Notched Box Plot — lets-plot

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 lets-plot

Python source (lets-plot)

""" anyplot.ai
box-notched: Notched Box Plot
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-07
"""

import os

import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave


LetsPlot.setup_html()

# 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"

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

# Data - department salaries with different distributions for statistical comparison
np.random.seed(42)

departments = ["Engineering", "Marketing", "Sales", "Finance", "Operations"]
data = []

# Engineering: higher salaries, moderate spread
eng_salaries = np.random.normal(95000, 12000, 80)
data.extend([{"Department": "Engineering", "Salary": s} for s in eng_salaries])

# Marketing: medium salaries, wider spread with some outliers
mkt_salaries = np.concatenate(
    [
        np.random.normal(72000, 15000, 70),
        np.array([120000, 125000, 35000]),  # outliers
    ]
)
data.extend([{"Department": "Marketing", "Salary": s} for s in mkt_salaries])

# Sales: variable salaries with commission-based outliers
sales_salaries = np.concatenate(
    [
        np.random.normal(68000, 10000, 65),
        np.array([130000, 140000, 145000, 30000, 28000]),  # high and low outliers
    ]
)
data.extend([{"Department": "Sales", "Salary": s} for s in sales_salaries])

# Finance: similar to engineering but slightly lower (overlapping notches expected)
fin_salaries = np.random.normal(90000, 11000, 75)
data.extend([{"Department": "Finance", "Salary": s} for s in fin_salaries])

# Operations: lower salaries, tight distribution
ops_salaries = np.random.normal(58000, 8000, 85)
data.extend([{"Department": "Operations", "Salary": s} for s in ops_salaries])

df = pd.DataFrame(data)

# Create notched box plot with Okabe-Ito palette
plot = (
    ggplot(df, aes(x="Department", y="Salary", fill="Department", color="Department"))
    + geom_boxplot(notch=True, outlier_size=4, outlier_alpha=0.8, size=1.2, alpha=0.85)
    + scale_fill_manual(values=IMPRINT)
    + scale_color_manual(values=IMPRINT)
    + labs(title="box-notched · letsplot · anyplot.ai", x="Department", y="Annual Salary (USD)")
    + theme_minimal()
    + theme(
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_grid_major_y=element_line(color=INK, size=0.3, linetype="solid"),
        panel_grid_major_x=element_blank(),
        panel_grid_minor=element_blank(),
        axis_title=element_text(size=20, color=INK),
        axis_text_x=element_text(size=16, color=INK_SOFT),
        axis_text_y=element_text(size=16, color=INK_SOFT),
        axis_line=element_line(color=INK_SOFT, size=0.4),
        plot_title=element_text(size=24, color=INK, hjust=0.5),
        legend_position="none",
    )
    + ggsize(1600, 900)
)

# Save as PNG (scale 3x for 4800x2700)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)

# Save as HTML for interactivity
ggsave(plot, f"plot-{THEME}.html", path=".")

Part of Notched Box Plot on anyplot.ai.

Other implementations