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: plotnine 0.15.4 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-08
"""
import os
import sys
# Remove the script's directory from sys.path to avoid circular imports
script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != script_dir]
import math
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_polygon,
geom_text,
ggplot,
labs,
scale_fill_identity,
scale_x_continuous,
scale_y_continuous,
theme,
)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BORDER_COLOR = "#E8E6DC" if THEME == "light" else "#353531"
# Data - Budget allocation: departments (inner) and expense categories (outer)
# First parent uses Okabe-Ito position 1; others use positions 2-4
data = {
"Engineering": [("Salaries", 450), ("Equipment", 120), ("Training", 80)],
"Marketing": [("Advertising", 280), ("Events", 95), ("Content", 75)],
"Operations": [("Facilities", 180), ("IT Infrastructure", 150), ("Utilities", 70)],
"Sales": [("Commissions", 220), ("Travel", 110), ("Tools", 50)],
}
# imprint families - parent + two lighter tonal shades for the outer ring
color_families = {
"Engineering": ("#009E73", ["#009E73", "#2DAE89", "#5BBFA0"]), # imprint green
"Marketing": ("#C475FD", ["#C475FD", "#D195FE", "#DEB5FE"]), # imprint lavender
"Operations": ("#4467A3", ["#4467A3", "#6883B6", "#8C9FC9"]), # imprint blue
"Sales": ("#BD8233", ["#BD8233", "#CC9852", "#DBAE71"]), # imprint ochre
}
# Calculate totals for each parent
parent_totals = {parent: sum(v for _, v in children) for parent, children in data.items()}
grand_total = sum(parent_totals.values())
# Ring dimensions
inner_ring_inner = 60 # Inner donut hole
inner_ring_outer = 100 # Inner ring outer radius
outer_ring_inner = 110 # Outer ring inner radius (gap for separation)
outer_ring_outer = 150 # Outer ring outer radius
def create_annular_segment(start_angle, end_angle, inner_radius, outer_radius, n_points=50):
"""Create polygon points for an annular (donut) segment."""
# Add small gap between segments
gap = 0.02
start_angle += gap
end_angle -= gap
points = []
# Inner arc (from start to end)
inner_angles = np.linspace(start_angle, end_angle, n_points)
for angle in inner_angles:
points.append((inner_radius * math.cos(angle), inner_radius * math.sin(angle)))
# Outer arc (from end back to start)
outer_angles = np.linspace(end_angle, start_angle, n_points)
for angle in outer_angles:
points.append((outer_radius * math.cos(angle), outer_radius * math.sin(angle)))
# Close the polygon
points.append(points[0])
return points
# Build polygon data for inner ring (parents)
inner_rows = []
current_angle = math.pi / 2 # Start at top (12 o'clock)
parent_angles = {} # Track start/end angles for each parent
for parent in data.keys():
parent_total = parent_totals[parent]
sweep = (parent_total / grand_total) * 2 * math.pi
end_angle = current_angle - sweep # Clockwise
parent_angles[parent] = (current_angle, end_angle)
# Create segment polygon
points = create_annular_segment(end_angle, current_angle, inner_ring_inner, inner_ring_outer)
for order, (x, y) in enumerate(points):
inner_rows.append({"x": x, "y": y, "segment": parent, "order": order, "fill": color_families[parent][0]})
current_angle = end_angle
inner_df = pd.DataFrame(inner_rows)
# Build polygon data for outer ring (children)
outer_rows = []
for parent, children in data.items():
parent_start, parent_end = parent_angles[parent]
parent_total = parent_totals[parent]
child_current_angle = parent_start
child_colors = color_families[parent][1]
for i, (child_name, child_value) in enumerate(children):
child_sweep = (child_value / parent_total) * (parent_start - parent_end)
child_end_angle = child_current_angle - child_sweep
# Create segment polygon
points = create_annular_segment(child_end_angle, child_current_angle, outer_ring_inner, outer_ring_outer)
segment_id = f"{parent}_{child_name}"
color = child_colors[i % len(child_colors)]
for order, (x, y) in enumerate(points):
outer_rows.append({"x": x, "y": y, "segment": segment_id, "order": order, "fill": color})
child_current_angle = child_end_angle
outer_df = pd.DataFrame(outer_rows)
# Create labels for inner ring (parent names)
inner_labels = []
for parent in data.keys():
start_angle, end_angle = parent_angles[parent]
mid_angle = (start_angle + end_angle) / 2
label_radius = (inner_ring_inner + inner_ring_outer) / 2
inner_labels.append(
{
"x": label_radius * math.cos(mid_angle),
"y": label_radius * math.sin(mid_angle),
"label": parent,
"value": f"${parent_totals[parent]:,}K",
}
)
inner_label_df = pd.DataFrame(inner_labels)
# Create labels for outer ring (larger segments only)
outer_labels = []
for parent, children in data.items():
parent_start, parent_end = parent_angles[parent]
parent_total = parent_totals[parent]
child_current_angle = parent_start
for child_name, child_value in children:
child_sweep = (child_value / parent_total) * (parent_start - parent_end)
child_end_angle = child_current_angle - child_sweep
# Only label segments that are large enough
if child_value >= 80: # Threshold for labeling
mid_angle = (child_current_angle + child_end_angle) / 2
label_radius = (outer_ring_inner + outer_ring_outer) / 2
outer_labels.append(
{"x": label_radius * math.cos(mid_angle), "y": label_radius * math.sin(mid_angle), "label": child_name}
)
child_current_angle = child_end_angle
outer_label_df = pd.DataFrame(outer_labels)
# Plot
plot = (
ggplot()
# Inner ring (parents)
+ geom_polygon(
aes(x="x", y="y", group="segment", fill="fill"), data=inner_df, color=BORDER_COLOR, size=0.5, alpha=0.95
)
# Outer ring (children)
+ geom_polygon(
aes(x="x", y="y", group="segment", fill="fill"), data=outer_df, color=BORDER_COLOR, size=0.5, alpha=0.9
)
# Inner ring labels (parent names)
+ geom_text(aes(x="x", y="y", label="label"), data=inner_label_df, size=11, fontweight="bold", color=INK)
# Outer ring labels (child names for large segments)
+ geom_text(aes(x="x", y="y", label="label"), data=outer_label_df, size=11, color=INK_SOFT)
# Use fill colors directly
+ scale_fill_identity()
# Fixed aspect ratio for proper circles
+ coord_fixed(ratio=1)
# Axis limits with padding
+ scale_x_continuous(limits=(-180, 180))
+ scale_y_continuous(limits=(-180, 180))
# Title
+ labs(title="Budget Allocation by Department · donut-nested · plotnine · pyplots.ai")
# Clean theme with adaptive background
+ theme(
figure_size=(12, 12),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_title=element_text(size=22, ha="center", color=INK),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
legend_position="none",
)
)
# Save
output_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"plot-{THEME}.png")
plot.save(output_path, dpi=300)
Part of Nested Donut Chart on anyplot.ai.