Overlapping histograms display multiple distributions on the same axes using semi-transparent bars, enabling direct visual comparison between groups. This technique reveals differences in central tendency, spread, and shape across categories while maintaining the familiar histogram format. The transparency allows viewers to see where distributions overlap and diverge.
""" anyplot.ai
histogram-overlapping: Overlapping Histograms
Library: letsplot 4.11.0 | Python 3.13.15
Quality: 93/100 | Updated: 2026-08-18
"""
import os
import shutil
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_histogram,
geom_text,
geom_vline,
ggplot,
ggsave,
ggsize,
labs,
layer_tooltips,
scale_fill_manual,
theme,
)
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"
RULE = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Imprint palette (first series always #009E73)
COLORS = ["#009E73", "#C475FD"]
# Data - comparing response times between two experimental conditions
np.random.seed(42)
# Control group - baseline response times (ms)
control = np.random.normal(loc=450, scale=80, size=200)
# Treatment group - faster response times with intervention
treatment = np.random.normal(loc=380, scale=70, size=200)
# Create DataFrame
df = pd.DataFrame(
{"response_time": np.concatenate([control, treatment]), "group": ["Control"] * 200 + ["Treatment"] * 200}
)
mean_control = control.mean()
mean_treatment = treatment.mean()
# Explicit bin width + shared boundary so both distributions align on identical edges
bin_width = 20
bin_start = np.floor(df["response_time"].min() / bin_width) * bin_width
edges = np.arange(bin_start, df["response_time"].max() + bin_width, bin_width)
peak_count = max(np.histogram(control, bins=edges)[0].max(), np.histogram(treatment, bins=edges)[0].max())
# Mean-line callouts, anchored just above the tallest bar for a clear focal point
annotations = pd.DataFrame(
{
"x": [mean_control, mean_treatment],
"y": [peak_count * 1.12] * 2,
"label": [f"Control {mean_control:.0f} ms", f"Treatment {mean_treatment:.0f} ms"],
}
)
# Distinctive lets-plot touch: custom tooltip content for the interactive HTML export
bar_tooltips = layer_tooltips().line("@group").line("Response time|^x ms").line("Count|^y")
# Theme-adaptive styling
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_grid_major_y=element_line(color=RULE, size=0.3),
panel_grid_minor_y=element_blank(),
panel_grid_major_x=element_blank(),
axis_title=element_text(size=12, color=INK),
axis_text=element_text(size=10, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT, size=0.3),
panel_border=element_blank(),
plot_title=element_text(size=16, color=INK),
plot_subtitle=element_text(size=11, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=""),
legend_text=element_text(size=10, color=INK_SOFT),
legend_title=element_text(size=11, color=INK),
legend_position="top",
)
# Overlapping histograms with aligned bins, mean reference lines, and mean-value callouts
plot = (
ggplot(df, aes(x="response_time", fill="group"))
+ geom_histogram(
alpha=0.55,
binwidth=bin_width,
boundary=bin_start,
position="identity",
color=PAGE_BG,
size=0.3,
tooltips=bar_tooltips,
)
+ geom_vline(xintercept=mean_control, color=COLORS[0], linetype="dashed", size=1.1, alpha=0.9)
+ geom_vline(xintercept=mean_treatment, color=COLORS[1], linetype="dashed", size=1.1, alpha=0.9)
+ geom_text(
aes(x="x", y="y", label="label"),
data=annotations.iloc[[0]],
color=COLORS[0],
size=3.2,
hjust=0,
nudge_x=8,
fontface="bold",
)
+ geom_text(
aes(x="x", y="y", label="label"),
data=annotations.iloc[[1]],
color=COLORS[1],
size=3.2,
hjust=1,
nudge_x=-8,
fontface="bold",
)
+ scale_fill_manual(values=COLORS)
+ labs(
x="Response Time (ms)",
y="Count",
title="histogram-overlapping · python · letsplot · anyplot.ai",
subtitle=f"Intervention shifts the mean left by {mean_control - mean_treatment:.0f} ms",
fill="Condition",
)
+ ggsize(800, 450)
+ anyplot_theme
)
# Save as PNG (scale 4x to get 3200 × 1800 px)
ggsave(plot, filename=f"plot-{THEME}.png", scale=4)
# Save as HTML for interactivity
ggsave(plot, filename=f"plot-{THEME}.html")
# Move files from lets-plot-images subdirectory to current directory
if os.path.exists("lets-plot-images"):
for file in os.listdir("lets-plot-images"):
src = os.path.join("lets-plot-images", file)
if os.path.isfile(src):
shutil.move(src, file)
shutil.rmtree("lets-plot-images")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/histogram-overlapping/letsplot/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "histogram-overlapping",
"language": "python",
"library": "letsplot",
"page": "https://anyplot.ai/histogram-overlapping/python/letsplot",
"hub": "https://anyplot.ai/histogram-overlapping",
"code_json": "https://api.anyplot.ai/specs/histogram-overlapping/letsplot/code",
"spec_json": "https://api.anyplot.ai/specs/histogram-overlapping",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-overlapping/python/letsplot/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-overlapping/python/letsplot/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/histogram-overlapping/python/letsplot/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/histogram-overlapping/python/letsplot/plot-dark.html",
"quality_score": 93.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Overlapping Histograms on anyplot.ai.