Notched Box Plot — plotnine

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 plotnine

Python source (plotnine)

""" anyplot.ai
box-notched: Notched Box Plot
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-07
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    element_line,
    element_rect,
    element_text,
    geom_boxplot,
    ggplot,
    labs,
    scale_fill_manual,
    theme,
    theme_minimal,
)


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

# Data - clinical trial outcomes across treatment groups
np.random.seed(42)

groups = ["Control", "Treatment A", "Treatment B", "Treatment C", "Long-term"]
n_per_group = [120, 105, 110, 95, 100]

data = []
# Control: baseline, modest median
data.extend([{"group": "Control", "score": v} for v in np.random.normal(65, 12, n_per_group[0])])
# Treatment A: moderate improvement
data.extend([{"group": "Treatment A", "score": v} for v in np.random.normal(72, 11, n_per_group[1])])
# Treatment B: strong improvement
data.extend([{"group": "Treatment B", "score": v} for v in np.random.normal(78, 10, n_per_group[2])])
# Treatment C: variable response, some outliers
treatment_c = np.concatenate([np.random.normal(70, 13, 70), np.random.normal(88, 6, 25)])
data.extend([{"group": "Treatment C", "score": v} for v in treatment_c])
# Long-term: sustained benefit
data.extend([{"group": "Long-term", "score": v} for v in np.random.normal(75, 9, n_per_group[4])])

df = pd.DataFrame(data)
df["group"] = pd.Categorical(df["group"], categories=groups, ordered=True)

# Plot
plot = (
    ggplot(df, aes(x="group", y="score", fill="group"))
    + geom_boxplot(notch=True, notchwidth=0.5, outlier_size=3, outlier_alpha=0.7, size=0.9)
    + scale_fill_manual(values=IMPRINT)
    + labs(x="Treatment Group", y="Clinical Score", title="box-notched · plotnine · anyplot.ai")
    + theme_minimal()
    + theme(
        figure_size=(16, 9),
        text=element_text(size=14),
        axis_title=element_text(size=20, color=INK),
        axis_text=element_text(size=16, color=INK_SOFT),
        plot_title=element_text(size=24, color=INK),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
        panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
        panel_border=element_rect(color=INK_SOFT, fill=None),
        axis_line=element_line(color=INK_SOFT),
        legend_position="none",
    )
)

# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)

Part of Notched Box Plot on anyplot.ai.

Other implementations