, row in df.iterrows():
low_side = min(row["low_value"], row["high_value"])
high_side = max(row["low_value"], row["high_value"])
low_delta = low_side - base_npv
high_delta = high_side - base_npv
low_nudge = low_delta - 0.25
high_nudge = high_delta + 0.25
rows.append(
{
"parameter": row["parameter"],
"value": low_delta,
"scenario": "Low Scenario",
"label": f"{low_delta:+.1f}",
"npv": f"${low_side:.1f}M",
"label_x": low_nudge,
}
)
rows.append(
{
"parameter": row["parameter"],
"value": high_delta,
"scenario": "High Scenario",
"label": f"{high_delta:+.1f}",
"npv": f"${high_side:.1f}M",
"label_x": high_nudge,
}
)
plot_df = pd.DataFrame(rows)
# Preserve sorted order (ascending range = narrowest at bottom, widest at top)
param_order = df["parameter"].tolist()
plot_df["parameter"] = pd.Categorical(plot_df["parameter"], categories=param_order, ordered=True)
# Title — scale fontsize for 83-char title (default 16, floor 11)
TITLE = "NPV Sensitivity Analysis · bar-tornado-sensitivity · python · letsplot · anyplot.ai"
title_fs = max(11, round(16 * 67 / len(TITLE)))
flavor = flavor_high_contrast_light() if THEME == "light" else flavor_high_contrast_dark()
# Plot
plot = (
ggplot(plot_df, aes(x="value", y="parameter", fill="scenario"))
+ geom_bar(
stat="identity",
width=0.7,
alpha=0.92,
position="identity",
tooltips=layer_tooltips()
.line("@parameter")
.line("Scenario: @scenario")
.line("NPV Impact: @label $M")
.line("Resulting NPV: @npv"),
)
+ geom_vline(xintercept=0, color=INK, size=1.4, linetype="solid")
+ geom_text(
aes(x="label_x", label="label"),
position="identity",
hjust=1.1,
size=4,
color=INK,
fontface="bold",
data=plot_df[plot_df["scenario"] == "Low Scenario"],
)
+ geom_text(
aes(x="label_x", label="label"),
position="identity",
hjust=-0.1,
size=4,
color=INK,
fontface="bold",
data=plot_df[plot_df["scenario"] == "High Scenario"],
)
+ scale_fill_manual(
values=[COLOR_LOW, COLOR_HIGH],
breaks=["Low Scenario", "High Scenario"],
labels=["◀ Low Scenario", "High Scenario ▶"],
)
+ scale_x_continuous(expand=[0.18, 0], format="{.1f}", breaks=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])
+ labs(
x="Change in NPV ($M)",
y="Input Parameter",
title=TITLE,
subtitle="One-at-a-time parameter variation from base case (NPV = $12.5M)",
caption="Revenue Growth and Discount Rate account for over 50% of total NPV sensitivity",
fill="",
)
+ theme_minimal()
+ flavor
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=title_fs, face="bold", hjust=0.5, color=INK),
plot_subtitle=element_text(size=10, hjust=0.5, color=INK_SOFT),
axis_title_x=element_text(size=12, margin=[10, 0, 0, 0], color=INK),
axis_title_y=element_text(size=12, color=INK),
axis_text_x=element_text(size=10, color=INK_SOFT),
axis_text_y=element_text(size=10, color=INK_SOFT),
legend_text=element_text(size=10, color=INK_SOFT),
legend_title=element_blank(),
legend_position="top",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
panel_grid_major_y=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_x=element_line(color=INK_SOFT, size=0.3),
plot_caption=element_text(size=8, color=INK_MUTED, face="italic", hjust=0.5),
plot_margin=[20, 30, 20, 10],
)
+ ggsize(800, 450) # 800 × 450 × scale=4 = 3200 × 1800 px (landscape)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Tornado Diagram for Sensitivity Analysis on anyplot.ai.