The same plot in 14 other libraries — Python: Altair, Bokeh, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Spine Plot for Two-Variable Proportions in Python, R, Julia and JavaScript.
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: letsplot 4.11.0 | Python 3.13.15
Quality: 90/100 | Updated: 2026-09-27
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_rect,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
LetsPlot.setup_html()
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", "#AE3030", "#2ABCCD", "#954477"]
ANYPLOT_AMBER = "#DDCC77"
BORDER_COLOR = "rgba(128, 128, 128, 0.35)" # theme-neutral separator, stays visible on both surfaces
# Data: customer churn by subscription tier
tiers = ["Basic", "Standard", "Premium", "Enterprise"]
retained_counts = [120, 400, 320, 180]
churned_counts = [180, 200, 80, 20]
totals = [r + c for r, c in zip(retained_counts, churned_counts, strict=True)]
grand_total = sum(totals)
widths = [t / grand_total for t in totals]
x_starts = np.concatenate([[0.0], np.cumsum(widths[:-1])])
x_ends = np.cumsum(widths)
x_mids = (x_starts + x_ends) / 2
# Build rectangle segments for each tier x status combination
records = []
for i, tier in enumerate(tiers):
total = totals[i]
retain_prop = retained_counts[i] / total
churn_prop = churned_counts[i] / total
records.append(
{
"tier": tier,
"status": "Retained",
"xmin": float(x_starts[i]),
"xmax": float(x_ends[i]),
"ymin": 0.0,
"ymax": retain_prop,
"x_mid": float(x_mids[i]),
"y_mid": retain_prop / 2,
"segment_height": retain_prop,
"label": f"{retain_prop:.0%}",
}
)
records.append(
{
"tier": tier,
"status": "Churned",
"xmin": float(x_starts[i]),
"xmax": float(x_ends[i]),
"ymin": retain_prop,
"ymax": 1.0,
"x_mid": float(x_mids[i]),
"y_mid": retain_prop + churn_prop / 2,
"segment_height": churn_prop,
"label": f"{churn_prop:.0%}",
}
)
df = pd.DataFrame(records)
df_labels = df[df["segment_height"] >= 0.08].copy()
# Enterprise has the lowest churn rate; a benchmark line at that level lets
# every other tier's much taller churned segment be read against it directly.
best_churn_rate = churned_counts[-1] / totals[-1]
benchmark_y = 1.0 - best_churn_rate
benchmark_label = pd.DataFrame(
{
"x": [x_starts[0] + 0.01],
"y": [benchmark_y + 0.035],
"label": [f"Enterprise-level churn ({best_churn_rate:.0%})"],
}
)
# Theme
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid=element_blank(),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
axis_line=element_line(color=INK_SOFT),
axis_ticks=element_blank(),
plot_title=element_text(color=INK, size=16),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=10),
legend_title=element_text(color=INK, size=12),
)
# Plot
plot = (
ggplot(df)
+ geom_rect(aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="status"), color=BORDER_COLOR, size=1.2)
+ geom_hline(yintercept=benchmark_y, linetype="dashed", color=ANYPLOT_AMBER, size=1)
+ geom_text(
data=benchmark_label, mapping=aes(x="x", y="y", label="label"), color=INK, size=4, hjust=0, fontface="bold"
)
+ geom_text(data=df_labels, mapping=aes(x="x_mid", y="y_mid", label="label"), color="#FAFAFA", size=5)
+ scale_fill_manual(values={"Retained": IMPRINT[0], "Churned": IMPRINT[1]}, name="Status")
+ scale_x_continuous(expand=[0, 0], breaks=list(x_mids), labels=tiers)
+ scale_y_continuous(expand=[0, 0], breaks=[0.0, 0.25, 0.5, 0.75, 1.0], labels=["0%", "25%", "50%", "75%", "100%"])
+ labs(x="Subscription Tier", y="Customer Proportion", title="bar-spine · python · letsplot · anyplot.ai")
+ anyplot_theme
+ ggsize(800, 450)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bar-spine/letsplot/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": "bar-spine",
"language": "python",
"library": "letsplot",
"page": "https://anyplot.ai/bar-spine/python/letsplot",
"hub": "https://anyplot.ai/bar-spine",
"code_json": "https://api.anyplot.ai/specs/bar-spine/letsplot/code",
"spec_json": "https://api.anyplot.ai/specs/bar-spine",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/letsplot/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/letsplot/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/letsplot/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/letsplot/plot-dark.html",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Spine Plot for Two-Variable Proportions on anyplot.ai.