Density Plot with Rug Marks — Seaborn

A kernel density estimation (KDE) plot combined with rug marks along the x-axis, showing both the smoothed probability distribution and the exact location of each individual data point. This combination provides the best of both worlds: the KDE reveals the overall shape, modality, and smoothed density of the distribution, while the rug marks preserve transparency about where actual observations fall, highlighting data density and potential gaps.

Density Plot with Rug Marks rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
density-rug: Density Plot with Rug Marks
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-18
"""

import os

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


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BRAND = "#009E73"  # Okabe-Ito position 1

# Data - Sensor measurement errors (single-peak exponential decay)
np.random.seed(17)
# Most measurements have small errors, few have large errors (exponential distribution)
# This represents measurement precision in scientific instruments
errors = np.random.exponential(scale=15, size=250)
# Add a small noise component to make it slightly more realistic
errors = errors + np.random.normal(0, 2, size=250)
# Clip to realistic bounds (0-80)
errors = np.clip(errors, 0, 80)

# Configure seaborn theme
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,
    },
)

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

# Plot KDE with fill
sns.kdeplot(data=errors, ax=ax, fill=True, alpha=0.35, color=BRAND, linewidth=3, bw_adjust=0.9)

# Add rug plot
sns.rugplot(data=errors, ax=ax, color=BRAND, alpha=0.5, height=0.04, linewidth=1.2)

# Styling
ax.set_xlabel("Measurement Error (μm)", fontsize=20, color=INK)
ax.set_ylabel("Density", fontsize=20, color=INK)
ax.set_title("density-rug · Python · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Grid styling (y-axis only, subtle)
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
ax.set_axisbelow(True)

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

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

Part of Density Plot with Rug Marks on anyplot.ai.

Density Plot with Rug Marks — Seaborn

Python source (Seaborn)

Other implementations