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: plotly 6.7.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-08
"""
import os
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# 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"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
# Data - customer age vs annual purchase frequency in retail market research
np.random.seed(42)
n_points = 5000
# Create correlated data: older customers tend to have slightly higher purchase frequency
# with realistic distributions
mean = [45, 25] # Mean age and mean purchases per year
cov = [[150, 35], [35, 120]] # Positive correlation (0.6)
data = np.random.multivariate_normal(mean, cov, n_points)
age = np.clip(data[:, 0], 18, 85) # Realistic age range
purchases = np.clip(data[:, 1], 0, 80) # Purchases per year
# Create figure with marginal histograms using shared_xaxes/shared_yaxes
fig = make_subplots(
rows=2,
cols=2,
column_widths=[0.8, 0.2],
row_heights=[0.2, 0.8],
horizontal_spacing=0.01,
vertical_spacing=0.01,
shared_xaxes=True,
shared_yaxes=True,
specs=[[{"type": "histogram"}, None], [{"type": "histogram2d"}, {"type": "histogram"}]],
)
# Main 2D histogram heatmap
fig.add_trace(
go.Histogram2d(
x=age,
y=purchases,
colorscale="Viridis",
nbinsx=40,
nbinsy=40,
colorbar=dict(
title=dict(text="Count", font=dict(size=20)), tickfont=dict(size=16), len=0.65, y=0.35, yanchor="middle"
),
),
row=2,
col=1,
)
# Marginal histogram for age (top)
fig.add_trace(
go.Histogram(x=age, nbinsx=40, marker=dict(color="#009E73", line=dict(width=0)), showlegend=False), row=1, col=1
)
# Marginal histogram for purchases (right)
fig.add_trace(
go.Histogram(y=purchases, nbinsy=40, marker=dict(color="#009E73", line=dict(width=0)), showlegend=False),
row=2,
col=2,
)
# Update layout with theme-adaptive styling
fig.update_layout(
title=dict(
text="histogram-2d · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center", y=0.98
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font=dict(color=INK),
bargap=0.02,
)
# Update axes for main plot with descriptive labels
fig.update_xaxes(
title=dict(text="Customer Age (years)", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
gridcolor=GRID,
linecolor=INK_SOFT,
row=2,
col=1,
)
fig.update_yaxes(
title=dict(text="Annual Purchases (count)", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
gridcolor=GRID,
linecolor=INK_SOFT,
row=2,
col=1,
)
# Configure marginal histogram axes with grid lines
fig.update_xaxes(showticklabels=False, gridcolor=GRID, linecolor=INK_SOFT, row=1, col=1)
fig.update_yaxes(showticklabels=False, gridcolor=GRID, linecolor=INK_SOFT, row=1, col=1)
fig.update_xaxes(showticklabels=False, gridcolor=GRID, linecolor=INK_SOFT, row=2, col=2)
fig.update_yaxes(showticklabels=False, gridcolor=GRID, linecolor=INK_SOFT, row=2, col=2)
# Save as PNG (4800 x 2700 px)
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
# Save as interactive HTML
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of 2D Histogram Heatmap on anyplot.ai.