A two-dimensional histogram that displays the joint distribution of two continuous variables as a heatmap with rectangular bins. Each bin's color intensity represents the frequency or count of data points falling within that region, making it ideal for revealing density patterns, clusters, and correlations in bivariate data. Unlike scatter plots that can become cluttered with large datasets, 2D histograms effectively summarize point density.
""" anyplot.ai
histogram-2d: 2D Histogram Heatmap
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 83/100 | Updated: 2026-05-08
"""
import os
import matplotlib.pyplot as plt
import numpy as np
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"
# Data - bivariate normal with positive correlation
np.random.seed(42)
n_points = 5000
mean = [0, 0]
cov = [[1, 0.7], [0.7, 1]]
data = np.random.multivariate_normal(mean, cov, n_points)
x = data[:, 0]
y = data[:, 1]
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
h = ax.hist2d(x, y, bins=40, cmap="viridis", cmin=1)
# Colorbar
cbar = fig.colorbar(h[3], ax=ax, pad=0.02)
cbar.set_label("Frequency", fontsize=20, color=INK)
cbar.ax.tick_params(axis="y", labelsize=16, colors=INK_SOFT)
# Labels and styling
ax.set_xlabel("Feature X", fontsize=20, color=INK)
ax.set_ylabel("Feature Y", fontsize=20, color=INK)
ax.set_title("histogram-2d · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
# Spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of 2D Histogram Heatmap on anyplot.ai.