A notched box plot extends the standard box plot by adding notches around the median that represent a confidence interval. If the notches of two boxes do not overlap, this provides visual evidence that the medians differ significantly. This variant is particularly valuable for quick visual hypothesis testing and comparing group medians in statistical analysis.
""" anyplot.ai
box-notched: Notched Box Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 87/100 | Updated: 2026-05-07
"""
import os
import sys
import time
from pathlib import Path
# Remove the script's own directory from sys.path so "bokeh" resolves to the
# installed package, not this file.
_this_dir = str(Path(__file__).parent.resolve())
sys.path = [p for p in sys.path if os.path.abspath(p) != _this_dir and p != ""]
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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 (first series is always #009E73)
IMPRINT = [
"#009E73", # bluish green (brand)
"#C475FD", # vermillion
"#4467A3", # blue
"#BD8233", # reddish purple
"#AE3030", # orange
]
# Data - Employee performance scores across departments
np.random.seed(42)
categories = ["Engineering", "Sales", "Marketing", "Operations", "HR"]
# Generate realistic performance score data with varying distributions
data = {
"Engineering": np.random.normal(78, 8, 60),
"Sales": np.random.normal(72, 12, 55),
"Marketing": np.random.normal(75, 6, 50),
"Operations": np.random.normal(68, 10, 65),
"HR": np.random.normal(74, 7, 45),
}
# Add some outliers (constrained to 0-100 range)
data["Sales"] = np.append(data["Sales"], [45, 98])
data["Operations"] = np.append(data["Operations"], [42, 95])
data["HR"] = np.append(data["HR"], [50])
# Clip all values to 0-100 range
for cat in categories:
data[cat] = np.clip(data[cat], 0, 100)
# Compute box plot statistics with notches for each category
box_data = {
"categories": [],
"q1": [],
"q2": [], # median
"q3": [],
"upper": [],
"lower": [],
"notch_lower": [],
"notch_upper": [],
"colors": [],
}
outlier_data = {"category": [], "value": [], "color": []}
for i, cat in enumerate(categories):
values = data[cat]
q1 = np.percentile(values, 25)
q2 = np.percentile(values, 50)
q3 = np.percentile(values, 75)
iqr = q3 - q1
n = len(values)
# Whiskers at 1.5 * IQR
lower_fence = q1 - 1.5 * iqr
upper_fence = q3 + 1.5 * iqr
# Find values within whisker range for accurate whisker placement
in_range = values[(values >= lower_fence) & (values <= upper_fence)]
if len(in_range) > 0:
lower_whisker = in_range.min()
upper_whisker = in_range.max()
else:
lower_whisker = q1
upper_whisker = q3
# Notch: 95% CI around median = ±1.57 × IQR / √n
notch_width = 1.57 * iqr / np.sqrt(n)
notch_lower = q2 - notch_width
notch_upper = q2 + notch_width
box_data["categories"].append(cat)
box_data["q1"].append(q1)
box_data["q2"].append(q2)
box_data["q3"].append(q3)
box_data["upper"].append(upper_whisker)
box_data["lower"].append(lower_whisker)
box_data["notch_lower"].append(notch_lower)
box_data["notch_upper"].append(notch_upper)
box_data["colors"].append(IMPRINT[i])
# Find outliers
outliers = values[(values < lower_fence) | (values > upper_fence)]
for o in outliers:
outlier_data["category"].append(cat)
outlier_data["value"].append(o)
outlier_data["color"].append(IMPRINT[i])
# Create figure
p = figure(
width=4800,
height=2700,
title="box-notched · bokeh · anyplot.ai",
x_range=categories,
y_axis_label="Performance Score (0–100)",
x_axis_label="Department",
)
# Styling - larger sizes for 4800x2700 canvas
p.title.text_font_size = "28pt"
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
p.title.text_color = INK
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
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
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.10
# Box width
box_width = 0.6
# Draw each notched box manually
for i, _cat in enumerate(categories):
q1 = box_data["q1"][i]
q2 = box_data["q2"][i]
q3 = box_data["q3"][i]
nl = box_data["notch_lower"][i]
nu = box_data["notch_upper"][i]
lower = box_data["lower"][i]
upper = box_data["upper"][i]
color = box_data["colors"][i]
half_width = box_width / 2
notch_indent = box_width / 4
# Lower box (q1 to notch_lower)
p.quad(
top=[nl],
bottom=[q1],
left=[i - half_width],
right=[i + half_width],
fill_color=color,
fill_alpha=0.85,
line_color=INK_SOFT,
line_width=2,
)
# Upper box (notch_upper to q3)
p.quad(
top=[q3],
bottom=[nu],
left=[i - half_width],
right=[i + half_width],
fill_color=color,
fill_alpha=0.85,
line_color=INK_SOFT,
line_width=2,
)
# Left notch triangle
p.patch(
x=[i - half_width, i - notch_indent, i - half_width],
y=[nl, q2, nu],
fill_color=color,
fill_alpha=0.85,
line_color=INK_SOFT,
line_width=2,
)
# Right notch triangle
p.patch(
x=[i + half_width, i + notch_indent, i + half_width],
y=[nl, q2, nu],
fill_color=color,
fill_alpha=0.85,
line_color=INK_SOFT,
line_width=2,
)
# Median line
p.segment(x0=[i - notch_indent], x1=[i + notch_indent], y0=[q2], y1=[q2], line_color=INK, line_width=3)
# Whiskers (vertical lines)
p.segment(x0=[i], x1=[i], y0=[q3], y1=[upper], line_color=INK_SOFT, line_width=2)
p.segment(x0=[i], x1=[i], y0=[q1], y1=[lower], line_color=INK_SOFT, line_width=2)
# Whisker caps (horizontal lines)
cap_width = box_width / 3
p.segment(x0=[i - cap_width], x1=[i + cap_width], y0=[upper], y1=[upper], line_color=INK_SOFT, line_width=2)
p.segment(x0=[i - cap_width], x1=[i + cap_width], y0=[lower], y1=[lower], line_color=INK_SOFT, line_width=2)
# Draw outliers
if outlier_data["category"]:
outlier_source = ColumnDataSource(
data={"x": outlier_data["category"], "y": outlier_data["value"], "color": outlier_data["color"]}
)
p.scatter(
x="x",
y="y",
source=outlier_source,
size=15,
fill_color=PAGE_BG,
line_color="color",
line_width=3,
fill_alpha=0.9,
)
# Remove toolbar
p.toolbar_location = None
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome
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 Notched Box Plot on anyplot.ai.