Histogram with KDE Overlay — Seaborn

A histogram with kernel density estimate (KDE) overlay combines discrete binning with continuous density estimation to visualize the distribution of continuous data. The histogram bars show frequency counts in each bin while the smooth KDE curve reveals the underlying probability density, making it easier to perceive the true shape of the distribution without binning artifacts.

Histogram with KDE Overlay rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
histogram-kde: Histogram with KDE Overlay
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-06
"""

import os

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


# Theme tokens (see prompts/default-style-guide.md)
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"

# Okabe-Ito palette — first series always #009E73
HISTOGRAM_COLOR = "#009E73"  # Brand green
KDE_COLOR = "#C475FD"  # Vermillion

# Configure seaborn theme with theme-adaptive colors
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data: Test score distribution (quality control scenario)
np.random.seed(42)
# Mix of different student performance patterns
test_scores = np.concatenate(
    [
        np.random.normal(72, 8, 300),  # Most students in 60-85 range
        np.random.normal(92, 5, 80),  # High-performing students
        np.random.normal(45, 10, 40),  # Struggling students
    ]
)
test_scores = np.clip(test_scores, 0, 100)  # Bound to valid range
np.random.shuffle(test_scores)

# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Histogram with semi-transparent bars
sns.histplot(
    test_scores,
    bins=35,
    kde=False,
    stat="density",
    alpha=0.5,
    color=HISTOGRAM_COLOR,
    edgecolor=PAGE_BG,
    linewidth=0.8,
    ax=ax,
    label="Histogram",
)

# KDE overlay for smooth density curve
sns.kdeplot(test_scores, color=KDE_COLOR, linewidth=4, ax=ax, label="KDE")

# Style
ax.set_xlabel("Test Score (%)", fontsize=20, color=INK)
ax.set_ylabel("Density", fontsize=20, color=INK)
ax.set_title("histogram-kde · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Clean up spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for spine in ("left", "bottom"):
    ax.spines[spine].set_color(INK_SOFT)

# Subtle grid
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, linestyle="-", color=INK)

# Legend
ax.legend(frameon=True, fancybox=False, fontsize=16, framealpha=0.95, edgecolor=INK_SOFT, facecolor=ELEVATED_BG)

plt.tight_layout()
output_dir = os.path.dirname(os.path.abspath(__file__))
plt.savefig(os.path.join(output_dir, f"plot-{THEME}.png"), dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Histogram with KDE Overlay on anyplot.ai.

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