A density contour plot (also known as a 2D KDE contour plot) displays the concentration of points in a 2D scatter plot using contour lines. The contours connect points of equal density, revealing clusters, patterns, and the overall bivariate distribution shape.
""" anyplot.ai
contour-density: Density Contour Plot
Library: plotly 6.7.0 | Python 3.13.13
Quality: 95/100 | Updated: 2026-05-16
"""
import os
import numpy as np
import plotly.graph_objects as go
# 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"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
# Data - bivariate distribution with three clusters
np.random.seed(42)
# Create three clusters with realistic domain context (height vs weight measurements)
n_points = 500
# Cluster 1: Lighter individuals
cluster1_x = np.random.normal(160, 8, n_points // 3) # Height in cm
cluster1_y = np.random.normal(60, 6, n_points // 3) # Weight in kg
# Cluster 2: Heavier individuals
cluster2_x = np.random.normal(175, 10, n_points // 3)
cluster2_y = np.random.normal(80, 8, n_points // 3)
# Cluster 3: Tall but lighter individuals
cluster3_x = np.random.normal(180, 7, n_points // 3)
cluster3_y = np.random.normal(70, 7, n_points // 3)
x = np.concatenate([cluster1_x, cluster2_x, cluster3_x])
y = np.concatenate([cluster1_y, cluster2_y, cluster3_y])
# Define theme-aware colorscale for density (continuous data)
# Use viridis-like progression that works on both light and dark backgrounds
colorscale = [
[0, "rgba(255,255,255,0)"], # Transparent at low density
[0.2, "#FDB462"], # Light orange (visible on both themes)
[0.5, "#4467A3"], # Blue (Okabe-Ito position 3)
[1, "#005073"], # Dark blue (increased contrast)
]
# Create figure with density contour
fig = go.Figure()
# Add density contour plot with interactive hover
fig.add_trace(
go.Histogram2dContour(
x=x,
y=y,
colorscale=colorscale,
contours=dict(showlabels=False, coloring="fill"),
ncontours=14,
showscale=True,
colorbar=dict(
title=dict(text="Density", font=dict(size=20, color=INK)),
tickfont=dict(size=16, color=INK_SOFT),
len=0.8,
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
),
line=dict(width=2, color="rgba(0,0,0,0.2)"),
hovertemplate="<b>Density Region</b><br>Height: %{x:.1f} cm<br>Weight: %{y:.1f} kg<extra></extra>",
)
)
# Add scatter points for context (semi-transparent, interactive)
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode="markers",
marker=dict(
size=6,
color="#009E73", # Okabe-Ito position 1 (brand color, theme-independent)
opacity=0.25,
line=dict(width=0),
),
showlegend=False,
name="Individual measurements",
hovertemplate="<b>Measurement</b><br>Height: %{x:.1f} cm<br>Weight: %{y:.1f} kg<extra></extra>",
)
)
# Update layout with theme-adaptive styling
fig.update_layout(
title=dict(text="contour-density · plotly · pyplots.ai", font=dict(size=32, color=INK), x=0.5, xanchor="center"),
xaxis=dict(
title=dict(text="Height (cm)", font=dict(size=24, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
showgrid=True,
gridcolor=GRID,
gridwidth=1,
linecolor=INK_SOFT,
zerolinecolor=INK_SOFT,
),
yaxis=dict(
title=dict(text="Weight (kg)", font=dict(size=24, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
showgrid=True,
gridcolor=GRID,
gridwidth=1,
linecolor=INK_SOFT,
zerolinecolor=INK_SOFT,
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font=dict(color=INK),
width=1600,
height=900,
margin=dict(l=100, r=120, t=100, b=100),
hovermode="closest",
)
# Save as PNG and HTML with theme-suffixed filenames
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Density Contour Plot on anyplot.ai.