Annotated Heatmap — plotnine

A heatmap with numeric values displayed inside each cell, combining color intensity with exact value labels. Essential for correlation matrices, confusion matrices, and any matrix visualization where both pattern recognition and precise values matter. Text color automatically contrasts with background for readability.

Annotated Heatmap rendered with plotnine

Renders

Python source (plotnine)

""" anyplot.ai
heatmap-annotated: Annotated Heatmap
Library: plotnine 0.15.7 | Python 3.13.14
Quality: 91/100 | Updated: 2026-08-05
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    coord_fixed,
    element_blank,
    element_rect,
    element_text,
    geom_text,
    geom_tile,
    ggplot,
    labs,
    scale_color_identity,
    scale_fill_gradient2,
    theme,
    theme_minimal,
)


# Theme tokens (see prompts/default-style-guide.md "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
TEXT_ON_SATURATED = "#F0EFE8"  # readable text over saturated red/blue cells in either theme

# Data: Correlation matrix of economic indicators
np.random.seed(42)
variables = [
    "GDP Growth",
    "Inflation",
    "Unemployment",
    "Interest Rate",
    "Consumer Conf",
    "Mfg Index",
    "Export Vol",
    "Housing",
]

n_vars = len(variables)

# Generate a realistic correlation matrix
base = np.random.randn(n_vars, n_vars)
corr_matrix = np.dot(base, base.T)
d = np.sqrt(np.diag(corr_matrix))
corr_matrix = corr_matrix / d[:, None] / d[None, :]
np.fill_diagonal(corr_matrix, 1.0)
corr_matrix = (corr_matrix + corr_matrix.T) / 2  # Ensure symmetry

# Create DataFrame in long format for plotnine
rows = []
for i, row_var in enumerate(variables):
    for j, col_var in enumerate(variables):
        rows.append({"x": col_var, "y": row_var, "value": corr_matrix[i, j]})

df = pd.DataFrame(rows)

# Convert to categorical to preserve order
df["x"] = pd.Categorical(df["x"], categories=variables, ordered=True)
df["y"] = pd.Categorical(df["y"], categories=variables[::-1], ordered=True)

# Text color must contrast with its own cell: saturated cells get light text,
# near-neutral (midpoint) cells get the theme's own ink color.
df["text_color"] = np.where(df["value"].abs() > 0.5, TEXT_ON_SATURATED, INK)

# Title — length-scaled per prompts/plot-generator.md "Title fontsize must scale with title length"
title = "heatmap-annotated · python · plotnine · anyplot.ai"
title_fontsize = round(12 * min(1.0, 67 / len(title)))

# Create the annotated heatmap using the Imprint diverging colormap (imprint_div)
plot = (
    ggplot(df, aes(x="x", y="y", fill="value"))
    + geom_tile(color=PAGE_BG, size=0.6)
    + geom_text(aes(label="value", color="text_color"), format_string="{:.2f}", size=3.5)
    + scale_fill_gradient2(low="#AE3030", mid=PAGE_BG, high="#4467A3", midpoint=0, limits=(-1, 1), name="Correlation")
    + scale_color_identity()
    + labs(x="Economic Indicator", y="Economic Indicator", title=title)
    + coord_fixed(ratio=1)
    + theme_minimal()
    + theme(
        figure_size=(6, 6),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_blank(),
        panel_grid_minor=element_blank(),
        panel_border=element_blank(),
        plot_title=element_text(size=title_fontsize, color=INK, ha="center"),
        axis_title=element_text(size=10, color=INK),
        axis_text_x=element_text(size=8, color=INK_SOFT, rotation=45, ha="right"),
        axis_text_y=element_text(size=8, color=INK_SOFT),
        axis_ticks=element_blank(),
        legend_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        legend_title=element_text(size=10, color=INK),
        legend_text=element_text(size=8, color=INK_SOFT),
    )
)

# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=6, height=6, units="in")

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-annotated/plotnine/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-annotated",
  "language": "python",
  "library": "plotnine",
  "page": "https://anyplot.ai/heatmap-annotated/python/plotnine",
  "hub": "https://anyplot.ai/heatmap-annotated",
  "code_json": "https://api.anyplot.ai/specs/heatmap-annotated/plotnine/code",
  "spec_json": "https://api.anyplot.ai/specs/heatmap-annotated",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-annotated/python/plotnine/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-annotated/python/plotnine/plot-dark.png",
  "quality_score": 91.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

Part of Annotated Heatmap on anyplot.ai.

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