A 100% stacked bar chart displays multiple data series as proportional segments within each bar, where every bar is normalized to sum to 100%. This visualization emphasizes composition and relative proportions rather than absolute values, making it ideal for comparing how different components contribute to a whole across categories. It reveals patterns in distribution and share that might be obscured when absolute values vary significantly between categories.
""" anyplot.ai
bar-stacked-percent: 100% Stacked Bar Chart
Library: plotnine 0.15.8 | Python 3.13.15
Quality: 88/100 | Updated: 2026-08-18
"""
import os
import sys
sys.path = [p for p in sys.path if os.path.abspath(p) != os.getcwd()]
import pandas as pd
from mizani.formatters import percent_format
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_bar,
geom_text,
ggplot,
labs,
position_fill,
scale_color_identity,
scale_fill_manual,
scale_y_continuous,
theme,
theme_minimal,
)
# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
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"
MUTED = "#6B6A63" if THEME == "light" else "#A8A79F" # Imprint semantic anchor: other/rest
# Imprint palette — named competitors take positions 1-3 in canonical order;
# "Others" uses the muted semantic anchor since it is literally the aggregate
# rest-of-market bucket, not a distinct company.
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - smartphone market share by quarter
quarters = ["Q1 2023", "Q2 2023", "Q3 2023", "Q4 2023", "Q1 2024", "Q2 2024"]
companies_ordered = ["Others", "Xiaomi", "Samsung", "Apple"]
apple_share = [23, 21, 20, 22, 21, 20]
samsung_share = [22, 21, 20, 19, 20, 19]
xiaomi_share = [12, 13, 14, 14, 15, 16]
others_share = [43, 45, 46, 45, 44, 45]
data = {
"Quarter": quarters * 4,
"Company": (["Apple"] * 6 + ["Samsung"] * 6 + ["Xiaomi"] * 6 + ["Others"] * 6),
"Share": apple_share + samsung_share + xiaomi_share + others_share,
}
df = pd.DataFrame(data)
# Set categorical ordering for proper display
df["Quarter"] = pd.Categorical(df["Quarter"], categories=quarters, ordered=True)
df["Company"] = pd.Categorical(df["Company"], categories=companies_ordered, ordered=True)
# Color mapping: Apple/Samsung/Xiaomi in canonical Imprint order, Others muted
color_map = {"Others": MUTED, "Xiaomi": IMPRINT[2], "Samsung": IMPRINT[1], "Apple": IMPRINT[0]}
# In-segment percentage labels (DE-03): pick whichever ink extreme has higher
# WCAG contrast against each segment's own fill color, so labels stay legible
# on both the mid-tone brand hues and the theme-adaptive "Others" gray.
LIGHT_INK = "#F0EFE8"
DARK_INK = "#1A1A17"
def _relative_luminance(hex_color):
r, g, b = (int(hex_color[i : i + 2], 16) / 255 for i in (1, 3, 5))
def _linearize(c):
return c / 12.92 if c <= 0.03928 else ((c + 0.055) / 1.055) ** 2.4
r, g, b = _linearize(r), _linearize(g), _linearize(b)
return 0.2126 * r + 0.7152 * g + 0.0722 * b
def _contrast_ratio(l1, l2):
lighter, darker = max(l1, l2), min(l1, l2)
return (lighter + 0.05) / (darker + 0.05)
def _label_color(fill_hex):
fill_l = _relative_luminance(fill_hex)
light_contrast = _contrast_ratio(fill_l, _relative_luminance(LIGHT_INK))
dark_contrast = _contrast_ratio(fill_l, _relative_luminance(DARK_INK))
return LIGHT_INK if light_contrast >= dark_contrast else DARK_INK
df["Label"] = df["Share"].astype(str) + "%"
df["LabelColor"] = df["Company"].map(color_map).map(_label_color)
# Precompute label y-positions explicitly (SC-03/VQ-02 fix): geom_bar and
# geom_text each calling their own independent position_fill() can derive
# mismatched per-group cumulative offsets when the source rows are grouped
# by company rather than interleaved per quarter. Instead, compute the exact
# fill-stack midpoint per (Quarter, Company) ourselves -- stacked bottom-to-top
# as Apple/Samsung/Xiaomi/Others, i.e. the reverse of the legend/factor order,
# matching plotnine's default stacking -- and feed it to geom_text via
# position="identity" so both layers are guaranteed to agree.
stack_order_bottom_to_top = ["Apple", "Samsung", "Xiaomi", "Others"]
stack_rank = {company: rank for rank, company in enumerate(stack_order_bottom_to_top)}
# .map() on a Categorical column returns a Categorical result that inherits
# the *original* category order, so sorting by it would sort by category
# position rather than by the mapped rank value -- cast to plain strings
# first so the mapped ranks are ordinary integers.
df["StackRank"] = df["Company"].astype(str).map(stack_rank)
df = df.sort_values(["Quarter", "StackRank"]).reset_index(drop=True)
df["Fraction"] = df["Share"] / 100
cum_top = df.groupby("Quarter", observed=True)["Fraction"].cumsum()
cum_bottom = cum_top - df["Fraction"]
df["LabelY"] = (cum_top + cum_bottom) / 2
# Theme-adaptive chrome
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_border=element_blank(),
panel_grid_major_x=element_blank(),
panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.10),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=10),
axis_text=element_text(color=INK_SOFT, size=8),
axis_line=element_line(color=INK_SOFT, size=0.5),
plot_title=element_text(color=INK, size=12),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_title=element_text(color=INK, size=8),
legend_text=element_text(color=INK_SOFT, size=8),
legend_position="right",
figure_size=(8, 4.5),
)
# Create 100% stacked bar chart with in-segment percentage labels
plot = (
ggplot(df, aes(x="Quarter", y="Share", fill="Company"))
+ geom_bar(stat="identity", position=position_fill(), width=0.7)
+ geom_text(aes(y="LabelY", label="Label", color="LabelColor"), position="identity", size=2.8, show_legend=False)
+ scale_fill_manual(values=color_map)
+ scale_color_identity()
+ scale_y_continuous(labels=percent_format())
+ labs(title="bar-stacked-percent · python · plotnine · anyplot.ai", x="Quarter", y="Market Share", fill="Company")
+ theme_minimal()
+ anyplot_theme
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bar-stacked-percent/plotnine/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-stacked-percent",
"language": "python",
"library": "plotnine",
"page": "https://anyplot.ai/bar-stacked-percent/python/plotnine",
"hub": "https://anyplot.ai/bar-stacked-percent",
"code_json": "https://api.anyplot.ai/specs/bar-stacked-percent/plotnine/code",
"spec_json": "https://api.anyplot.ai/specs/bar-stacked-percent",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-stacked-percent/python/plotnine/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-stacked-percent/python/plotnine/plot-dark.png",
"quality_score": 88.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of 100% Stacked Bar Chart on anyplot.ai.