Density Histogram — Seaborn

A density histogram displays the distribution of a continuous variable normalized so that the total area under the histogram equals 1, representing probability density instead of raw counts. This normalization allows direct comparison between distributions with different sample sizes and enables overlaying theoretical probability density functions (PDFs) for statistical analysis.

Density Histogram rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
histogram-density: Density Histogram
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 81/100 | Updated: 2026-05-11
"""

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


# Data - Generate realistic test scores with bimodal distribution
np.random.seed(42)
# Create bimodal distribution (two groups of students with different means)
group1 = np.random.normal(loc=65, scale=10, size=300)  # Average performers
group2 = np.random.normal(loc=85, scale=5, size=200)  # High performers
test_scores = np.concatenate([group1, group2])
# Clip to realistic test score range
test_scores = np.clip(test_scores, 0, 100)

# Create figure
fig, ax = plt.subplots(figsize=(16, 9))

# Plot density histogram
sns.histplot(
    test_scores,
    stat="density",
    bins=25,
    color="#306998",
    alpha=0.7,
    edgecolor="white",
    linewidth=1.5,
    ax=ax,
    kde=False,
    label="Density Histogram",
)

# Add KDE overlay for smooth density estimate (seaborn feature)
sns.kdeplot(test_scores, ax=ax, color="#FFD43B", linewidth=4, label="Kernel Density Estimate (KDE)")

# Style and labels
ax.set_xlabel("Test Score (points)", fontsize=20)
ax.set_ylabel("Probability Density", fontsize=20)
ax.set_title("histogram-density · seaborn · pyplots.ai", fontsize=24)
ax.tick_params(axis="both", labelsize=16)
ax.grid(True, alpha=0.3, linestyle="--")

# Legend
ax.legend(fontsize=14, loc="upper left")

plt.tight_layout()
plt.savefig("plot.png", dpi=300, bbox_inches="tight")

Part of Density Histogram on anyplot.ai.

Other implementations