Notched Box Plot — Seaborn

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.

Notched Box Plot rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
box-notched: Notched Box Plot
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-07
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


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 = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]

# Data - Test score distributions across student cohorts
np.random.seed(42)

cohorts = ["Cohort A", "Cohort B", "Cohort C", "Cohort D"]
data = []

# Cohort A: strong performance, tight cluster
data.extend([{"Cohort": "Cohort A", "Test Score": val} for val in np.random.normal(82, 8, 85)])

# Cohort B: moderate performance, some high outliers
cohort_b_base = np.random.normal(75, 12, 75)
cohort_b_outliers = np.array([95, 96, 98])
data.extend([{"Cohort": "Cohort B", "Test Score": val} for val in np.concatenate([cohort_b_base, cohort_b_outliers])])

# Cohort C: wide variation in performance
data.extend([{"Cohort": "Cohort C", "Test Score": val} for val in np.random.normal(70, 15, 80)])

# Cohort D: lower performance, tight clustering
data.extend([{"Cohort": "Cohort D", "Test Score": val} for val in np.random.normal(68, 9, 70)])

df = pd.DataFrame(data)

# Setup theme
sns.set_theme(
    style="whitegrid",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Create plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)

# Create notched box plot with Okabe-Ito palette
sns.boxplot(
    data=df,
    x="Cohort",
    y="Test Score",
    hue="Cohort",
    palette=IMPRINT[: len(cohorts)],
    notch=True,
    width=0.5,
    linewidth=2.0,
    fliersize=10,
    flierprops={"marker": "o", "markerfacecolor": INK_SOFT, "markeredgecolor": INK, "alpha": 0.6},
    ax=ax,
    legend=False,
)

# Styling
ax.set_title("box-notched · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.set_xlabel("Student Cohort", fontsize=20, color=INK)
ax.set_ylabel("Test Score (%)", fontsize=20, color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

# Grid styling
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
ax.set_axisbelow(True)

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.

Other implementations