A bullet chart displays a single measure against qualitative ranges and a target marker, designed by Stephen Few as a space-efficient alternative to gauge charts. The linear format shows actual performance as a bar, a target as a vertical marker, and background bands representing qualitative ranges (poor/satisfactory/good). Its compact design allows multiple bullet charts to fit on a single dashboard for easy comparison across metrics.
""" anyplot.ai
bullet-basic: Basic Bullet Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-29
"""
import os
import pandas as pd
from plotnine import (
aes,
annotate,
element_blank,
element_line,
element_rect,
element_text,
geom_rect,
geom_segment,
geom_text,
geom_tile,
ggplot,
guides,
labs,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# Theme-adaptive chrome tokens
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 — brand green is always first series
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
BRAND = IMPRINT_PALETTE[0] # #009E73 — actual value bar
# Grayscale bands: darker = worse performance zone (theme-adaptive for readability)
if THEME == "light":
band_colors = {"Poor": "#686868", "Satisfactory": "#9E9E9E", "Good": "#C8C8C8"}
else:
band_colors = {"Poor": "#383838", "Satisfactory": "#555555", "Good": "#727272"}
# Data — four business KPIs with mixed above/below-target performance
metrics = [
{"label": "Revenue ($K)", "actual": 275, "target": 250, "ranges": [150, 225, 300]},
{"label": "Profit (%)", "actual": 22, "target": 26, "ranges": [15, 22.5, 30]},
{"label": "New Orders", "actual": 1050, "target": 1100, "ranges": [600, 900, 1200]},
{"label": "Satisfaction", "actual": 4.5, "target": 4.2, "ranges": [2.5, 3.5, 5.0]},
]
# Build dataframes — all values normalized to 0–100 scale for aligned x-axis
tile_data = []
actual_data = []
target_data = []
range_height = 0.68
actual_height = 0.28
for i, m in enumerate(metrics):
y_pos = len(metrics) - 1 - i # first metric at top
max_val = m["ranges"][-1]
band_bounds = [0, (m["ranges"][0] / max_val) * 100, (m["ranges"][1] / max_val) * 100, 100]
band_names = ["Poor", "Satisfactory", "Good"]
for j, band in enumerate(band_names):
x_center = (band_bounds[j] + band_bounds[j + 1]) / 2
width = band_bounds[j + 1] - band_bounds[j]
tile_data.append({"y": y_pos, "x": x_center, "width": width, "band": band})
actual_pct = (m["actual"] / max_val) * 100
val = m["actual"]
val_str = str(int(val)) if val == int(val) else str(val)
actual_data.append(
{
"y": y_pos,
"xmin": 0,
"xmax": actual_pct,
"ymin": y_pos - actual_height / 2,
"ymax": y_pos + actual_height / 2,
"label": m["label"],
"actual": val_str,
"label_y": y_pos + range_height / 2 + 0.04,
}
)
target_pct = (m["target"] / max_val) * 100
target_data.append(
{
"y": y_pos,
"target": target_pct,
"seg_ymin": y_pos - range_height / 2.2,
"seg_ymax": y_pos + range_height / 2.2,
}
)
df_tiles = pd.DataFrame(tile_data)
df_actual = pd.DataFrame(actual_data)
df_target = pd.DataFrame(target_data)
# Band label x positions: 3 of 4 metrics share 50%/75% band boundaries
poor_mid = 25.0
satis_mid = 62.5
good_mid = 87.5
plot = (
ggplot()
# Qualitative range bands
+ geom_tile(df_tiles, aes(x="x", y="y", width="width", fill="band"), height=range_height, color="none")
+ scale_fill_manual(values=band_colors, limits=["Good", "Satisfactory", "Poor"])
+ guides(fill=False)
# Actual value bar
+ geom_rect(df_actual, aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"), fill=BRAND, color="none")
# Target marker — thin contrasting line perpendicular to the bar
+ geom_segment(df_target, aes(x="target", xend="target", y="seg_ymin", yend="seg_ymax"), color=INK, size=2.0)
# Actual value labels (geom_text size in mm; ~3.5 ≈ 10pt at 400dpi)
+ geom_text(
df_actual,
aes(x="xmax", y="label_y", label="actual"),
ha="right",
va="bottom",
size=3.5,
color=BRAND,
fontweight="bold",
)
# Band zone labels below the bottom metric
+ annotate("text", x=poor_mid, y=-0.5, label="Poor", size=3.4, color=INK_MUTED, va="top")
+ annotate("text", x=satis_mid, y=-0.5, label="Satisfactory", size=3.4, color=INK_MUTED, va="top")
+ annotate("text", x=good_mid, y=-0.5, label="Good", size=3.4, color=INK_MUTED, va="top")
# Scales
+ scale_x_continuous(limits=(0, 100), breaks=[0, 25, 50, 75, 100], expand=(0, 0.02))
+ scale_y_continuous(
breaks=list(range(len(metrics))), labels=[m["label"] for m in reversed(metrics)], expand=(0.08, 0.08)
)
+ labs(title="bullet-basic · python · plotnine · anyplot.ai", x="Performance (%)", y="")
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
plot_title=element_text(size=12, ha="center", weight="bold", color=INK),
axis_title_x=element_text(size=10, color=INK),
axis_title_y=element_blank(),
axis_text_x=element_text(size=8, color=INK_SOFT),
axis_text_y=element_text(size=9, ha="right", color=INK_SOFT),
panel_grid_major_y=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_x=element_line(color=INK, size=0.3, alpha=0.15),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color="none"),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT),
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Basic Bullet Chart on anyplot.ai.