A split violin plot displaying two distributions side-by-side within each violin, with each half representing a different group. Unlike standard violin plots that mirror the same distribution, split violins use the left and right halves to compare two conditions (such as before/after, male/female, or control/treatment) at each category level. This enables direct visual comparison of distribution shapes between paired groups.
""" anyplot.ai
violin-split: Split Violin Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 95/100 | Updated: 2026-05-08
"""
import os
import sys
# Remove script directory from path to avoid importing this file as altair
script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if p != script_dir and p != ""]
import altair as alt
import numpy as np
import pandas as pd
# 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 (positions 1-2)
IMPRINT = ["#009E73", "#C475FD"]
# Data: Test scores (%) by department comparing control vs treatment groups
np.random.seed(42)
departments = ["Engineering", "Marketing", "Sales", "HR"]
data = []
for dept in departments:
# Control group - baseline performance
if dept == "Engineering":
control_scores = np.random.normal(72, 12, 100)
treatment_scores = np.random.normal(85, 10, 100) # Larger improvement
elif dept == "Marketing":
control_scores = np.random.normal(68, 15, 100)
treatment_scores = np.random.normal(78, 12, 100)
elif dept == "Sales":
control_scores = np.random.normal(75, 14, 100)
treatment_scores = np.random.normal(80, 11, 100)
else: # HR
control_scores = np.random.normal(70, 10, 100)
treatment_scores = np.random.normal(76, 9, 100)
for score in control_scores:
data.append({"Department": dept, "Score (%)": np.clip(score, 30, 100), "Group": "Control"})
for score in treatment_scores:
data.append({"Department": dept, "Score (%)": np.clip(score, 30, 100), "Group": "Treatment"})
df = pd.DataFrame(data)
# Calculate quartile statistics for inner markers
quartile_data = (
df.groupby(["Department", "Group"])["Score (%)"]
.agg(median="median", q1=lambda x: x.quantile(0.25), q3=lambda x: x.quantile(0.75))
.reset_index()
)
# Merge quartile data back to main dataframe
df_with_quartiles = df.merge(quartile_data, on=["Department", "Group"])
# Create split violin using density transform with xOffset for split
base = alt.Chart().transform_density(
density="Score (%)", as_=["Score (%)", "density"], groupby=["Department", "Group"], extent=[30, 100]
)
# For split violin: Control goes left (negative), Treatment goes right (positive)
split_violin = (
base.transform_calculate(signed_density="datum.Group === 'Control' ? -datum.density : datum.density")
.mark_area(orient="horizontal", opacity=0.75)
.encode(
x=alt.X("signed_density:Q", title=None, axis=alt.Axis(labels=False, ticks=False, domain=False), stack=None),
y=alt.Y("Score (%):Q", title="Score (%)", scale=alt.Scale(domain=[30, 100])),
color=alt.Color(
"Group:N",
scale=alt.Scale(domain=["Control", "Treatment"], range=IMPRINT),
legend=alt.Legend(
title="Group",
titleFontSize=20,
labelFontSize=18,
symbolSize=400,
orient="right",
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
titleColor=INK,
labelColor=INK_SOFT,
),
),
)
)
# IQR rule (vertical line from q1 to q3)
iqr_rule = (
alt.Chart()
.transform_aggregate(q1="min(q1)", q3="max(q3)", groupby=["Department", "Group"])
.mark_rule(size=4, opacity=0.85)
.encode(
y=alt.Y("q1:Q", scale=alt.Scale(domain=[30, 100])),
y2="q3:Q",
xOffset=alt.XOffset("Group:N", scale=alt.Scale(domain=["Control", "Treatment"], range=[-20, 20])),
color=alt.Color("Group:N", scale=alt.Scale(domain=["Control", "Treatment"], range=IMPRINT)),
)
)
# Median point marker (diamond shape for visibility)
median_marker = (
alt.Chart()
.transform_aggregate(median="min(median)", groupby=["Department", "Group"])
.mark_point(size=120, filled=True, shape="diamond", opacity=1)
.encode(
y=alt.Y("median:Q", scale=alt.Scale(domain=[30, 100])),
xOffset=alt.XOffset("Group:N", scale=alt.Scale(domain=["Control", "Treatment"], range=[-20, 20])),
color=alt.value("white"),
stroke=alt.Color("Group:N", scale=alt.Scale(domain=["Control", "Treatment"], range=IMPRINT)),
strokeWidth=alt.value(2),
)
)
# Layer violin, IQR, and median markers with shared data
layered = alt.layer(split_violin, iqr_rule, median_marker, data=df_with_quartiles)
# Facet by department
chart = (
layered.properties(width=320, height=400)
.facet(column=alt.Column("Department:N", title=None, header=alt.Header(labelFontSize=22, labelPadding=15)))
.resolve_scale(x="independent")
.properties(title=alt.Title("violin-split · altair · anyplot.ai", fontSize=28), background=PAGE_BG)
.configure_facet(spacing=50)
.configure_view(stroke=None, fill=PAGE_BG)
.configure_axis(
labelFontSize=18,
titleFontSize=22,
titleColor=INK,
labelColor=INK_SOFT,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.12,
)
.configure_title(anchor="middle", offset=20, color=INK)
)
# Save (in script directory)
output_dir = os.path.dirname(os.path.abspath(__file__))
chart.save(os.path.join(output_dir, f"plot-{THEME}.png"), scale_factor=3.0)
chart.save(os.path.join(output_dir, f"plot-{THEME}.html"))
Part of Split Violin Plot on anyplot.ai.