A grouped violin plot with individual data points overlaid as swarm points, showing distributions across two categorical dimensions simultaneously. Multiple violin plots are arranged by category on the x-axis and grouped by a secondary variable using color/hue, with swarm points revealing the underlying raw data. This visualization combines distribution shape visualization with complete data transparency, ideal for comparing how distributions differ across multiple factors.
""" anyplot.ai
violin-grouped-swarm: Grouped Violin Plot with Swarm Overlay
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-18
"""
import os
import sys
import time
from pathlib import Path
import numpy as np
from scipy import stats
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
_cwd = os.getcwd()
sys.path = [p for p in sys.path if os.path.abspath(p) != os.path.dirname(__file__)]
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Legend, LegendItem
from bokeh.plotting import figure
sys.path.insert(0, os.path.dirname(__file__))
# 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
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Data - Response times (ms) across 3 task types and 2 expertise levels
np.random.seed(42)
categories = ["Simple", "Moderate", "Complex"]
groups = ["Novice", "Expert"]
# Generate realistic response time data for each combination
data = []
for cat_idx, category in enumerate(categories):
for _grp_idx, group in enumerate(groups):
# Experts faster, complex tasks take longer
base = 200 + cat_idx * 150
expert_adjust = -80 if group == "Expert" else 0
mean = base + expert_adjust
std = 30 + cat_idx * 15
values = np.random.normal(mean, std, 40)
values = np.clip(values, 50, 900)
for val in values:
data.append({"category": category, "group": group, "value": val})
# Color mapping
colors = {group: IMPRINT[i] for i, group in enumerate(groups)}
# Create figure
p = figure(
width=4800,
height=2700,
title="violin-grouped-swarm · Python · bokeh · anyplot.ai",
x_axis_label="Task Type",
y_axis_label="Response Time (ms)",
x_range=[-0.5, 2.5],
y_range=[0, 750],
tools="",
toolbar_location=None,
)
# Theme-adaptive styling
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
# Positioning
cat_positions = {cat: i for i, cat in enumerate(categories)}
group_offsets = {"Novice": -0.2, "Expert": 0.2}
# Store legend items
legend_items = []
# Draw violin shapes and swarm points for each category-group combination
for _grp_idx, group in enumerate(groups):
first_violin = None
for _cat_idx, category in enumerate(categories):
# Get values for this category-group
values = np.array([d["value"] for d in data if d["category"] == category and d["group"] == group])
base_x = cat_positions[category] + group_offsets[group]
# Compute kernel density estimate for violin
kde = stats.gaussian_kde(values)
y_range = np.linspace(values.min() - 15, values.max() + 15, 100)
density = kde(y_range)
# Scale density to reasonable width
max_width = 0.17
density_scaled = density / density.max() * max_width
# Create violin polygon
violin_x = np.concatenate([base_x - density_scaled, (base_x + density_scaled)[::-1]])
violin_y = np.concatenate([y_range, y_range[::-1]])
# Draw violin
v_glyph = p.patch(
violin_x, violin_y, fill_color=colors[group], fill_alpha=0.5, line_color=colors[group], line_width=3
)
if first_violin is None:
first_violin = v_glyph
# Create swarm points - bin values and assign jittered x positions
swarm_x = []
bin_width = 20
value_bins = {}
for val in values:
bin_key = int(val // bin_width)
if bin_key not in value_bins:
value_bins[bin_key] = 0
count = value_bins[bin_key]
# Alternate sides with increasing offset
offset = (count // 2 + 1) * 0.025 * (1 if count % 2 == 0 else -1)
if count == 0:
offset = 0
# Clamp offset within violin width
max_offset = density_scaled[min(int((val - y_range[0]) / (y_range[-1] - y_range[0]) * 99), 99)] * 0.7
offset = np.clip(offset, -max_offset, max_offset)
swarm_x.append(base_x + offset)
value_bins[bin_key] += 1
swarm_source = ColumnDataSource(
data={"x": swarm_x, "y": values, "group": [group] * len(values), "category": [category] * len(values)}
)
# Add hover tool for swarm points
hover = HoverTool(
tooltips=[("Task Type", "@category"), ("Expertise", "@group"), ("Response Time", "@y{0.0f} ms")]
)
p.add_tools(hover)
p.scatter(
"x",
"y",
source=swarm_source,
size=15,
fill_color=colors[group],
fill_alpha=0.75,
line_color="white",
line_width=2,
)
# Add legend item for this group
legend_items.append(LegendItem(label=group, renderers=[first_violin]))
# Custom x-axis with category labels
p.xaxis.ticker = list(range(len(categories)))
p.xaxis.major_label_overrides = dict(enumerate(categories))
# Add legend with larger sizing
legend = Legend(
items=legend_items,
location="top_right",
label_text_font_size="20pt",
label_text_color=INK_SOFT,
glyph_width=50,
glyph_height=50,
spacing=20,
padding=25,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.95,
border_line_color=INK_SOFT,
border_line_width=2,
)
p.add_layout(legend, "right")
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Grouped Violin Plot with Swarm Overlay on anyplot.ai.