{"spec_id":"violin-split","library":"plotly","language":"python","code":"\"\"\" anyplot.ai\nviolin-split: Split Violin Plot\nLibrary: plotly 6.7.0 | Python 3.13.13\nQuality: 87/100 | Updated: 2026-05-08\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objects as go\n\n\n# Theme-adaptive colors\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\"\nGRID = \"rgba(26,26,23,0.10)\" if THEME == \"light\" else \"rgba(240,239,232,0.10)\"\n\n# Okabe-Ito palette (first series is always #009E73)\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\", \"#AE3030\", \"#2ABCCD\", \"#954477\"]\n\n# Data - Salary distributions by gender across job categories\nnp.random.seed(42)\n\ncategories = [\"Software Engineering\", \"Product Management\", \"Sales\", \"Operations\"]\nn_per_group = 100\n\ndata = []\nfor category in categories:\n    # Male (left half) - varied distributions\n    if category == \"Software Engineering\":\n        male = np.concatenate(\n            [np.random.normal(95000, 18000, n_per_group // 2), np.random.normal(135000, 15000, n_per_group // 2)]\n        )\n    elif category == \"Product Management\":\n        male = np.random.normal(105000, 20000, n_per_group)\n    elif category == \"Sales\":\n        male = np.concatenate(\n            [\n                np.random.normal(60000, 12000, n_per_group // 3),\n                np.random.normal(95000, 15000, n_per_group // 3),\n                np.random.normal(140000, 18000, n_per_group // 3),\n            ]\n        )\n    else:  # Operations\n        male = np.random.normal(72000, 16000, n_per_group)\n\n    # Female (right half) - varied distributions with different centers\n    if category == \"Software Engineering\":\n        female = np.concatenate(\n            [np.random.normal(92000, 17000, n_per_group // 2), np.random.normal(132000, 16000, n_per_group // 2)]\n        )\n    elif category == \"Product Management\":\n        female = np.random.normal(101000, 21000, n_per_group)\n    elif category == \"Sales\":\n        female = np.concatenate(\n            [\n                np.random.normal(58000, 13000, n_per_group // 3),\n                np.random.normal(92000, 16000, n_per_group // 3),\n                np.random.normal(135000, 19000, n_per_group // 3),\n            ]\n        )\n    else:  # Operations\n        female = np.random.normal(68000, 17000, n_per_group)\n\n    # Clamp to reasonable salary range\n    male = np.clip(male, 40000, 200000)\n    female = np.clip(female, 40000, 200000)\n\n    for val in male:\n        data.append({\"Category\": category, \"Salary\": val, \"Gender\": \"Male\"})\n    for val in female:\n        data.append({\"Category\": category, \"Salary\": val, \"Gender\": \"Female\"})\n\ndf = pd.DataFrame(data)\n\n# Create split violin plot\nfig = go.Figure()\n\n# Add traces for each gender\nfor gender, okabe_idx in [(\"Male\", 0), (\"Female\", 1)]:\n    subset = df[df[\"Gender\"] == gender]\n    side = \"negative\" if gender == \"Male\" else \"positive\"\n\n    fig.add_trace(\n        go.Violin(\n            x=subset[\"Category\"],\n            y=subset[\"Salary\"],\n            name=gender,\n            side=side,\n            line_color=IMPRINT[okabe_idx],\n            fillcolor=IMPRINT[okabe_idx],\n            opacity=0.75,\n            meanline_visible=True,\n            meanline_color=INK_SOFT,\n            points=False,\n            scalemode=\"width\",\n            width=0.9,\n        )\n    )\n\n# Update layout with theme-adaptive styling\nfig.update_layout(\n    title=dict(text=\"violin-split · plotly · pyplots.ai\", font=dict(size=28, color=INK), x=0.5, xanchor=\"center\"),\n    xaxis=dict(\n        title=dict(text=\"Job Category\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        gridcolor=GRID,\n        linecolor=INK_SOFT,\n        zerolinecolor=INK_SOFT,\n    ),\n    yaxis=dict(\n        title=dict(text=\"Annual Salary ($)\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        gridcolor=GRID,\n        linewidth=0.5,\n        linecolor=INK_SOFT,\n        zerolinecolor=INK_SOFT,\n    ),\n    legend=dict(\n        title=dict(text=\"Gender\", font=dict(size=18, color=INK)),\n        bgcolor=ELEVATED_BG,\n        bordercolor=INK_SOFT,\n        borderwidth=1,\n        font=dict(size=16, color=INK_SOFT),\n        orientation=\"h\",\n        yanchor=\"bottom\",\n        y=1.02,\n        xanchor=\"center\",\n        x=0.5,\n    ),\n    paper_bgcolor=PAGE_BG,\n    plot_bgcolor=PAGE_BG,\n    violingap=0.15,\n    violinmode=\"overlay\",\n    margin=dict(l=100, r=60, t=120, b=80),\n    font=dict(family=\"sans-serif\", color=INK),\n)\n\n# Save as PNG and HTML\nfig.write_image(f\"plot-{THEME}.png\", width=1600, height=900, scale=3)\nfig.write_html(f\"plot-{THEME}.html\", include_plotlyjs=\"cdn\")\n"}