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: matplotlib 3.11.1 | Python 3.13.15
Quality: 92/100 | Updated: 2026-08-18
"""
import os
import matplotlib.pyplot as plt
import numpy as np
# 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"
# Imprint palette positions 1-3 (three subcategories)
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3"]
# Data - Employee performance scores across departments and experience levels
np.random.seed(42)
categories = ["Sales", "Engineering", "Marketing", "Support"]
subcategories = ["Junior", "Mid-Level", "Senior"]
# Generate realistic performance data with varying distributions per department
data = {}
for cat_idx, cat in enumerate(categories):
data[cat] = {}
for sub_idx, sub in enumerate(subcategories):
# Vary base performance by department (Sales lower, Support higher)
dept_offset = cat_idx * 5
base = 55 + sub_idx * 12 + dept_offset
variance = 15 - sub_idx * 3
n_points = np.random.randint(30, 60)
scores = np.random.normal(base, variance, n_points)
# Add outliers to some groups
if np.random.random() > 0.6:
outliers = np.random.choice([base - 25, base + 25], size=np.random.randint(1, 3))
scores = np.concatenate([scores, outliers])
data[cat][sub] = np.clip(scores, 0, 100)
# Order departments by overall median score, best to worst, for a clearer narrative
categories = sorted(categories, key=lambda cat: -np.median(np.concatenate(list(data[cat].values()))))
# Create plot
title = "box-grouped · python · matplotlib · anyplot.ai"
title_fontsize = round(12 * 67 / len(title)) if len(title) > 67 else 12
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Calculate positions for grouped boxes
n_categories = len(categories)
n_subcategories = len(subcategories)
box_width = 0.25
group_gap = 0.4
# Plot boxes for each subcategory, marking the mean alongside the median
for sub_idx, sub in enumerate(subcategories):
positions = []
box_data = []
for cat_idx, cat in enumerate(categories):
pos = cat_idx * (n_subcategories * box_width + group_gap) + sub_idx * box_width
positions.append(pos)
box_data.append(data[cat][sub])
bp = ax.boxplot(
box_data,
positions=positions,
widths=box_width * 0.8,
patch_artist=True,
showfliers=True,
showmeans=True,
flierprops={"marker": "o", "markerfacecolor": IMPRINT_PALETTE[sub_idx], "markersize": 6.5, "alpha": 0.7},
medianprops={"color": INK, "linewidth": 1.5},
meanprops={
"marker": "D",
"markerfacecolor": PAGE_BG,
"markeredgecolor": INK,
"markersize": 7,
"markeredgewidth": 1,
},
whiskerprops={"color": INK_SOFT, "linewidth": 1},
capprops={"color": INK_SOFT, "linewidth": 1},
boxprops={"linewidth": 1},
)
# Color the boxes with the Imprint palette
for patch in bp["boxes"]:
patch.set_facecolor(IMPRINT_PALETTE[sub_idx])
patch.set_alpha(0.85)
patch.set_edgecolor(INK_SOFT)
# Set x-axis tick positions and labels
center_positions = [
cat_idx * (n_subcategories * box_width + group_gap) + (n_subcategories - 1) * box_width / 2
for cat_idx in range(n_categories)
]
ax.set_xticks(center_positions)
ax.set_xticklabels(categories, fontsize=8, color=INK)
# Sharpen the best-to-worst narrative: bold the top-performing department as a focal point
ax.get_xticklabels()[0].set_fontweight("bold")
# Labels and title
ax.set_xlabel("Department", fontsize=10, color=INK)
ax.set_ylabel("Performance Score (0-100)", fontsize=10, color=INK)
ax.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK)
# Tick params
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT, labelcolor=INK_SOFT)
# Legend (diamond marker explains the mean; box color explains experience level)
legend_patches = [
plt.Rectangle((0, 0), 1, 1, facecolor=IMPRINT_PALETTE[i], edgecolor=INK_SOFT, alpha=0.85)
for i in range(len(subcategories))
]
leg = ax.legend(legend_patches, subcategories, title="Experience Level", loc="upper right", fontsize=8)
if leg:
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
leg.get_title().set_color(INK_SOFT)
leg.get_title().set_fontsize(8)
plt.setp(leg.get_texts(), color=INK_SOFT)
# Grid (y-axis only, subtle)
ax.yaxis.grid(True, alpha=0.12, linewidth=0.8, color=INK)
ax.set_axisbelow(True)
# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
ax.spines[s].set_linewidth(1)
# Y-axis limits — extra headroom above the data ceiling so the legend never crowds
# whiskers/fliers, even on data regenerations with taller spreads
ax.set_ylim(0, 122)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/box-grouped/matplotlib/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": "matplotlib",
"page": "https://anyplot.ai/box-grouped/python/matplotlib",
"hub": "https://anyplot.ai/box-grouped",
"code_json": "https://api.anyplot.ai/specs/box-grouped/matplotlib/code",
"spec_json": "https://api.anyplot.ai/specs/box-grouped",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/matplotlib/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/box-grouped/python/matplotlib/plot-dark.png",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Grouped Box Plot on anyplot.ai.