Grouped Bar Chart — plotnine

A grouped bar chart that displays multiple bars side-by-side for each category, enabling direct comparison of values across different groups within the same categorical dimension. This visualization excels at showing how different groups perform relative to each other across multiple categories, making patterns and differences immediately apparent. Grouped bar charts are essential for comparative analysis where you need to track multiple series against the same set of categories.

Grouped Bar Chart rendered with plotnine

Renders

Python source (plotnine)

""" anyplot.ai
bar-grouped: Grouped Bar Chart
Library: plotnine 0.15.7 | Python 3.13.14
Quality: 93/100 | Updated: 2026-08-05
"""

import os

import pandas as pd
from plotnine import (
    aes,
    element_blank,
    element_line,
    element_rect,
    element_text,
    geom_col,
    geom_text,
    ggplot,
    ggsave,
    labs,
    position_dodge,
    scale_fill_manual,
    scale_y_continuous,
    theme,
)


# Theme tokens (see prompts/default-style-guide.md "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"

# Imprint palette (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]

# Data - Regional sales across product categories, ordered by total revenue
# so the chart reads as a declining performance story left to right
regions = ["North America", "Europe", "Asia Pacific", "Latin America", "Middle East"]
data = {
    "Region": regions * 3,
    "Category": ["Electronics"] * 5 + ["Apparel"] * 5 + ["Home Goods"] * 5,
    "Revenue": [145, 118, 162, 68, 52, 78, 95, 54, 42, 31, 62, 71, 48, 35, 28],
}
df = pd.DataFrame(data)

# Order regions by total revenue (descending) and categories to match the Imprint order
df["Region"] = pd.Categorical(df["Region"], categories=regions, ordered=True)
df["Category"] = pd.Categorical(df["Category"], categories=["Electronics", "Apparel", "Home Goods"], ordered=True)

# Highlight the single top-performing region/category pairing as the chart's focal point
top = df.loc[df["Revenue"].idxmax()]
subtitle = f"{top.Region} leads all groups at ${top.Revenue:.0f}M in {top.Category}"

# Theme-adaptive chrome
anyplot_theme = theme(
    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_background=element_rect(fill=PAGE_BG),
    panel_border=element_blank(),
    axis_line_x=element_line(color=INK_SOFT, size=0.8),
    axis_line_y=element_line(color=INK_SOFT, size=0.8),
    axis_ticks_major=element_blank(),
    panel_grid_major_x=element_blank(),
    panel_grid_minor=element_blank(),
    panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.12),
    axis_title=element_text(size=10, color=INK),
    axis_text=element_text(size=8, color=INK_SOFT),
    plot_title=element_text(size=12, color=INK),
    plot_subtitle=element_text(size=9, color=INK_SOFT),
    legend_background=element_rect(fill=ELEVATED_BG, color=ELEVATED_BG),
    legend_key=element_rect(fill=ELEVATED_BG, color=ELEVATED_BG),
    legend_text=element_text(size=8, color=INK_SOFT),
    legend_title=element_text(size=9, color=INK),
    figure_size=(8, 4.5),
)

# Plot
plot = (
    ggplot(df, aes(x="Region", y="Revenue", fill="Category"))
    + geom_col(position=position_dodge(width=0.75), width=0.65)
    + geom_text(
        aes(label="Revenue"),
        position=position_dodge(width=0.75),
        format_string="${:.0f}",
        va="bottom",
        size=2.8,
        color=INK_SOFT,
    )
    + scale_fill_manual(values=IMPRINT)
    + scale_y_continuous(labels=lambda ticks: [f"${v:.0f}M" for v in ticks], expand=(0, 0, 0.12, 0))
    + labs(
        x="Region",
        y="Revenue ($M)",
        title="bar-grouped · python · plotnine · anyplot.ai",
        subtitle=subtitle,
        fill="Product Category",
    )
    + anyplot_theme
)

# Save
ggsave(plot, filename=f"plot-{THEME}.png", dpi=400, width=8, height=4.5)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bar-grouped/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-grouped",
  "language": "python",
  "library": "plotnine",
  "page": "https://anyplot.ai/bar-grouped/python/plotnine",
  "hub": "https://anyplot.ai/bar-grouped",
  "code_json": "https://api.anyplot.ai/specs/bar-grouped/plotnine/code",
  "spec_json": "https://api.anyplot.ai/specs/bar-grouped",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-grouped/python/plotnine/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-grouped/python/plotnine/plot-dark.png",
  "quality_score": 93.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

Part of Grouped Bar Chart on anyplot.ai.

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