{"spec_id":"violin-split","library":"plotnine","language":"python","code":"\"\"\" anyplot.ai\nviolin-split: Split Violin Plot\nLibrary: plotnine 0.15.4 | Python 3.13.13\nQuality: 88/100 | Updated: 2026-05-08\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom plotnine import (\n    aes,\n    element_line,\n    element_rect,\n    element_text,\n    geom_boxplot,\n    geom_violin,\n    ggplot,\n    labs,\n    scale_fill_manual,\n    theme,\n    theme_minimal,\n)\n\n\n# Theme tokens\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# Okabe-Ito palette - first series is always #009E73 (brand)\nIMPRINT = [\"#009E73\", \"#C475FD\"]\n\n# Data - Employee satisfaction scores before and after training program\nnp.random.seed(42)\n\ncategories = [\"Engineering\", \"Marketing\", \"Sales\", \"Support\"]\n\ndata = []\nfor category in categories:\n    # Generate different distributions for each category and time point\n    if category == \"Engineering\":\n        before = np.random.normal(65, 12, 80)\n        after = np.random.normal(78, 10, 80)\n    elif category == \"Marketing\":\n        before = np.random.normal(58, 15, 100)\n        after = np.random.normal(72, 12, 100)\n    elif category == \"Sales\":\n        before = np.random.normal(70, 10, 90)\n        after = np.random.normal(82, 8, 90)\n    else:  # Support\n        before = np.random.normal(55, 18, 70)\n        after = np.random.normal(75, 14, 70)\n\n    for val in before:\n        data.append({\"category\": category, \"value\": val, \"split_group\": \"Before Training\"})\n    for val in after:\n        data.append({\"category\": category, \"value\": val, \"split_group\": \"After Training\"})\n\ndf = pd.DataFrame(data)\n\n# Clip values to realistic range (0-100 for satisfaction scores)\ndf[\"value\"] = df[\"value\"].clip(0, 100)\n\n# Plot - True split violin with left-right style for side-by-side halves\nplot = (\n    ggplot(df, aes(x=\"category\", y=\"value\", fill=\"split_group\"))\n    + geom_violin(style=\"left-right\", alpha=0.8, size=0.8, scale=\"width\", trim=True)\n    + geom_boxplot(width=0.15, alpha=0.9, outlier_alpha=0.5, outlier_size=2, size=0.6, position=\"identity\")\n    + scale_fill_manual(values=IMPRINT)\n    + labs(x=\"Department\", y=\"Satisfaction Score (0-100)\", fill=\"Period\", title=\"violin-split · plotnine · anyplot.ai\")\n    + theme_minimal()\n    + theme(\n        figure_size=(16, 9),\n        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n        panel_background=element_rect(fill=PAGE_BG),\n        panel_grid_major_y=element_line(color=INK, alpha=0.10, size=0.3),\n        panel_grid_major_x=element_line(color=INK, alpha=0.05, size=0.2),\n        panel_border=element_rect(color=INK_SOFT, fill=None),\n        axis_title_x=element_text(size=20, color=INK),\n        axis_title_y=element_text(size=20, color=INK),\n        axis_text_x=element_text(size=16, color=INK_SOFT),\n        axis_text_y=element_text(size=16, color=INK_SOFT),\n        axis_line=element_line(color=INK_SOFT),\n        plot_title=element_text(size=24, color=INK, ha=\"center\"),\n        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),\n        legend_title=element_text(size=18, color=INK),\n        legend_text=element_text(size=16, color=INK_SOFT),\n        legend_position=\"right\",\n    )\n)\n\n# Save\nplot.save(f\"plot-{THEME}.png\", dpi=300, verbose=False)\n"}