The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal; 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: seaborn 0.13.2 | Python 3.13.15
Quality: 89/100 | Updated: 2026-09-27
"""
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
# 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 palette — Retained uses the brand green, Churned uses the semantic
# red anchor (bad/loss role), per the style guide's status semantic exception.
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
sns.set_context("notebook", font_scale=1.0)
# Route the two fill colors through seaborn's palette machinery (matplotlib's
# ax.bar draws the bars themselves — sns.barplot can't vary bar width per
# category — but the color lookup for every segment is genuine seaborn usage).
sns.set_palette(IMPRINT)
palette = sns.color_palette()
# Data — customers acquired per channel, six months later split into retained/churned
channels = ["Referral", "Organic Search", "Email", "Direct", "Paid Social"]
retained_counts = np.array([663, 440, 280, 186, 122])
churned_counts = np.array([187, 180, 150, 124, 168])
channel_totals = retained_counts + churned_counts
total_customers = channel_totals.sum()
fill_order = ["Retained", "Churned"]
colors = [palette[0], palette[4]]
widths = channel_totals / total_customers
x_positions = np.cumsum(np.concatenate([[0], widths[:-1]]))
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
for i in range(len(channels)):
proportions = [retained_counts[i] / channel_totals[i], churned_counts[i] / channel_totals[i]]
bottom = 0.0
for j, proportion in enumerate(proportions):
ax.bar(
x=x_positions[i],
height=proportion,
width=widths[i],
bottom=bottom,
color=colors[j],
align="edge",
edgecolor=PAGE_BG,
linewidth=0.8,
)
if proportion > 0.06:
ax.text(
x_positions[i] + widths[i] / 2,
bottom + proportion / 2,
f"{proportion:.0%}",
ha="center",
va="center",
fontsize=11,
color="white",
fontweight="bold",
)
bottom += proportion
# X-axis labels centered under variable-width bars
ax.set_ylim(-0.14, 1.05)
for i, channel in enumerate(channels):
center = x_positions[i] + widths[i] / 2
ax.text(center, -0.05, f"{channel}\n(n={channel_totals[i]})", ha="center", va="top", fontsize=9, color=INK_SOFT)
# Style
title = "Customer Retention by Channel · bar-spine · python · seaborn · anyplot.ai"
title_fontsize = round(12 * min(1.0, 67 / len(title)))
title_fontsize = max(title_fontsize, 8)
ax.set_xlim(0, 1)
ax.set_xticks([])
ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
ax.set_yticklabels(["0%", "25%", "50%", "75%", "100%"], fontsize=8, color=INK_SOFT)
ax.set_ylabel("Proportion of Customers", fontsize=10, color=INK)
ax.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK)
sns.despine(ax=ax, bottom=True)
ax.spines["left"].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
legend_patches = [mpatches.Patch(color=colors[j], label=fill_order[j]) for j in range(len(fill_order))]
ax.legend(
handles=legend_patches, loc="upper right", fontsize=8, frameon=True, facecolor=ELEVATED_BG, edgecolor=INK_SOFT
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bar-spine/seaborn/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": "seaborn",
"page": "https://anyplot.ai/bar-spine/python/seaborn",
"hub": "https://anyplot.ai/bar-spine",
"code_json": "https://api.anyplot.ai/specs/bar-spine/seaborn/code",
"spec_json": "https://api.anyplot.ai/specs/bar-spine",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/seaborn/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/seaborn/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Spine Plot for Two-Variable Proportions on anyplot.ai.