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: bokeh 3.9.0 | Python 3.13.13
Quality: 80/100 | Updated: 2026-05-08
"""
import numpy as np
from bokeh.io import export_png, save
from bokeh.models import ColumnDataSource, Legend, LegendItem
from bokeh.plotting import figure
from bokeh.resources import CDN
# Data - Employee performance scores across departments by experience level
np.random.seed(42)
categories = ["Sales", "Engineering", "Marketing", "Support"]
subcategories = ["Junior", "Senior", "Lead"]
colors = ["#306998", "#FFD43B", "#4ECDC4"] # Python Blue, Python Yellow, Teal
# Generate performance data with different distributions per group
data = {}
for cat in categories:
data[cat] = {}
for i, sub in enumerate(subcategories):
# Different base means for departments
base = {"Sales": 70, "Engineering": 75, "Marketing": 68, "Support": 72}[cat]
# Experience adds to mean
exp_bonus = i * 8
# Generate realistic performance scores (50-100 range)
n_points = 50
scores = np.random.normal(base + exp_bonus, 10, n_points)
scores = np.clip(scores, 40, 100)
# Add some outliers for visual interest
if cat == "Engineering" and sub == "Lead":
scores = np.append(scores, [38, 100, 100]) # Add outliers
if cat == "Sales" and sub == "Junior":
scores = np.append(scores, [35, 105]) # Add outliers
data[cat][sub] = scores
# Calculate box plot statistics
def calc_boxplot_stats(values):
q1 = np.percentile(values, 25)
q2 = np.percentile(values, 50) # median
q3 = np.percentile(values, 75)
iqr = q3 - q1
upper_whisker = min(max(values), q3 + 1.5 * iqr)
lower_whisker = max(min(values), q1 - 1.5 * iqr)
outliers = values[(values < lower_whisker) | (values > upper_whisker)]
return {"q1": q1, "q2": q2, "q3": q3, "lower": lower_whisker, "upper": upper_whisker, "outliers": outliers}
# Create figure
p = figure(
width=4800,
height=2700,
x_range=categories,
y_range=(30, 110),
title="box-grouped · bokeh · pyplots.ai",
x_axis_label="Department",
y_axis_label="Performance Score",
tools="",
toolbar_location=None,
)
# Styling
p.title.text_font_size = "36pt"
p.title.align = "center"
p.xaxis.axis_label_text_font_size = "28pt"
p.yaxis.axis_label_text_font_size = "28pt"
p.xaxis.major_label_text_font_size = "22pt"
p.yaxis.major_label_text_font_size = "22pt"
p.xgrid.grid_line_alpha = 0.3
p.ygrid.grid_line_alpha = 0.3
p.xgrid.grid_line_dash = "dashed"
p.ygrid.grid_line_dash = "dashed"
# Box dimensions
box_width = 0.22
offsets = [-0.28, 0, 0.28] # Position offsets for subcategories
# Store renderers for legend
legend_items = []
# Draw grouped box plots
for sub_idx, sub in enumerate(subcategories):
color = colors[sub_idx]
offset = offsets[sub_idx]
# Collect data for this subcategory across all categories
boxes_lower = []
boxes_upper = []
boxes_q1 = []
boxes_q2 = []
boxes_q3 = []
x_positions = []
all_outliers_x = []
all_outliers_y = []
for cat_idx, cat in enumerate(categories):
stats = calc_boxplot_stats(data[cat][sub])
x_pos = cat_idx + offset
x_positions.append(x_pos)
boxes_lower.append(stats["lower"])
boxes_upper.append(stats["upper"])
boxes_q1.append(stats["q1"])
boxes_q2.append(stats["q2"])
boxes_q3.append(stats["q3"])
# Collect outliers
for outlier in stats["outliers"]:
all_outliers_x.append(x_pos)
all_outliers_y.append(outlier)
# Draw whisker stems (vertical lines from lower to upper)
for i, _cat in enumerate(categories):
x_pos = x_positions[i]
# Lower whisker
p.segment(x0=[x_pos], y0=[boxes_lower[i]], x1=[x_pos], y1=[boxes_q1[i]], line_color="#333333", line_width=3)
# Upper whisker
p.segment(x0=[x_pos], y0=[boxes_q3[i]], x1=[x_pos], y1=[boxes_upper[i]], line_color="#333333", line_width=3)
# Whisker caps
cap_width = box_width * 0.6
p.segment(
x0=[x_pos - cap_width / 2],
y0=[boxes_lower[i]],
x1=[x_pos + cap_width / 2],
y1=[boxes_lower[i]],
line_color="#333333",
line_width=3,
)
p.segment(
x0=[x_pos - cap_width / 2],
y0=[boxes_upper[i]],
x1=[x_pos + cap_width / 2],
y1=[boxes_upper[i]],
line_color="#333333",
line_width=3,
)
# Draw boxes (q1 to q3)
box_source = ColumnDataSource(data={"x": x_positions, "bottom": boxes_q1, "top": boxes_q3})
box_renderer = p.vbar(
x="x",
width=box_width,
bottom="bottom",
top="top",
source=box_source,
fill_color=color,
fill_alpha=0.8,
line_color="#333333",
line_width=2,
)
# Draw median lines
for i in range(len(categories)):
p.segment(
x0=[x_positions[i] - box_width / 2],
y0=[boxes_q2[i]],
x1=[x_positions[i] + box_width / 2],
y1=[boxes_q2[i]],
line_color="#333333",
line_width=4,
)
# Draw outliers
if all_outliers_x:
p.scatter(
x=all_outliers_x,
y=all_outliers_y,
size=18,
color=color,
alpha=0.9,
line_color="#333333",
line_width=2,
marker="circle",
)
# Store for legend
legend_items.append(LegendItem(label=sub, renderers=[box_renderer]))
# Add legend
legend = Legend(
items=legend_items,
location="top_right",
label_text_font_size="22pt",
glyph_width=40,
glyph_height=40,
spacing=15,
padding=20,
background_fill_alpha=0.8,
border_line_color="#cccccc",
border_line_width=2,
)
p.add_layout(legend, "right")
# Adjust x-axis to show category names at correct positions
p.xaxis.major_label_overrides = {cat: cat for cat in categories}
# Save
export_png(p, filename="plot.png")
# Also save HTML for interactive version
save(p, filename="plot.html", resources=CDN, title="box-grouped · bokeh · pyplots.ai")
Part of Grouped Box Plot on anyplot.ai.