A pie chart showing proportions of categorical data as slices of a circle. Each slice represents a category's contribution to the whole, with the angle proportional to its value. Essential for visualizing parts-to-whole relationships in a simple, intuitive format.
""" anyplot.ai
pie-basic: Basic Pie Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 81/100 | Created: 2026-05-28
"""
import os
import sys
# Remove this file's directory from sys.path to prevent self-import
# (this file is named plotnine.py, same as the library being imported)
_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _dir]
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_equal,
element_rect,
element_text,
geom_polygon,
geom_text,
ggplot,
labs,
scale_fill_manual,
theme,
theme_void,
)
# 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"
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
# Data — annual R&D budget allocation by department
departments = ["R&D", "Marketing", "Operations", "Human Resources", "IT Infrastructure", "Administration"]
budget_shares = [32, 22, 18, 13, 11, 4]
total = sum(budget_shares)
# Build pie wedge polygons starting at 12 o'clock, going clockwise
N_ARC = 80
EXPLODE_OFFSET = 0.07 # slight offset for the largest slice
wedge_rows = []
label_rows = []
cumsum = 0
for cat, val in zip(departments, budget_shares, strict=False):
pct = val / total
# Clockwise from 12 o'clock: start at π/2, decrease by pct * 2π
start_angle = np.pi / 2 - 2 * np.pi * cumsum
end_angle = np.pi / 2 - 2 * np.pi * (cumsum + pct)
mid_angle = (start_angle + end_angle) / 2
# Explode largest slice (R&D) outward along its bisector
offset = EXPLODE_OFFSET if cat == "R&D" else 0.0
dx, dy = offset * np.cos(mid_angle), offset * np.sin(mid_angle)
# Wedge vertices: center → arc → (auto-closed by geom_polygon)
angles = np.linspace(start_angle, end_angle, N_ARC)
xs = np.concatenate([[0.0], np.cos(angles)]) + dx
ys = np.concatenate([[0.0], np.sin(angles)]) + dy
for xi, yi in zip(xs, ys, strict=False):
wedge_rows.append({"x": xi, "y": yi, "category": cat})
# Percentage label at 62% of radius, only show if slice >= 5%
if pct >= 0.05:
label_r = 0.62 + offset
label_rows.append(
{
"x": label_r * np.cos(mid_angle) + dx,
"y": label_r * np.sin(mid_angle) + dy,
"label": f"{round(pct * 100)}%",
}
)
cumsum += pct
df_wedges = pd.DataFrame(wedge_rows)
df_wedges["category"] = pd.Categorical(df_wedges["category"], categories=departments, ordered=True)
df_labels = pd.DataFrame(label_rows)
title = "pie-basic · python · plotnine · anyplot.ai"
# Plot
plot = (
ggplot()
+ geom_polygon(data=df_wedges, mapping=aes(x="x", y="y", fill="category", group="category"))
+ geom_text(data=df_labels, mapping=aes(x="x", y="y", label="label"), size=4.0, color="white")
+ scale_fill_manual(values=IMPRINT_PALETTE)
+ coord_equal()
+ labs(title=title, fill="Department")
+ theme_void()
+ theme(
figure_size=(6, 6),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=12, color=INK, ha="center"),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=10, color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_key=element_rect(fill=ELEVATED_BG, color=ELEVATED_BG),
)
)
# Draw and save — use subplots_adjust to give the right-side legend breathing room
fig = plot.draw()
fig.subplots_adjust(right=0.85)
fig.savefig(f"plot-{THEME}.png", dpi=400)
plt.close(fig)
Part of Basic Pie Chart on anyplot.ai.