Frequency Polygon for Distribution Comparison — lets-plot

A frequency polygon connects the midpoints of histogram bins with straight line segments, creating a smooth outline of the distribution shape. This visualization excels at comparing multiple distributions simultaneously since lines overlap without obscuring each other, unlike stacked or overlapping histogram bars. Frequency polygons reveal differences in central tendency, spread, skewness, and modality across groups with minimal visual clutter.

Frequency Polygon for Distribution Comparison rendered with lets-plot

Python source (lets-plot)

""" anyplot.ai
frequency-polygon-basic: Frequency Polygon for Distribution Comparison
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-17
"""

import os

import numpy as np
import pandas as pd
from lets_plot import (
    LetsPlot,
    aes,
    element_line,
    element_rect,
    element_text,
    geom_freqpoly,
    ggplot,
    ggsize,
    labs,
    scale_color_manual,
    theme,
    theme_minimal,
)
from lets_plot.export import ggsave


LetsPlot.setup_html()

# 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"
RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

# Okabe-Ito palette (first series is brand green #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]

# Data - Response times (ms) across three experimental conditions
np.random.seed(42)

# Control group: normal distribution centered at 350ms
control = np.random.normal(loc=350, scale=60, size=200)

# Treatment A: slightly faster responses, centered at 300ms
treatment_a = np.random.normal(loc=300, scale=50, size=200)

# Treatment B: bimodal - mix of fast and slow responders
treatment_b = np.concatenate(
    [np.random.normal(loc=280, scale=40, size=120), np.random.normal(loc=420, scale=45, size=80)]
)

# Combine into DataFrame
df = pd.DataFrame(
    {
        "response_time": np.concatenate([control, treatment_a, treatment_b]),
        "condition": (["Control"] * 200 + ["Treatment A"] * 200 + ["Treatment B"] * 200),
    }
)

# Create frequency polygon with 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=element_line(color=RULE, size=0.3),
    panel_grid_minor=element_line(color=RULE, size=0.15),
    axis_title=element_text(size=20, color=INK),
    axis_text=element_text(size=16, color=INK_SOFT),
    axis_line=element_line(color=INK_SOFT, size=0.5),
    plot_title=element_text(size=24, color=INK),
    legend_position="right",
    legend_text=element_text(size=16, color=INK_SOFT),
    legend_title=element_text(size=16, color=INK),
)

plot = (
    ggplot(df, aes(x="response_time", color="condition"))
    + geom_freqpoly(bins=25, size=2.5, alpha=0.9)
    + scale_color_manual(values=IMPRINT)
    + labs(
        x="Response Time (ms)",
        y="Frequency",
        title="frequency-polygon-basic · letsplot · anyplot.ai",
        color="Condition",
    )
    + theme_minimal()
    + anyplot_theme
    + ggsize(1600, 900)
)

# Save PNG (scale 3x for 4800 × 2700 px)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)

# Save HTML for interactive version
ggsave(plot, f"plot-{THEME}.html", path=".")

Part of Frequency Polygon for Distribution Comparison on anyplot.ai.

Other implementations