Basic Rug Plot — plotnine

A rug plot displays individual data points as small tick marks along an axis, typically at the bottom or side of another plot. Unlike histograms or density plots that bin data, rug plots show the exact location of every observation. They reveal clustering patterns, gaps in data, and the precise distribution of values with minimal visual footprint.

Basic Rug Plot rendered with plotnine

Python source (plotnine)

""" anyplot.ai
rug-basic: Basic Rug Plot
Library: plotnine 0.15.7 | Python 3.13.14
Quality: 93/100 | Updated: 2026-07-25
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    annotate,
    element_blank,
    element_line,
    element_rect,
    element_text,
    geom_density,
    geom_rug,
    ggplot,
    labs,
    scale_x_continuous,
    theme,
    theme_minimal,
)


# 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"
AMBER = "#DDCC77"  # Imprint semantic anchor — warning / caution, used to flag the outlier region

# Data - simulated response times with realistic clustering and gaps
np.random.seed(42)
cluster1 = np.random.normal(150, 20, 40)
cluster2 = np.random.normal(280, 35, 30)
cluster3 = np.random.normal(450, 50, 20)
outliers = np.array([620, 680, 750, 820])

values = np.concatenate([cluster1, cluster2, cluster3, outliers])
df = pd.DataFrame({"response_time": values})
outlier_start = 550  # visual boundary separating the main mass from the sparse tail

# Plot
anyplot_theme = theme(
    figure_size=(8, 4.5),
    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_background=element_rect(fill=PAGE_BG),
    panel_grid_major_x=element_blank(),
    panel_grid_minor_x=element_blank(),
    panel_grid_major_y=element_blank(),
    panel_grid_minor_y=element_blank(),
    panel_border=element_blank(),
    axis_line_x=element_line(color=INK_SOFT, size=0.5),
    axis_title_x=element_text(size=10, color=INK),
    axis_title_y=element_blank(),
    axis_text_x=element_text(size=8, color=INK_SOFT),
    axis_text_y=element_blank(),
    axis_ticks_major_y=element_blank(),
    plot_title=element_text(size=12, color=INK),
    plot_subtitle=element_text(size=8, color=INK_SOFT),
)

plot = (
    ggplot(df, aes(x="response_time"))
    # Subtle amber tint calls out the sparse outlier tail against the three-cluster mass
    + annotate(
        "rect",
        xmin=outlier_start,
        xmax=float(values.max()) + 40,
        ymin=-float("inf"),
        ymax=float("inf"),
        fill=AMBER,
        alpha=0.08,
    )
    + annotate(
        "text",
        x=outlier_start + 10,
        y=float("inf"),
        label="outliers",
        color=INK_SOFT,
        size=7,
        ha="left",
        va="top",
        fontstyle="italic",
    )
    + geom_density(fill=BRAND, color=BRAND, alpha=0.3, size=1.0)
    + geom_rug(alpha=0.6, sides="b", size=0.8, color=BRAND)
    + scale_x_continuous(expand=(0.03, 0))
    + labs(
        x="Response Time (ms)",
        y="",
        title="rug-basic · plotnine · anyplot.ai",
        subtitle="94 API calls — three latency clusters with a sparse slow-response tail",
    )
    + theme_minimal()
    + anyplot_theme
)

# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)

Part of Basic Rug Plot on anyplot.ai.

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