Density Plot with Rug Marks — Pygal

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 Pygal

Python source (Pygal)

""" anyplot.ai
density-rug: Density Plot with Rug Marks
Library: pygal 3.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""

import os

import numpy as np
import pygal
from pygal.style import Style
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Okabe-Ito palette (first series = brand green)
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")

# Data - bimodal distribution to show interesting density shape
np.random.seed(42)
values = np.concatenate(
    [
        np.random.normal(loc=35, scale=8, size=60),  # First mode
        np.random.normal(loc=65, scale=10, size=90),  # Second mode
    ]
)

# Calculate KDE
kde = gaussian_kde(values)
x_range = np.linspace(values.min() - 10, values.max() + 10, 200)
density = kde(x_range)

# Scale density for visibility
density_scaled = density / density.max()

# Custom style (scaled for 4800x2700 canvas)
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=28,
    label_font_size=22,
    major_label_font_size=18,
    legend_font_size=16,
    value_font_size=14,
    stroke_width=3,
)

# Create XY chart for continuous data
chart = pygal.XY(
    width=4800,
    height=2700,
    style=custom_style,
    title="density-rug · Python · pygal · anyplot.ai",
    x_title="Measurement Value",
    y_title="Density (normalized)",
    show_legend=True,
    legend_at_bottom=True,
    stroke=True,
    fill=True,
    show_dots=False,
    show_x_guides=True,
    show_y_guides=True,
    range=(0, 1.15),
    include_x_axis=True,
    truncate_legend=-1,
)

# Add KDE curve as line with fill
kde_points = [(float(x), float(y)) for x, y in zip(x_range, density_scaled, strict=True)]
chart.add("KDE Density Curve", kde_points, stroke_style={"width": 5})

# Add rug marks as dots along the x-axis at y=0
sorted_values = np.sort(values)
rug_points = [(float(v), 0.0) for v in sorted_values]
chart.add("Rug Marks", rug_points, stroke=False, show_dots=True, fill=False, dots_size=10)

# Save as PNG and HTML with theme-suffixed names
chart.render_to_png(f"plot-{THEME}.png")
chart.render_to_file(f"plot-{THEME}.html")

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

Density Plot with Rug Marks — Pygal

Python source (Pygal)

Other implementations