Horizontal Box Plot — Seaborn

A horizontal box plot displays the distribution of numerical data through quartiles with the boxes oriented horizontally. This orientation is particularly useful when category labels are long or when comparing many groups, as it allows for easier reading of labels on the y-axis.

Horizontal Box Plot rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
box-horizontal: Horizontal Box Plot
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-12
"""

import os

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


# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
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"
BRAND = "#009E73"  # Okabe-Ito position 1 — ALWAYS first series

# Data - salary ranges by job title (with realistic distributions)
np.random.seed(42)

categories = ["Software Engineer", "Product Manager", "Data Scientist", "UX Designer", "DevOps Engineer"]

data = []
# Software Engineer - moderate spread
data.extend(
    [
        {"Job Title": "Software Engineer", "Salary (USD)": v}
        for v in np.concatenate(
            [
                np.random.normal(135000, 25000, 80),
                np.random.normal(200000, 15000, 5),  # outliers (senior/staff)
            ]
        )
    ]
)

# Product Manager - higher median with tight distribution
data.extend([{"Job Title": "Product Manager", "Salary (USD)": v} for v in np.random.normal(155000, 22000, 85)])

# Data Scientist - wide spread with high values
data.extend(
    [
        {"Job Title": "Data Scientist", "Salary (USD)": v}
        for v in np.concatenate(
            [
                np.random.normal(145000, 30000, 75),
                np.random.normal(220000, 20000, 10),  # outliers
            ]
        )
    ]
)

# UX Designer - moderate values
data.extend([{"Job Title": "UX Designer", "Salary (USD)": v} for v in np.random.normal(120000, 20000, 85)])

# DevOps Engineer - highest median, tight distribution
data.extend([{"Job Title": "DevOps Engineer", "Salary (USD)": v} for v in np.random.normal(160000, 18000, 90)])

df = pd.DataFrame(data)

# Sort categories by median for easier comparison
category_order = df.groupby("Job Title")["Salary (USD)"].median().sort_values().index.tolist()

# Set theme-adaptive styling
sns.set_theme(
    style="ticks",
    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 figure
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)

# Horizontal box plot with single color
sns.boxplot(
    data=df,
    x="Salary (USD)",
    y="Job Title",
    order=category_order,
    color=BRAND,
    linewidth=2,
    width=0.6,
    flierprops={"marker": "o", "markersize": 8, "alpha": 0.6},
    ax=ax,
)

# Labels and styling
ax.set_xlabel("Salary (USD)", fontsize=20, color=INK)
ax.set_ylabel("Job Title", fontsize=20, color=INK)
ax.set_title("box-horizontal · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for spine in ("left", "bottom"):
    ax.spines[spine].set_color(INK_SOFT)

# Grid styling - subtle on x-axis only
ax.xaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
ax.yaxis.grid(False)

# Adjust layout
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Horizontal Box Plot on anyplot.ai.

Other implementations