A violin plot with an embedded box plot inside, combining the distribution shape visualization (KDE) with traditional quartile statistics. Shows both the probability density and summary statistics in one plot.
""" anyplot.ai
violin-box: Violin Plot with Embedded Box Plot
Library: pygal 3.1.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-12
"""
import os
import sys
import numpy as np
sys.path = [p for p in sys.path if not p.endswith("/python")]
import pygal
from pygal.style import Style
# Theme tokens
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Okabe-Ito palette - use first 4 colors for violin categories
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233")
# Data - Generate distributions for different categories with scores constrained to 0-100
np.random.seed(42)
raw_data = {
"Engineering": np.random.normal(75, 10, 200),
"Marketing": np.random.normal(62, 12, 200),
"Sales": np.random.normal(68, 14, 200),
"Operations": np.random.normal(55, 8, 200),
}
# Clip all values to 0-100 range
data = {k: np.clip(v, 0, 100) for k, v in raw_data.items()}
# Custom style for 4800x2700 px canvas
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=IMPRINT,
title_font_size=28,
label_font_size=22,
major_label_font_size=18,
legend_font_size=16,
value_font_size=14,
stroke_width=3,
)
# Create XY chart for violin plot with embedded box
chart = pygal.XY(
width=4800,
height=2700,
style=custom_style,
title="violin-box · pygal · anyplot.ai",
x_title="Department",
y_title="Performance Score (0-100)",
show_legend=False,
stroke=True,
fill=True,
dots_size=0,
show_x_guides=False,
show_y_guides=True,
range=(0, 105),
xrange=(0, 6),
margin=80,
)
# Parameters for violin shapes
violin_width = 0.35
n_points = 100
# Box plot styling
box_stroke_style = {"width": 4, "dasharray": ""}
median_stroke_style = {"width": 6, "dasharray": ""}
whisker_stroke_style = {"width": 3, "dasharray": ""}
# Add violins with embedded box plots for each category
for i, (category, values) in enumerate(data.items()):
center_x = i + 1.5
violin_color = IMPRINT[i]
# Compute KDE using Silverman's rule
n = len(values)
std = np.std(values)
iqr = np.percentile(values, 75) - np.percentile(values, 25)
bandwidth = 0.9 * min(std, iqr / 1.34) * n ** (-0.2)
# Create range of y values for density
y_min, y_max = values.min(), values.max()
y_range = np.linspace(max(0, y_min - 5), min(100, y_max + 5), n_points)
# Gaussian kernel density estimation
density = np.zeros_like(y_range)
for v in values:
density += np.exp(-0.5 * ((y_range - v) / bandwidth) ** 2)
density /= n * bandwidth * np.sqrt(2 * np.pi)
# Normalize density to desired width
density = density / density.max() * violin_width
# Create violin shape (mirrored density)
left_points = [(center_x - d, y) for y, d in zip(y_range, density, strict=True)]
right_points = [(center_x + d, y) for y, d in zip(y_range[::-1], density[::-1], strict=True)]
violin_points = left_points + right_points + [left_points[0]]
chart.add(category, violin_points)
# Calculate box plot statistics
median = float(np.median(values))
q1 = float(np.percentile(values, 25))
q3 = float(np.percentile(values, 75))
iqr_val = q3 - q1
# Whiskers: 1.5 * IQR or data min/max
lower_whisker = max(values.min(), q1 - 1.5 * iqr_val)
upper_whisker = min(values.max(), q3 + 1.5 * iqr_val)
# Identify outliers
outliers = values[(values < lower_whisker) | (values > upper_whisker)]
box_width = 0.10
# Quartile box - use elevated background color for visibility
elevated_bg = "#FFFDF6" if THEME == "light" else "#242420"
quartile_box = [
(center_x - box_width, q1),
(center_x - box_width, q3),
(center_x + box_width, q3),
(center_x + box_width, q1),
(center_x - box_width, q1),
]
chart.add(None, quartile_box, stroke=True, fill=True, show_dots=False, stroke_style=box_stroke_style)
# Whisker lines (vertical lines from box to whisker ends)
lower_whisker_line = [(center_x, q1), (center_x, lower_whisker)]
upper_whisker_line = [(center_x, q3), (center_x, upper_whisker)]
chart.add(None, lower_whisker_line, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
chart.add(None, upper_whisker_line, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
# Whisker caps (horizontal lines at ends)
cap_width = box_width * 0.8
lower_cap = [(center_x - cap_width, lower_whisker), (center_x + cap_width, lower_whisker)]
upper_cap = [(center_x - cap_width, upper_whisker), (center_x + cap_width, upper_whisker)]
chart.add(None, lower_cap, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
chart.add(None, upper_cap, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
# Median line (thicker, contrasting)
median_line = [(center_x - box_width * 1.2, median), (center_x + box_width * 1.2, median)]
chart.add(None, median_line, stroke=True, fill=False, show_dots=False, stroke_style=median_stroke_style)
# Outliers as points
if len(outliers) > 0:
outlier_points = [(center_x, float(o)) for o in outliers]
chart.add(None, outlier_points, stroke=False, fill=False, show_dots=True, dots_size=18)
# X-axis labels at violin positions
chart.x_labels = ["", "Engineering", "Marketing", "Sales", "Operations", ""]
chart.x_labels_major_count = 4
# Save outputs
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")
Part of Violin Plot with Embedded Box Plot on anyplot.ai.