{"spec_id":"area-stacked-percent","library":"seaborn","language":"python","code":"\"\"\" anyplot.ai\narea-stacked-percent: 100% Stacked Area Chart\nLibrary: seaborn 0.13.2 | Python 3.13.13\nQuality: 86/100 | Updated: 2026-05-12\n\"\"\"\n\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\n\n# Okabe-Ito palette (positions 1-4 for 4 categories)\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\"]\n\n# Data: Market share evolution of renewable energy sources\nnp.random.seed(42)\nyears = np.arange(2015, 2025)\n\n# Generate synthetic data showing interesting transitions\nsolar = np.array([10, 12, 15, 18, 22, 26, 30, 35, 40, 45])\nwind = np.array([20, 22, 24, 26, 28, 30, 32, 33, 34, 35])\nhydro = np.array([50, 48, 45, 42, 38, 34, 30, 27, 23, 18])\nother = np.array([20, 18, 16, 14, 12, 10, 8, 5, 3, 2])\n\n# Create DataFrame and normalize to 100%\ndf_wide = pd.DataFrame({\"Year\": years, \"Solar\": solar, \"Wind\": wind, \"Hydro\": hydro, \"Other\": other})\n\n# Calculate percentages (normalize to 100%)\ntotal = df_wide[[\"Solar\", \"Wind\", \"Hydro\", \"Other\"]].sum(axis=1)\nfor col in [\"Solar\", \"Wind\", \"Hydro\", \"Other\"]:\n    df_wide[col] = df_wide[col] / total * 100\n\n# Calculate cumulative values for stacking\ndf_wide[\"Other_top\"] = df_wide[\"Other\"]\ndf_wide[\"Hydro_top\"] = df_wide[\"Other\"] + df_wide[\"Hydro\"]\ndf_wide[\"Wind_top\"] = df_wide[\"Other\"] + df_wide[\"Hydro\"] + df_wide[\"Wind\"]\ndf_wide[\"Solar_top\"] = 100\n\ndf_wide[\"Other_bottom\"] = 0\ndf_wide[\"Hydro_bottom\"] = df_wide[\"Other\"]\ndf_wide[\"Wind_bottom\"] = df_wide[\"Other\"] + df_wide[\"Hydro\"]\ndf_wide[\"Solar_bottom\"] = df_wide[\"Other\"] + df_wide[\"Hydro\"] + df_wide[\"Wind\"]\n\n# Configure seaborn theme\nsns.set_theme(\n    style=\"ticks\",\n    rc={\n        \"figure.facecolor\": PAGE_BG,\n        \"axes.facecolor\": PAGE_BG,\n        \"axes.edgecolor\": INK_SOFT,\n        \"axes.labelcolor\": INK,\n        \"text.color\": INK,\n        \"xtick.color\": INK_SOFT,\n        \"ytick.color\": INK_SOFT,\n        \"grid.color\": INK,\n        \"grid.alpha\": 0.10,\n    },\n)\n\n# Create plot\nfig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)\nax.set_facecolor(PAGE_BG)\n\n# Plot stacked areas in order: Solar (brand green), Wind, Hydro, Other\ncategories = [\"Solar\", \"Wind\", \"Hydro\", \"Other\"]\ncolors_map = dict(zip(categories, IMPRINT, strict=True))\n\nfor cat in categories:\n    ax.fill_between(\n        df_wide[\"Year\"],\n        df_wide[f\"{cat}_bottom\"],\n        df_wide[f\"{cat}_top\"],\n        label=cat,\n        color=colors_map[cat],\n        alpha=0.85,\n        linewidth=0.5,\n        edgecolor=colors_map[cat],\n    )\n\n# Style\nax.set_xlabel(\"Year\", fontsize=20, color=INK)\nax.set_ylabel(\"Share (%)\", fontsize=20, color=INK)\nax.set_title(\"area-stacked-percent · seaborn · anyplot.ai\", fontsize=24, color=INK)\nax.tick_params(axis=\"both\", labelsize=16, colors=INK_SOFT)\n\nax.set_ylim(0, 100)\nax.set_xlim(2015, 2024)\n\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nfor spine in (\"left\", \"bottom\"):\n    ax.spines[spine].set_color(INK_SOFT)\n\nax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)\n\nax.legend(loc=\"upper left\", fontsize=16, framealpha=0.95, facecolor=PAGE_BG, edgecolor=INK_SOFT)\n\nplt.tight_layout()\nplt.savefig(f\"plot-{THEME}.png\", dpi=300, bbox_inches=\"tight\", facecolor=PAGE_BG)\n"}