{"spec_id":"bar-grouped","library":"plotnine","language":"python","code":"\"\"\" anyplot.ai\nbar-grouped: Grouped Bar Chart\nLibrary: plotnine 0.15.7 | Python 3.13.14\nQuality: 93/100 | Updated: 2026-08-05\n\"\"\"\n\nimport os\n\nimport pandas as pd\nfrom plotnine import (\n    aes,\n    element_blank,\n    element_line,\n    element_rect,\n    element_text,\n    geom_col,\n    geom_text,\n    ggplot,\n    ggsave,\n    labs,\n    position_dodge,\n    scale_fill_manual,\n    scale_y_continuous,\n    theme,\n)\n\n\n# Theme tokens (see prompts/default-style-guide.md \"Theme-adaptive Chrome\")\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\n\n# Imprint palette (first series always #009E73)\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\"]\n\n# Data - Regional sales across product categories, ordered by total revenue\n# so the chart reads as a declining performance story left to right\nregions = [\"North America\", \"Europe\", \"Asia Pacific\", \"Latin America\", \"Middle East\"]\ndata = {\n    \"Region\": regions * 3,\n    \"Category\": [\"Electronics\"] * 5 + [\"Apparel\"] * 5 + [\"Home Goods\"] * 5,\n    \"Revenue\": [145, 118, 162, 68, 52, 78, 95, 54, 42, 31, 62, 71, 48, 35, 28],\n}\ndf = pd.DataFrame(data)\n\n# Order regions by total revenue (descending) and categories to match the Imprint order\ndf[\"Region\"] = pd.Categorical(df[\"Region\"], categories=regions, ordered=True)\ndf[\"Category\"] = pd.Categorical(df[\"Category\"], categories=[\"Electronics\", \"Apparel\", \"Home Goods\"], ordered=True)\n\n# Highlight the single top-performing region/category pairing as the chart's focal point\ntop = df.loc[df[\"Revenue\"].idxmax()]\nsubtitle = f\"{top.Region} leads all groups at ${top.Revenue:.0f}M in {top.Category}\"\n\n# Theme-adaptive chrome\nanyplot_theme = theme(\n    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n    panel_background=element_rect(fill=PAGE_BG),\n    panel_border=element_blank(),\n    axis_line_x=element_line(color=INK_SOFT, size=0.8),\n    axis_line_y=element_line(color=INK_SOFT, size=0.8),\n    axis_ticks_major=element_blank(),\n    panel_grid_major_x=element_blank(),\n    panel_grid_minor=element_blank(),\n    panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.12),\n    axis_title=element_text(size=10, color=INK),\n    axis_text=element_text(size=8, color=INK_SOFT),\n    plot_title=element_text(size=12, color=INK),\n    plot_subtitle=element_text(size=9, color=INK_SOFT),\n    legend_background=element_rect(fill=ELEVATED_BG, color=ELEVATED_BG),\n    legend_key=element_rect(fill=ELEVATED_BG, color=ELEVATED_BG),\n    legend_text=element_text(size=8, color=INK_SOFT),\n    legend_title=element_text(size=9, color=INK),\n    figure_size=(8, 4.5),\n)\n\n# Plot\nplot = (\n    ggplot(df, aes(x=\"Region\", y=\"Revenue\", fill=\"Category\"))\n    + geom_col(position=position_dodge(width=0.75), width=0.65)\n    + geom_text(\n        aes(label=\"Revenue\"),\n        position=position_dodge(width=0.75),\n        format_string=\"${:.0f}\",\n        va=\"bottom\",\n        size=2.8,\n        color=INK_SOFT,\n    )\n    + scale_fill_manual(values=IMPRINT)\n    + scale_y_continuous(labels=lambda ticks: [f\"${v:.0f}M\" for v in ticks], expand=(0, 0, 0.12, 0))\n    + labs(\n        x=\"Region\",\n        y=\"Revenue ($M)\",\n        title=\"bar-grouped · python · plotnine · anyplot.ai\",\n        subtitle=subtitle,\n        fill=\"Product Category\",\n    )\n    + anyplot_theme\n)\n\n# Save\nggsave(plot, filename=f\"plot-{THEME}.png\", dpi=400, width=8, height=4.5)\n"}