A spine plot (spineplot) is a stacked bar chart where bar widths are proportional to the marginal frequency of one categorical variable and the subdivisions within each bar show the conditional distribution of a second categorical variable. All bars are normalized to the same height (100%), so visual comparison focuses on how the conditional proportions shift across categories. It is a one-dimensional specialization of mosaic plots and excels at revealing associations between two categorical variables in contingency table data.
""" anyplot.ai
bar-spine: Spine Plot for Two-Variable Proportions
Library: altair 6.1.0 | Python 3.13.13
Quality: 90/100 | Created: 2026-05-08
"""
import os
import sys
# Prevent this file from shadowing the installed altair package
_script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p or ".") != _script_dir]
del _script_dir
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"
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Data — technology comfort survey by age group
age_groups = ["18-24", "25-34", "35-44", "45-54", "55-64", "65+"]
comfort_cats = ["Very Comfortable", "Comfortable", "Neutral", "Uncomfortable"]
raw_counts = {
"18-24": [280, 180, 60, 30],
"25-34": [420, 310, 90, 40],
"35-44": [380, 320, 120, 60],
"45-54": [200, 290, 180, 110],
"55-64": [120, 240, 200, 160],
"65+": [60, 150, 170, 200],
}
records = []
for age in age_groups:
for cat, cnt in zip(comfort_cats, raw_counts[age], strict=False):
records.append({"age_group": age, "comfort": cat, "count": cnt})
df = pd.DataFrame(records)
# Marginal totals → proportional bar widths
totals = df.groupby("age_group")["count"].sum().reindex(age_groups)
grand_total = int(totals.sum())
x_widths = totals / grand_total
x_ends = x_widths.cumsum()
x_starts = x_ends - x_widths
x_mids = (x_starts + x_ends) / 2
# Pre-compute rectangle boundaries for each (age_group, comfort) combination
spine_records = []
for age in age_groups:
group = df[df["age_group"] == age].set_index("comfort").reindex(comfort_cats)
total = int(totals[age])
y_acc = 0.0
for cat in comfort_cats:
cnt = int(group.loc[cat, "count"])
prop = cnt / total
y0 = y_acc
y1 = y_acc + prop
spine_records.append(
{
"age_group": age,
"comfort": cat,
"x_start": float(x_starts[age]),
"x_end": float(x_ends[age]),
"x_mid": float(x_mids[age]),
"y_start": y0,
"y_end": y1,
"y_mid": (y0 + y1) / 2,
"proportion": prop,
"count": cnt,
"total": total,
"pct_label": f"{prop:.0%}" if prop >= 0.10 else "",
}
)
y_acc = y1
spine_df = pd.DataFrame(spine_records)
# X-axis tick positions at bar midpoints with custom labels
x_mid_list = [round(float(x_mids[a]), 6) for a in age_groups]
label_expr = " : ".join(
[f"abs(datum.value - {xm}) < 0.01 ? '{age}'" for age, xm in zip(age_groups, x_mid_list, strict=False)] + ["''"]
)
# Spine bars
bars = (
alt.Chart(spine_df)
.mark_rect(stroke=PAGE_BG, strokeWidth=1.5)
.encode(
x=alt.X(
"x_start:Q",
scale=alt.Scale(domain=[0, 1]),
axis=alt.Axis(
values=x_mid_list,
labelExpr=label_expr,
labelAngle=0,
title="Age Group",
titleFontSize=22,
labelFontSize=18,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
grid=False,
tickSize=6,
),
),
x2="x_end:Q",
y=alt.Y(
"y_start:Q",
scale=alt.Scale(domain=[0, 1]),
axis=alt.Axis(
format="%",
title="Proportion of Respondents",
titleFontSize=22,
labelFontSize=18,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
gridColor=INK,
gridOpacity=0.10,
grid=True,
),
),
y2="y_end:Q",
color=alt.Color(
"comfort:N",
scale=alt.Scale(domain=comfort_cats, range=IMPRINT),
legend=alt.Legend(
title="Technology Comfort",
titleFontSize=18,
labelFontSize=16,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
titleColor=INK,
labelColor=INK_SOFT,
orient="right",
padding=10,
),
),
tooltip=[
alt.Tooltip("age_group:N", title="Age Group"),
alt.Tooltip("comfort:N", title="Comfort Level"),
alt.Tooltip("proportion:Q", title="Proportion", format=".1%"),
alt.Tooltip("count:Q", title="Count"),
],
)
)
# Percentage labels inside segments wide enough to fit text
pct_labels = (
alt.Chart(spine_df[spine_df["pct_label"] != ""])
.mark_text(align="center", baseline="middle", fontSize=13, fontWeight="bold", color="white")
.encode(
x=alt.X("x_mid:Q", scale=alt.Scale(domain=[0, 1])),
y=alt.Y("y_mid:Q", scale=alt.Scale(domain=[0, 1])),
text="pct_label:N",
)
)
# Compose and configure
chart = (
alt.layer(bars, pct_labels)
.properties(
width=1600,
height=900,
background=PAGE_BG,
title=alt.TitleParams(
"Technology Comfort by Age Group · bar-spine · altair · anyplot.ai",
fontSize=28,
color=INK,
anchor="start",
offset=20,
),
)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT)
.configure_title(color=INK, fontSize=28)
)
# Save
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Spine Plot for Two-Variable Proportions on anyplot.ai.