Mosaic Plot for Categorical Association Analysis — Matplotlib

A mosaic plot visualizes contingency tables by dividing a rectangular area into smaller rectangles whose areas are proportional to cell frequencies. This statistical visualization technique effectively shows relationships and associations between two or more categorical variables, making it easy to identify patterns, dependencies, and deviations from expected frequencies in cross-tabulated data.

Mosaic Plot for Categorical Association Analysis rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
mosaic-categorical: Mosaic Plot for Categorical Association Analysis
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-19
"""

import os
import sys


sys.path = [p for p in sys.path if "implementations" not in p]

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from statsmodels.graphics.mosaicplot import mosaic


# 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"

# Okabe-Ito palette — first series always #009E73
CELL_COLORS = {"Survived": "#009E73", "Did Not Survive": "#C475FD"}

# Data: Titanic passenger survival by class (realistic proportions)
counts = {
    ("First", "Survived"): 136,
    ("First", "Did Not Survive"): 64,
    ("Second", "Survived"): 87,
    ("Second", "Did Not Survive"): 93,
    ("Third", "Survived"): 119,
    ("Third", "Did Not Survive"): 381,
}

# Plot: core mosaic via statsmodels
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

fig, rects = mosaic(
    counts,
    ax=ax,
    gap=0.015,
    properties=lambda key: {"facecolor": CELL_COLORS[key[1]], "edgecolor": PAGE_BG, "linewidth": 3},
    labelizer=lambda key: str(counts[key]),
    title="",
    axes_label=True,
    statistic=False,
)

# Style cell count labels (white for contrast against colored cells)
for text in ax.texts:
    text.set_color("white")
    text.set_fontsize(20)
    text.set_fontweight("bold")

# Style
ax.set_title(
    "Titanic Passenger Survival · mosaic-categorical · python · matplotlib · anyplot.ai",
    fontsize=24,
    fontweight="medium",
    color=INK,
    pad=20,
)
ax.set_xlabel("Passenger Class", fontsize=20, color=INK, labelpad=12)
ax.set_ylabel("Survival Status", fontsize=20, color=INK, labelpad=12)
ax.tick_params(length=0, labelcolor=INK_SOFT, labelsize=14)

for spine in ax.spines.values():
    spine.set_color(INK_SOFT)

# Legend
legend_handles = [
    mpatches.Patch(facecolor=color, edgecolor=INK_SOFT, linewidth=1.5, label=label)
    for label, color in CELL_COLORS.items()
]
leg = ax.legend(handles=legend_handles, loc="upper right", fontsize=16)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)

# Save
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Mosaic Plot for Categorical Association Analysis on anyplot.ai.

Mosaic Plot for Categorical Association Analysis — Matplotlib

Python source (Matplotlib)

Other implementations