A violin plot with individual data points overlaid as a swarm plot, combining smooth kernel density estimation with raw data visibility. The violin shape shows the distribution density while the swarm points reveal actual observations, enabling viewers to see both the overall distribution pattern and individual data values simultaneously. This hybrid approach provides maximum transparency, showing exactly how many observations exist at each level while maintaining the smooth distribution visualization.
""" anyplot.ai
violin-swarm: Violin Plot with Overlaid Swarm Points
Library: pygal 3.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""
import os
import numpy as np
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_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Okabe-Ito palette - first series is always #009E73
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")
# Data - Reaction times (ms) across 4 experimental conditions
np.random.seed(42)
data = {
"Control": np.random.normal(320, 60, 50),
"Treatment A": np.random.normal(280, 45, 50),
"Treatment B": np.random.normal(250, 55, 50),
"Treatment C": np.random.normal(290, 70, 50),
}
# Clip to realistic range (100-600ms)
for key in data:
data[key] = np.clip(data[key], 100, 600)
# Custom style for theme-adaptive rendering
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,
opacity=0.35,
stroke_width=3,
)
# Create XY chart for violin plot with swarm overlay
chart = pygal.XY(
width=4800,
height=2700,
style=custom_style,
title="violin-swarm · Python · pygal · anyplot.ai",
x_title="Experimental Condition",
y_title="Reaction Time (ms)",
show_legend=False,
stroke=True,
fill=True,
dots_size=0,
show_x_guides=False,
show_y_guides=True,
range=(50, 550),
xrange=(0, 6),
margin=80,
)
# Parameters for violin shapes
violin_width = 0.4
n_points = 80
# Process each category
all_violins = []
all_swarms = []
for i, (_category, values) in enumerate(data.items()):
center_x = i + 1.5
# Create range of y values for density
y_min, y_max = values.min(), values.max()
padding = (y_max - y_min) * 0.2
y_range = np.linspace(y_min - padding, y_max + padding, n_points)
# 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)
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=False)]
right_points = [(center_x + d, y) for y, d in zip(y_range[::-1], density[::-1], strict=False)]
violin_points = left_points + right_points + [left_points[0]]
all_violins.append(violin_points)
# Compute swarm positions - arrange points to minimize overlap
sorted_indices = np.argsort(values)
positions = np.zeros(len(values))
# Bin values and offset within bins
y_sorted = values[sorted_indices]
y_range_data = y_sorted.max() - y_sorted.min()
bin_height = y_range_data / 12 if y_range_data > 0 else 1
current_bin = []
current_bin_y = y_sorted[0] if len(y_sorted) > 0 else 0
for idx, y in enumerate(y_sorted):
if y - current_bin_y > bin_height:
# Process current bin - spread points horizontally
n_in_bin = len(current_bin)
if n_in_bin > 0:
if n_in_bin > 1:
offsets = np.linspace(-violin_width / 2.5, violin_width / 2.5, n_in_bin)
else:
offsets = [0]
for j, bin_idx in enumerate(current_bin):
positions[bin_idx] = center_x + offsets[j]
current_bin = [sorted_indices[idx]]
current_bin_y = y
else:
current_bin.append(sorted_indices[idx])
# Process last bin
n_in_bin = len(current_bin)
if n_in_bin > 0:
if n_in_bin > 1:
offsets = np.linspace(-violin_width / 2.5, violin_width / 2.5, n_in_bin)
else:
offsets = [0]
for j, bin_idx in enumerate(current_bin):
positions[bin_idx] = center_x + offsets[j]
swarm_points = list(zip(positions, values, strict=False))
all_swarms.extend(swarm_points)
# Add violins first (filled, semi-transparent)
for violin in all_violins:
chart.add(None, violin, show_dots=False)
# Add swarm points as chunked series with visible dots
chunk_size = 8
swarm_chunks = [all_swarms[i : i + chunk_size] for i in range(0, len(all_swarms), chunk_size)]
for chunk in swarm_chunks:
chart.add(None, chunk, stroke=False, fill=False, show_dots=True, dots_size=12)
# X-axis labels at violin positions
chart.x_labels = [
{"value": 0, "label": ""},
{"value": 1.5, "label": "Control"},
{"value": 2.5, "label": "Treatment A"},
{"value": 3.5, "label": "Treatment B"},
{"value": 4.5, "label": "Treatment C"},
{"value": 6, "label": ""},
]
# Save outputs with theme-suffixed filenames
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")
Part of Violin Plot with Overlaid Swarm Points on anyplot.ai.