The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, 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: pygal 3.1.3 | Python 3.13.15
Quality: 91/100 | Updated: 2026-09-27
"""
import os
import sys
# Remove current directory from path to avoid collision with this filename
_cwd = sys.path[0] if sys.path[0] else "."
if _cwd in sys.path:
sys.path.remove(_cwd)
import pygal
from pygal.style import Style
sys.path.insert(0, _cwd)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")
# Data: Titanic passenger survival by passenger class
class_names = ["1st Class", "2nd Class", "3rd Class"]
survived_counts = [200, 119, 181]
not_survived_counts = [123, 158, 528]
class_totals = [s + n for s, n in zip(survived_counts, not_survived_counts, strict=True)]
grand_total = sum(class_totals)
# Bar widths proportional to marginal (class) frequencies
widths = [t / grand_total for t in class_totals]
x_ranges = []
cumulative = 0.0
for w in widths:
x_ranges.append((cumulative, cumulative + w))
cumulative += w
# Conditional proportions within each bar (spine plot segments)
survive_props = [s / t for s, t in zip(survived_counts, class_totals, strict=True)]
not_survive_props = [1.0 - sp for sp in survive_props]
# Title fontsize scales with title length off the 67-char mandated baseline
TITLE = "Titanic Survival by Passenger Class · bar-spine · python · pygal · anyplot.ai"
title_font_size = round(66 * min(1.0, 67 / len(TITLE)))
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK_SOFT,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=IMPRINT,
opacity=1,
stroke_opacity=1,
stroke_width=1.5,
# Serif title distinguishes the headline from the monospace data labels
# below, rather than leaving every text element in the library-default face.
title_font_family='Georgia, "Times New Roman", serif',
title_font_size=title_font_size,
label_font_size=56,
major_label_font_size=44,
legend_font_size=44,
value_font_size=36,
)
chart = pygal.Histogram(
style=custom_style,
width=3200,
height=1800,
title=TITLE,
y_title="Survival Rate (%)",
show_legend=True,
show_x_guides=False,
show_y_guides=True,
legend_at_bottom=True,
legend_at_bottom_columns=2,
print_values=True,
print_values_position="top",
truncate_label=-1,
)
# Spine plot using overlapping Histogram bars:
# Series 1 "Survived" (Imprint green #009E73): full-height background bar (value=1.0)
# Series 2 "Not Survived" (Imprint lavender #C475FD): overlay bar from y=0 to not_survive_prop
# → lavender covers the bottom portion; green is visible at top (survive_prop)
# opacity=1 keeps the overlay fully solid so the covered green never bleeds through.
survived_data = [
{"value": (1.0, x_min, x_max), "label": f"{cls} — {sp:.1%} survived"}
for cls, sp, (x_min, x_max) in zip(class_names, survive_props, x_ranges, strict=True)
]
not_survived_data = [
{"value": (nsp, x_min, x_max), "label": f"{cls} — {nsp:.1%} not survived"}
for cls, nsp, (x_min, x_max) in zip(class_names, not_survive_props, x_ranges, strict=True)
]
# print_values_position="top" anchors each series' label just above its own
# rect's top edge. "Survived" spans the full [0, 1] range, so its label lands
# above the plot as a per-bar headline. "Not Survived" only rises to
# not_survive_prop, so its label sits right at the green/purple boundary —
# inside the visible green sliver, next to the segment it describes.
chart.add("Survived", survived_data, formatter=lambda _v, index=None, **_kw: f"{survive_props[index]:.0%} survived")
chart.add(
"Not Survived",
not_survived_data,
formatter=lambda _v, index=None, **_kw: f"{not_survive_props[index]:.0%} not survived",
)
# Y-axis in percentage format
chart.y_labels = [
{"label": "0%", "value": 0},
{"label": "25%", "value": 0.25},
{"label": "50%", "value": 0.50},
{"label": "75%", "value": 0.75},
{"label": "100%", "value": 1.0},
]
# X-axis labels centered under each variable-width bar (spec requirement)
chart.x_labels = [
{"label": f"{cls} ({w:.1%})", "value": (x_min + x_max) / 2}
for cls, w, (x_min, x_max) in zip(class_names, widths, x_ranges, strict=True)
]
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
f.write(chart.render())
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bar-spine/pygal/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": "pygal",
"page": "https://anyplot.ai/bar-spine/python/pygal",
"hub": "https://anyplot.ai/bar-spine",
"code_json": "https://api.anyplot.ai/specs/bar-spine/pygal/code",
"spec_json": "https://api.anyplot.ai/specs/bar-spine",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/pygal/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/pygal/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/pygal/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/bar-spine/python/pygal/plot-dark.html",
"quality_score": 91.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Spine Plot for Two-Variable Proportions on anyplot.ai.