A density plot (also known as Kernel Density Estimation or KDE plot) visualizes the distribution of a continuous variable by smoothing the data into a continuous probability density curve. Unlike histograms which use discrete bins, density plots provide a smooth representation of the underlying distribution, making it easier to identify patterns such as skewness, modality, and overall shape.
""" anyplot.ai
density-basic: Basic Density Plot
Library: letsplot 4.10.1 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-30
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_density,
geom_segment,
geom_text,
geom_vline,
ggplot,
ggsize,
labs,
layer_tooltips,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
# Theme tokens — Imprint palette, theme-adaptive chrome
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" # Imprint palette position 1 — always first series
# Data - Marathon finish times: trimodal distribution with realistic right skew
np.random.seed(42)
finish_minutes = np.concatenate(
[
np.random.normal(240, 25, 350), # Main pack (~4 hour runners)
np.random.normal(200, 15, 100), # Competitive runners (~3:20)
np.random.normal(300, 20, 50), # Casual runners (~5 hours)
]
)
finish_minutes = np.clip(finish_minutes, 140, 400)
df = pd.DataFrame({"time": finish_minutes})
# Rug data: individual observations as small vertical ticks at the x-axis
rug_df = pd.DataFrame({"x": finish_minutes, "y0": 0.0, "y1": 0.0003})
# Peak annotation data for the three runner sub-populations
peaks_df = pd.DataFrame(
{
"x": [200, 240, 300],
"y": [0.0065, 0.0110, 0.0030],
"label": ["Elite\n~3:20", "Main Pack\n~4:00", "Casual\n~5:00"],
}
)
# Title
title = "density-basic · python · letsplot · anyplot.ai"
# Chrome theme (theme-adaptive background, text, grid)
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_border=element_blank(),
panel_grid_major_x=element_blank(),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.3),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
axis_ticks=element_blank(),
plot_title=element_text(color=INK, size=16),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
)
# Plot
plot = (
ggplot(df, aes(x="time"))
+ geom_vline(data=peaks_df, mapping=aes(xintercept="x"), color=INK_SOFT, linetype="dashed", size=0.5, alpha=0.6)
+ geom_density(
fill=BRAND,
color=BRAND,
alpha=0.35,
size=1.5,
kernel="gaussian",
adjust=0.85,
trim=True,
tooltips=layer_tooltips().line("density|@..density.."),
)
+ geom_segment(data=rug_df, mapping=aes(x="x", y="y0", xend="x", yend="y1"), color=BRAND, alpha=0.40, size=0.6)
+ geom_text(data=peaks_df, mapping=aes(x="x", y="y", label="label"), color=INK, size=3.5, vjust=0, hjust=0.5)
+ labs(x="Finish Time (minutes)", y="Density (×10⁻³)", title=title)
+ scale_x_continuous(breaks=list(range(150, 401, 50)))
+ scale_y_continuous(
breaks=[0.002, 0.004, 0.006, 0.008, 0.010], labels=["2", "4", "6", "8", "10"], expand=[0.02, 0, 0.15, 0]
)
+ theme_minimal()
+ anyplot_theme
+ ggsize(800, 450)
)
# Save — theme-suffixed filenames, scale=4 → 3200×1800 px
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Density Plot on anyplot.ai.