A heatmap specifically designed to display correlation coefficients between variables, using a diverging color scheme centered at zero. The symmetric matrix visualization makes it easy to identify positive correlations, negative correlations, and independent variables at a glance. Essential for exploratory data analysis, feature engineering, and multicollinearity detection in statistical and machine learning workflows.
""" anyplot.ai
heatmap-correlation: Correlation Matrix Heatmap
Library: altair 6.2.2 | Python 3.13.15
Quality: 92/100 | Updated: 2026-08-18
"""
import os
import altair as alt
import numpy as np
import pandas as pd
from PIL import Image
# Theme tokens (Imprint palette — theme-adaptive chrome)
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 - realistic financial metrics correlation matrix
np.random.seed(42)
variables = ["Revenue", "Profit", "Expenses", "Employees", "Market Cap", "Debt", "Assets", "R&D Spend"]
n = len(variables)
# Latent-factor model ("company scale", "profitability", "leverage") so relationships
# read as economically sensible: bigger companies have more Revenue/Expenses/Employees/
# Assets (scale factor), Profit tracks Market Cap (profitability factor), and Debt scales
# with leverage independent of profitability.
loadings = np.array(
[
[0.82, 0.15, 0.00], # Revenue
[0.20, 0.85, -0.10], # Profit
[0.84, -0.15, 0.05], # Expenses
[0.65, 0.05, 0.00], # Employees
[0.35, 0.82, -0.10], # Market Cap
[0.30, -0.30, 0.78], # Debt
[0.72, 0.10, 0.42], # Assets
[0.42, 0.15, 0.00], # R&D Spend
]
)
idiosyncratic = np.diag(0.30 + 0.15 * np.random.rand(n))
covariance = loadings @ loadings.T + idiosyncratic
D = np.sqrt(np.diag(covariance))
correlation = covariance / np.outer(D, D)
np.fill_diagonal(correlation, 1.0)
correlation = np.round(correlation, 2)
# Convert to long format for Altair, masking the upper triangle to avoid redundancy
rows = [
{"Variable 1": variables[i], "Variable 2": variables[j], "Correlation": correlation[i, j]}
for i in range(n)
for j in range(n)
if i >= j
]
df = pd.DataFrame(rows)
# Highlight strong correlations (|r| > 0.6) with an ink outline for visual hierarchy
strong = (alt.datum.Correlation > 0.6) | (alt.datum.Correlation < -0.6)
title = "heatmap-correlation · python · altair · anyplot.ai"
title_fontsize = round(16 * (67 / len(title) if len(title) > 67 else 1.0))
base = alt.Chart(df).encode(
x=alt.X(
"Variable 1:N",
title="Financial Metrics",
sort=variables,
axis=alt.Axis(
labelAngle=-40,
labelFontSize=11,
labelColor=INK_SOFT,
titleColor=INK,
titleFontSize=13,
titleFontWeight="bold",
grid=False,
),
),
y=alt.Y(
"Variable 2:N",
title="Financial Metrics",
sort=variables,
axis=alt.Axis(
labelFontSize=11, labelColor=INK_SOFT, titleColor=INK, titleFontSize=13, titleFontWeight="bold", grid=False
),
),
)
# Heatmap cells: Imprint diverging colormap centered on zero, fixed -1..1 domain
heatmap = base.mark_rect().encode(
color=alt.Color(
"Correlation:Q",
scale=alt.Scale(domain=[-1, 1], range=["#AE3030", PAGE_BG, "#4467A3"], domainMid=0),
legend=alt.Legend(
title="Correlation",
titleFontSize=13,
titleColor=INK,
labelFontSize=12,
labelColor=INK_SOFT,
gradientLength=180,
gradientThickness=14,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
),
),
stroke=alt.condition(strong, alt.value(INK), alt.value(PAGE_BG)),
strokeWidth=alt.condition(strong, alt.value(2.5), alt.value(1)),
tooltip=[
alt.Tooltip("Variable 1:N", title="X Variable"),
alt.Tooltip("Variable 2:N", title="Y Variable"),
alt.Tooltip("Correlation:Q", title="Correlation", format=".3f"),
],
)
# Correlation value annotations, with contrast-aware text color per cell
text = base.mark_text(fontSize=12, fontWeight="bold").encode(
text=alt.Text("Correlation:Q", format=".2f"), color=alt.condition(strong, alt.value(PAGE_BG), alt.value(INK))
)
chart = (
(heatmap + text)
.properties(
background=PAGE_BG,
width=412,
height=460,
title=alt.Title(title, fontSize=title_fontsize, fontWeight="bold", anchor="middle", color=INK),
)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT, continuousWidth=412, continuousHeight=460)
)
# Hard target: 2400 x 2400 (square). See prompts/library/altair.md "Canvas".
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
TW, TH = 2400, 2400
_img = Image.open(f"plot-{THEME}.png").convert("RGB")
_w, _h = _img.size
if _w > TW or _h > TH:
raise SystemExit(
f"altair vl-convert produced {_w}x{_h}, exceeds target {TW}x{TH}. "
f"Shrink chart .properties(width=, height=) values and re-render."
)
if _w < TW or _h < TH:
_canvas = Image.new("RGB", (TW, TH), PAGE_BG)
_canvas.paste(_img, ((TW - _w) // 2, (TH - _h) // 2))
_canvas.save(f"plot-{THEME}.png")
chart.save(f"plot-{THEME}.html")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-correlation/altair/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "heatmap-correlation",
"language": "python",
"library": "altair",
"page": "https://anyplot.ai/heatmap-correlation/python/altair",
"hub": "https://anyplot.ai/heatmap-correlation",
"code_json": "https://api.anyplot.ai/specs/heatmap-correlation/altair/code",
"spec_json": "https://api.anyplot.ai/specs/heatmap-correlation",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/altair/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/altair/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/altair/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/altair/plot-dark.html",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Correlation Matrix Heatmap on anyplot.ai.