Horizontal Box Plot — Plotly

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 Plotly

Python source (Plotly)

""" anyplot.ai
box-horizontal: Horizontal Box Plot
Library: plotly 6.7.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-12
"""

import os

import numpy as np
import pandas as pd
import plotly.graph_objects as go


# 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"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

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

# Data - Response times (ms) by service type
np.random.seed(42)

services = ["Database Query", "API Gateway", "Authentication", "File Storage", "Cache Lookup", "Message Queue"]

# Generate data with different distributions for each service
data = []
distributions = [
    (120, 40, 15),  # Database Query - higher, more spread
    (85, 25, 8),  # API Gateway - medium
    (45, 15, 5),  # Authentication - fast, tight
    (200, 80, 20),  # File Storage - slow, very spread, many outliers
    (15, 5, 3),  # Cache Lookup - very fast
    (65, 30, 10),  # Message Queue - medium with spread
]

for service, (mean, std, n_outliers) in zip(services, distributions):
    n = 100
    values = np.random.normal(mean, std, n)
    # Add some outliers
    outliers = np.random.normal(mean + 3 * std, std / 2, n_outliers)
    all_values = np.concatenate([values, outliers])
    # Ensure positive values (response times can't be negative)
    all_values = np.maximum(all_values, 5)
    for v in all_values:
        data.append({"Service": service, "Response Time (ms)": v})

df = pd.DataFrame(data)

# Sort services by median response time for easier comparison
median_order = df.groupby("Service")["Response Time (ms)"].median().sort_values()
services_sorted = median_order.index.tolist()

# Create figure with horizontal box plots
fig = go.Figure()

for i, service in enumerate(services_sorted):
    service_data = df[df["Service"] == service]["Response Time (ms)"]
    fig.add_trace(
        go.Box(
            x=service_data,
            name=service,
            orientation="h",
            marker=dict(color=IMPRINT[i % len(IMPRINT)], size=8, outliercolor=IMPRINT[i % len(IMPRINT)]),
            line=dict(color=IMPRINT[i % len(IMPRINT)], width=2),
            fillcolor=IMPRINT[i % len(IMPRINT)],
            opacity=0.7,
            boxmean=False,
            hovertemplate="<b>%{name}</b><br>Value: %{x:.1f} ms<extra></extra>",
        )
    )

# Layout
fig.update_layout(
    title=dict(text="box-horizontal · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"),
    xaxis=dict(
        title=dict(text="Response Time (ms)", font=dict(size=22, color=INK)),
        tickfont=dict(size=18, color=INK_SOFT),
        gridcolor=GRID,
        gridwidth=1,
        linecolor=INK_SOFT,
        zerolinecolor=INK_SOFT,
    ),
    yaxis=dict(
        title=dict(text="Service Type", font=dict(size=22, color=INK)),
        tickfont=dict(size=18, color=INK_SOFT),
        linecolor=INK_SOFT,
    ),
    paper_bgcolor=PAGE_BG,
    plot_bgcolor=PAGE_BG,
    showlegend=False,
    margin=dict(l=180, r=50, t=80, b=80),
    font=dict(color=INK),
)

# Save
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")

Part of Horizontal Box Plot on anyplot.ai.

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