Mosaic Plot for Categorical Association Analysis — Altair

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 Altair

Python source (Altair)

""" anyplot.ai
mosaic-categorical: Mosaic Plot for Categorical Association Analysis
Library: altair 6.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-19
"""

import os
import sys


# Work around filename shadowing the altair library
sys.path.pop(0)

import altair as alt
import pandas as pd


# 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 positions 1 and 2
COLOR_SURVIVED = "#009E73"
COLOR_NOT_SURVIVED = "#C475FD"

# Data: Titanic survival by passenger class (contingency table)
data = pd.DataFrame(
    {
        "Class": ["1st", "1st", "2nd", "2nd", "3rd", "3rd", "Crew", "Crew"],
        "Survival": [
            "Survived",
            "Did Not Survive",
            "Survived",
            "Did Not Survive",
            "Survived",
            "Did Not Survive",
            "Survived",
            "Did Not Survive",
        ],
        "Count": [203, 122, 118, 167, 178, 528, 212, 673],
    }
)

# Calculate mosaic layout: widths from marginal proportions, heights from conditional proportions
class_totals = data.groupby("Class")["Count"].sum().reset_index()
class_totals.columns = ["Class", "ClassTotal"]
total = class_totals["ClassTotal"].sum()
class_totals["Width"] = class_totals["ClassTotal"] / total

class_order = ["1st", "2nd", "3rd", "Crew"]
class_totals["ClassOrder"] = class_totals["Class"].map({c: i for i, c in enumerate(class_order)})
class_totals = class_totals.sort_values("ClassOrder")
class_totals["x_start"] = class_totals["Width"].cumsum() - class_totals["Width"]
class_totals["x_end"] = class_totals["Width"].cumsum()
class_totals["x_mid"] = (class_totals["x_start"] + class_totals["x_end"]) / 2

data = data.merge(class_totals[["Class", "Width", "x_start", "x_end", "x_mid", "ClassTotal"]], on="Class")
data["Height"] = data["Count"] / data["ClassTotal"]

survival_order = {"Survived": 0, "Did Not Survive": 1}
data["SurvivalOrder"] = data["Survival"].map(survival_order)
data = data.sort_values(["Class", "SurvivalOrder"])

y_positions = []
for cls in class_order:
    cls_data = data[data["Class"] == cls].sort_values("SurvivalOrder")
    cumsum = 0.0
    for idx in cls_data.index:
        y_positions.append({"index": idx, "y_start": cumsum, "y_end": cumsum + data.loc[idx, "Height"]})
        cumsum += data.loc[idx, "Height"]

y_df = pd.DataFrame(y_positions).set_index("index")
data["y_start"] = data.index.map(y_df["y_start"])
data["y_end"] = data.index.map(y_df["y_end"])
data["y_mid"] = (data["y_start"] + data["y_end"]) / 2
data["Percentage"] = (data["Count"] / total * 100).round(1)

# Plot: mosaic rectangles with axis titles for both categorical dimensions
mosaic = (
    alt.Chart(data)
    .mark_rect(stroke=PAGE_BG, strokeWidth=3)
    .encode(
        x=alt.X("x_start:Q", axis=alt.Axis(title="Passenger Class", labels=False, ticks=False, domain=False)),
        x2=alt.X2("x_end:Q"),
        y=alt.Y("y_start:Q", axis=alt.Axis(title="Proportion", labels=False, ticks=False, domain=False)),
        y2=alt.Y2("y_end:Q"),
        color=alt.Color(
            "Survival:N",
            scale=alt.Scale(domain=["Survived", "Did Not Survive"], range=[COLOR_SURVIVED, COLOR_NOT_SURVIVED]),
            legend=alt.Legend(
                title="Survival Status", titleFontSize=20, labelFontSize=18, orient="right", symbolSize=400
            ),
        ),
        tooltip=["Class:N", "Survival:N", "Count:Q", "Percentage:Q"],
    )
)

# Count labels — white on green (survived), theme ink on vermillion (did not survive)
labels = (
    alt.Chart(data)
    .mark_text(fontSize=22, fontWeight="bold", align="center", baseline="middle")
    .encode(
        x=alt.X("x_mid:Q"),
        y=alt.Y("y_mid:Q"),
        text=alt.Text("Count:Q"),
        color=alt.condition(alt.datum.Survival == "Survived", alt.value("white"), alt.value(INK)),
    )
)

# Class name labels at top of each column
class_labels_df = class_totals[["Class", "x_mid"]].copy()
class_labels = (
    alt.Chart(class_labels_df)
    .mark_text(fontSize=20, fontWeight="bold", baseline="top", dy=15, color=INK)
    .encode(x=alt.X("x_mid:Q"), y=alt.value(1.0), text="Class:N")
)

# Combine layers with theme-adaptive chrome
chart = (
    alt.layer(mosaic, labels, class_labels)
    .properties(
        width=1600,
        height=900,
        background=PAGE_BG,
        title=alt.Title("mosaic-categorical · python · altair · anyplot.ai", fontSize=28, anchor="middle"),
    )
    .configure_view(fill=PAGE_BG, strokeWidth=0)
    .configure_axis(
        domainColor=INK_SOFT,
        tickColor=INK_SOFT,
        gridColor=INK,
        gridOpacity=0.10,
        labelColor=INK_SOFT,
        titleColor=INK,
        titleFontSize=22,
    )
    .configure_title(color=INK)
    .configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
)

# Save outputs
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")

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

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