A grouped box plot displays multiple box plots side-by-side within each category, enabling comparison of distributions across subgroups. Each group contains boxes representing different subcategories or conditions, making it ideal for multi-factor comparisons and A/B testing scenarios with multiple metrics.
""" anyplot.ai
box-grouped: Grouped Box Plot
Library: plotly 6.9.0 | Python 3.13.15
Quality: 91/100 | Updated: 2026-08-18
"""
import os
import numpy as np
import plotly.graph_objects as go
# 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"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Imprint palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Employee performance scores by department and experience level
np.random.seed(42)
categories = ["Sales", "Engineering", "Marketing", "Support"]
subcategories = ["Junior", "Mid-Level", "Senior"]
# Generate realistic performance data with varying distributions
data = {
"Sales": {
"Junior": np.random.normal(65, 12, 50),
"Mid-Level": np.random.normal(75, 10, 50),
"Senior": np.random.normal(85, 8, 50),
},
"Engineering": {
"Junior": np.random.normal(60, 15, 50),
"Mid-Level": np.random.normal(78, 9, 50),
"Senior": np.random.normal(88, 6, 50),
},
"Marketing": {
"Junior": np.random.normal(62, 14, 50),
"Mid-Level": np.random.normal(72, 11, 50),
"Senior": np.random.normal(82, 9, 50),
},
"Support": {
"Junior": np.random.normal(58, 13, 50),
"Mid-Level": np.random.normal(70, 10, 50),
"Senior": np.random.normal(80, 7, 50),
},
}
# Add some outliers for feature coverage
data["Sales"]["Junior"] = np.append(data["Sales"]["Junior"], [35, 95])
data["Engineering"]["Senior"] = np.append(data["Engineering"]["Senior"], [55, 95])
data["Marketing"]["Mid-Level"] = np.append(data["Marketing"]["Mid-Level"], [40, 98])
# Create figure
fig = go.Figure()
# Add box traces for each subcategory
for i, subcat in enumerate(subcategories):
x_vals = []
y_vals = []
for cat in categories:
values = data[cat][subcat]
x_vals.extend([cat] * len(values))
y_vals.extend(values)
fig.add_trace(
go.Box(
x=x_vals,
y=y_vals,
name=subcat,
marker_color=IMPRINT[i],
boxmean="sd",
notched=True,
line={"width": 1.5},
marker={"size": 8, "opacity": 0.85, "line": {"width": 1, "color": PAGE_BG}},
boxpoints="outliers",
jitter=0.4,
pointpos=0,
)
)
# Second storytelling layer — call out the department with the strongest overall performance
dept_medians = {cat: float(np.median(np.concatenate([data[cat][s] for s in subcategories]))) for cat in categories}
best_dept = max(dept_medians, key=dept_medians.get)
fig.add_annotation(
x=best_dept,
xref="x",
y=0.97,
yref="paper",
text=f"★ {best_dept}: strongest overall performance",
showarrow=False,
font={"size": 10, "color": INK},
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
borderpad=4,
)
# Benchmark line for visual hierarchy — flags a company-wide performance target
TARGET_SCORE = 75
fig.add_hline(
y=TARGET_SCORE,
line={"color": INK_SOFT, "width": 1.5, "dash": "dash"},
annotation_text=f"Company target: {TARGET_SCORE}",
annotation_position="top left",
annotation_font={"size": 10, "color": INK_SOFT},
)
# Update layout — canonical 3200x1800 landscape canvas
title_text = "box-grouped · python · plotly · anyplot.ai"
fig.update_layout(
autosize=False,
title={"text": title_text, "font": {"size": 16, "color": INK}, "x": 0.5, "xanchor": "center"},
xaxis={
"title": {"text": "Department", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"showgrid": False,
"showline": False,
"zeroline": False,
},
yaxis={
"title": {"text": "Performance Score (0-100)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"gridwidth": 1,
"showline": False,
"zeroline": False,
},
legend={
"title": {"text": "Experience Level", "font": {"size": 10, "color": INK}},
"font": {"size": 10, "color": INK_SOFT},
"bgcolor": ELEVATED_BG,
"borderwidth": 0,
"x": 1.02,
"y": 1,
"xanchor": "left",
"yanchor": "top",
},
boxmode="group",
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK},
margin={"l": 80, "r": 140, "t": 90, "b": 70},
)
# Save as PNG and HTML — hard target: 3200x1800 (landscape)
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/box-grouped/plotly/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "box-grouped",
"language": "python",
"library": "plotly",
"page": "https://anyplot.ai/box-grouped/python/plotly",
"hub": "https://anyplot.ai/box-grouped",
"code_json": "https://api.anyplot.ai/specs/box-grouped/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/box-grouped",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/plotly/plot-dark.html",
"quality_score": 91.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Grouped Box Plot on anyplot.ai.