Histogram with KDE Overlay — Matplotlib

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 Matplotlib

Python source (Matplotlib)

""" anyplot.ai
histogram-kde: Histogram with KDE Overlay
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 82/100 | Updated: 2026-05-06
"""

import os

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde


# Theme tokens
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"
ACCENT = "#C475FD"

# Data - simulate stock daily returns (realistic financial data)
np.random.seed(42)
normal_returns = np.random.normal(0.0005, 0.015, 800)
volatile_returns = np.random.normal(-0.002, 0.035, 150)
extreme_returns = np.random.normal(0.001, 0.05, 50)
returns = np.concatenate([normal_returns, volatile_returns, extreme_returns])
np.random.shuffle(returns)
returns = returns * 100

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

# Histogram with density scaling (semi-transparent, brand color)
ax.hist(returns, bins=40, density=True, alpha=0.5, color=BRAND, edgecolor=INK_SOFT, linewidth=1.5, label="Histogram")

# KDE overlay using scipy
kde = gaussian_kde(returns)
x_range = np.linspace(returns.min() - 0.5, returns.max() + 0.5, 500)
kde_values = kde(x_range)
ax.plot(x_range, kde_values, color=ACCENT, linewidth=4, label="KDE")

# Style
ax.set_xlabel("Daily Return (%)", fontsize=20, color=INK)
ax.set_ylabel("Density", fontsize=20, color=INK)
ax.set_title("histogram-kde · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.legend(fontsize=16, loc="upper right")
leg = ax.get_legend()
if leg:
    leg.get_frame().set_facecolor(ELEVATED_BG)
    leg.get_frame().set_edgecolor(INK_SOFT)
    plt.setp(leg.get_texts(), color=INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK_SOFT)

# Save
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Histogram with KDE Overlay on anyplot.ai.

Histogram with KDE Overlay — Matplotlib

Python source (Matplotlib)

Other implementations