Density Plot with Rug Marks — lets-plot

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 lets-plot

Python source (lets-plot)

""" anyplot.ai
density-rug: Density Plot with Rug Marks
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-18
"""

import os

import numpy as np
import pandas as pd
from lets_plot import (
    LetsPlot,
    aes,
    element_line,
    element_rect,
    element_text,
    geom_density,
    geom_segment,
    ggplot,
    ggsave,
    ggsize,
    labs,
    theme,
    theme_minimal,
)


LetsPlot.setup_html()

# 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"
BRAND = "#009E73"  # Okabe-Ito position 1

# Data - Simulated response times (ms) showing bimodal distribution
np.random.seed(42)
fast_responses = np.random.normal(loc=250, scale=40, size=80)
slow_responses = np.random.normal(loc=450, scale=60, size=40)
response_times = np.concatenate([fast_responses, slow_responses])
df = pd.DataFrame({"response_time": response_times})

# Create rug data - small vertical segments at each data point
rug_height = 0.0003
rug_df = pd.DataFrame({"x": response_times, "ymin": 0, "ymax": rug_height})

# Plot
plot = (
    ggplot()
    + geom_density(aes(x="response_time"), data=df, fill=BRAND, color=BRAND, alpha=0.4, size=1.5)
    + geom_segment(aes(x="x", xend="x", y="ymin", yend="ymax"), data=rug_df, color=BRAND, alpha=0.6, size=1.0)
    + labs(x="Response Time (ms)", y="Density", title="density-rug · Python · letsplot · anyplot.ai")
    + ggsize(1600, 900)
    + theme_minimal()
    + theme(
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_line(color=INK_SOFT, size=0.3),
        panel_grid_minor=element_line(color=INK_SOFT, size=0.2),
        axis_title=element_text(size=20, color=INK),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_line=element_line(color=INK_SOFT),
        plot_title=element_text(size=24, color=INK),
    )
)

# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
ggsave(plot, f"plot-{THEME}.html", path=".")

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

Density Plot with Rug Marks — lets-plot

Python source (lets-plot)

Other implementations