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: matplotlib 3.10.9 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-07
"""
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"
# Okabe-Ito palette - first series is always brand green
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
# Data - Test success rates across different test suites
np.random.seed(42)
# Unit Tests: High success rate, tight distribution
unit_tests = np.random.normal(loc=97, scale=2, size=65)
unit_tests = np.clip(unit_tests, 90, 100)
# Integration Tests: Medium-high success rate, wider spread
integration_tests = np.random.normal(loc=87, scale=8, size=60)
integration_tests = np.clip(integration_tests, 70, 100)
# System Tests: Medium success rate, similar to integration (overlapping notches expected)
system_tests = np.random.normal(loc=85, scale=9, size=55)
system_tests = np.clip(system_tests, 65, 100)
# E2E Tests: Lower success rate with some outliers
e2e_base = np.random.normal(loc=78, scale=10, size=50)
e2e_outliers = np.array([98, 99, 45, 42])
e2e_tests = np.concatenate([e2e_base, e2e_outliers])
e2e_tests = np.clip(e2e_tests, 30, 100)
# Load Tests: Variable success rate due to infrastructure variance
load_tests = np.random.normal(loc=75, scale=12, size=45)
load_tests = np.clip(load_tests, 40, 100)
data = [unit_tests, integration_tests, system_tests, e2e_tests, load_tests]
test_suites = ["Unit Tests", "Integration Tests", "System Tests", "E2E Tests", "Load Tests"]
# Create plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Create notched boxplot
bp = ax.boxplot(
data,
notch=True,
patch_artist=True,
tick_labels=test_suites,
widths=0.6,
showfliers=True,
flierprops={"marker": "o", "markerfacecolor": INK_SOFT, "markersize": 10, "alpha": 0.6},
medianprops={"color": INK, "linewidth": 2.5},
whiskerprops={"color": INK_SOFT, "linewidth": 2},
capprops={"color": INK_SOFT, "linewidth": 2},
boxprops={"color": INK_SOFT, "linewidth": 1.5},
)
# Apply colors to boxes
for patch, color in zip(bp["boxes"], IMPRINT, strict=True):
patch.set_facecolor(color)
patch.set_alpha(0.75)
patch.set_edgecolor(INK_SOFT)
patch.set_linewidth(1.5)
# Labels and styling
ax.set_xlabel("Test Suite", fontsize=20, color=INK)
ax.set_ylabel("Success Rate (%)", fontsize=20, color=INK)
ax.set_title("box-notched · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)
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.yaxis.grid(True, alpha=0.1, linewidth=0.8, color=INK)
# Add annotation explaining notches
ax.annotate(
"Non-overlapping notches suggest\nsignificant median difference",
xy=(1, 97),
xytext=(1.5, 105),
fontsize=14,
color=INK,
arrowprops={"arrowstyle": "->", "color": INK_SOFT, "lw": 1.5},
bbox={"boxstyle": "round,pad=0.5", "facecolor": ELEVATED_BG, "edgecolor": INK_SOFT, "alpha": 0.9},
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Notched Box Plot on anyplot.ai.