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.
""" anyplot.ai
density-rug: Density Plot with Rug Marks
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde
# 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 - Response times (ms) for a web service with bimodal distribution
np.random.seed(42)
# Mix of fast responses (cache hits) and slower responses (database queries)
fast_responses = np.random.normal(loc=45, scale=8, size=80)
slow_responses = np.random.normal(loc=120, scale=25, size=70)
response_times = np.concatenate([fast_responses, slow_responses])
response_times = response_times[response_times > 0] # Keep only positive values
# Compute KDE
kde = gaussian_kde(response_times, bw_method="scott")
x_range = np.linspace(response_times.min() - 10, response_times.max() + 10, 500)
density = kde(x_range)
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# KDE curve with fill
ax.fill_between(x_range, density, alpha=0.4, color=BRAND)
ax.plot(x_range, density, color=BRAND, linewidth=3)
# Rug marks along x-axis
rug_height = 0.015 * density.max()
for val in response_times:
ax.plot([val, val], [0, rug_height], color=BRAND, alpha=0.6, linewidth=2)
# Style
ax.set_xlabel("Response Time (ms)", fontsize=20, color=INK)
ax.set_ylabel("Density", fontsize=20, color=INK)
ax.set_title("density-rug · Python · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
# Set y-axis to start at 0 with some padding at top
ax.set_ylim(bottom=-0.0005, top=density.max() * 1.1)
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.