A nested donut chart displays hierarchical data as multiple concentric rings, where each ring represents a level of the hierarchy. Inner rings show parent categories while outer rings show their subdivisions. This visualization effectively reveals part-to-whole relationships across multiple levels while maintaining the familiar donut format.
""" anyplot.ai
donut-nested: Nested Donut Chart
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-08
"""
import math
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_polygon,
geom_text,
ggplot,
ggsize,
labs,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
# Theme 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"
# Okabe-Ito palette (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Data - Budget allocation by department (inner) and expense categories (outer)
data = [
# Marketing department
{"level_1": "Marketing", "level_2": "Advertising", "value": 18},
{"level_1": "Marketing", "level_2": "Events", "value": 8},
{"level_1": "Marketing", "level_2": "Content", "value": 6},
# Operations department
{"level_1": "Operations", "level_2": "Facilities", "value": 12},
{"level_1": "Operations", "level_2": "IT Support", "value": 10},
{"level_1": "Operations", "level_2": "Logistics", "value": 8},
# Research & Development
{"level_1": "R&D", "level_2": "Product Dev", "value": 15},
{"level_1": "R&D", "level_2": "Research", "value": 10},
# Sales department
{"level_1": "Sales", "level_2": "Field Sales", "value": 9},
{"level_1": "Sales", "level_2": "Inside Sales", "value": 4},
]
df = pd.DataFrame(data)
total_value = df["value"].sum()
# Ring radii
r_inner_1, r_outer_1 = 25, 50
r_inner_2, r_outer_2 = 55, 85
# Calculate percentages and aggregations
level1_agg = df.groupby("level_1")["value"].sum().reset_index()
level1_agg["pct"] = level1_agg["value"] / total_value
level1_order = ["Marketing", "Operations", "R&D", "Sales"]
level1_agg["level_1"] = pd.Categorical(level1_agg["level_1"], categories=level1_order, ordered=True)
level1_agg = level1_agg.sort_values("level_1").reset_index(drop=True)
level2_agg = df.groupby(["level_1", "level_2"])["value"].sum().reset_index()
level2_agg["pct"] = level2_agg["value"] / total_value
# Map categories to colors
color_map_l1 = {cat: IMPRINT[i] for i, cat in enumerate(level1_order)}
color_map_l2 = {}
for l1_cat in level1_order:
children = level2_agg[level2_agg["level_1"] == l1_cat]["level_2"].unique()
parent_color = color_map_l1[l1_cat]
for child in children:
color_map_l2[child] = parent_color
# Build polygon data for segments
polygon_rows = []
label_rows = []
segment_id = 0
level1_angles = {}
start_angle = math.pi / 2
# Inner ring (level 1)
for _, row in level1_agg.iterrows():
end_angle = start_angle - row["pct"] * 2 * math.pi
level1_angles[row["level_1"]] = {"start": start_angle, "end": end_angle}
# Create polygon points for this wedge
angles_outer = [start_angle + (end_angle - start_angle) * i / 40 for i in range(41)]
angles_inner = angles_outer[::-1]
x_outer = [r_outer_1 * math.cos(a) for a in angles_outer]
y_outer = [r_outer_1 * math.sin(a) for a in angles_outer]
x_inner = [r_inner_1 * math.cos(a) for a in angles_inner]
y_inner = [r_inner_1 * math.sin(a) for a in angles_inner]
x_pts, y_pts = x_outer + x_inner, y_outer + y_inner
for x, y in zip(x_pts, y_pts, strict=False):
polygon_rows.append(
{"x": x, "y": y, "segment_id": segment_id, "level": 1, "label": row["level_1"], "color": row["level_1"]}
)
# Label for inner ring
mid_angle = (start_angle + end_angle) / 2
label_r = (r_inner_1 + r_outer_1) / 2
label_rows.append(
{
"x": label_r * math.cos(mid_angle),
"y": label_r * math.sin(mid_angle),
"label": row["level_1"],
"level": 1,
"pct": row["pct"] * 100,
}
)
segment_id += 1
start_angle = end_angle
# Outer ring (level 2)
for level1_name in level1_order:
if level1_name not in level1_angles:
continue
l1_angles = level1_angles[level1_name]
l2_data = level2_agg[level2_agg["level_1"] == level1_name].sort_values("level_2")
cur_angle = l1_angles["start"]
for _, row in l2_data.iterrows():
end_angle = cur_angle - row["pct"] * 2 * math.pi
# Create polygon points for this wedge
angles_outer = [cur_angle + (end_angle - cur_angle) * i / 40 for i in range(41)]
angles_inner = angles_outer[::-1]
x_outer = [r_outer_2 * math.cos(a) for a in angles_outer]
y_outer = [r_outer_2 * math.sin(a) for a in angles_outer]
x_inner = [r_inner_2 * math.cos(a) for a in angles_inner]
y_inner = [r_inner_2 * math.sin(a) for a in angles_inner]
x_pts, y_pts = x_outer + x_inner, y_outer + y_inner
for x, y in zip(x_pts, y_pts, strict=False):
polygon_rows.append(
{"x": x, "y": y, "segment_id": segment_id, "level": 2, "label": row["level_2"], "color": row["level_2"]}
)
# Label for outer ring with percentage
mid_angle = (cur_angle + end_angle) / 2
label_r = (r_inner_2 + r_outer_2) / 2
label_rows.append(
{
"x": label_r * math.cos(mid_angle),
"y": label_r * math.sin(mid_angle),
"label": row["level_2"],
"level": 2,
"pct": row["pct"] * 100,
}
)
segment_id += 1
cur_angle = end_angle
polygon_df = pd.DataFrame(polygon_rows)
label_df = pd.DataFrame(label_rows)
# Build unified color mapping
all_colors = {}
all_colors.update(color_map_l1)
all_colors.update(color_map_l2)
unique_colors = polygon_df["color"].unique()
color_values = [all_colors.get(c, "#888888") for c in unique_colors]
# Center label
center_df = pd.DataFrame({"x": [0.0], "y": [0.0], "label": [f"Total\n${total_value}M"]})
# Theme configuration
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=28, color=INK, hjust=0.5),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid=element_blank(),
legend_position="none",
)
# Plot
plot = (
ggplot(polygon_df)
+ geom_polygon(aes(x="x", y="y", fill="color", group="segment_id"), color=PAGE_BG, size=2.0, alpha=0.95)
# Inner ring labels (parent categories)
+ geom_text(
aes(x="x", y="y", label="label"), data=label_df[label_df["level"] == 1], size=12, color=INK, fontface="bold"
)
# Outer ring labels (child categories)
+ geom_text(aes(x="x", y="y", label="label"), data=label_df[label_df["level"] == 2], size=10, color=INK_SOFT)
# Center label
+ geom_text(aes(x="x", y="y", label="label"), data=center_df, size=16, color=INK, fontface="bold")
+ scale_fill_manual(values=color_values)
+ coord_fixed(ratio=1)
+ scale_x_continuous(limits=(-110, 110))
+ scale_y_continuous(limits=(-110, 110))
+ labs(title="donut-nested · letsplot · anyplot.ai")
+ ggsize(1200, 1200)
+ anyplot_theme
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Nested Donut Chart on anyplot.ai.