{"spec_id":"swarm-basic","library":"letsplot","language":"python","code":"\"\"\" anyplot.ai\nswarm-basic: Basic Swarm Plot\nLibrary: letsplot 4.11.0 | Python 3.13.14\nQuality: 93/100 | Updated: 2026-07-26\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom lets_plot import (\n    LetsPlot,\n    aes,\n    element_blank,\n    element_line,\n    element_rect,\n    element_text,\n    geom_crossbar,\n    geom_sina,\n    geom_violin,\n    ggplot,\n    ggsave,\n    ggsize,\n    labs,\n    scale_color_manual,\n    scale_fill_manual,\n    theme,\n    theme_minimal,\n)\n\n\nLetsPlot.setup_html()\n\n# Theme-adaptive chrome tokens (Imprint palette + surface 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\nIMPRINT_PALETTE = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\"]\n\n# Data - Performance scores across departments\nnp.random.seed(42)\n\ndepartments = [\"Engineering\", \"Marketing\", \"Sales\", \"Support\"]\nn_per_group = [45, 38, 52, 40]\n\ndata = []\nfor dept, n in zip(departments, n_per_group, strict=True):\n    if dept == \"Engineering\":\n        # Higher scores, moderate spread\n        scores = np.random.normal(82, 8, n)\n    elif dept == \"Marketing\":\n        # Mid-range scores, wider spread\n        scores = np.random.normal(75, 12, n)\n    elif dept == \"Sales\":\n        # Bimodal distribution (high and low performers)\n        scores = np.concatenate([np.random.normal(65, 6, n // 2), np.random.normal(88, 5, n - n // 2)])\n    else:  # Support\n        # Lower average, tight distribution with some outliers\n        scores = np.concatenate([np.random.normal(70, 6, n - 3), [45, 48, 95]])\n\n    scores = np.clip(scores, 0, 100)\n    for score in scores:\n        data.append({\"Department\": dept, \"Performance Score\": score})\n\ndf = pd.DataFrame(data)\n\n# Calculate means for each department\nmeans = df.groupby(\"Department\")[\"Performance Score\"].mean().reset_index()\nmeans.columns = [\"Department\", \"mean\"]\n\ntitle = \"swarm-basic · python · letsplot · anyplot.ai\"\n\n# Plot\nplot = (\n    ggplot(df, aes(x=\"Department\", y=\"Performance Score\"))\n    + geom_violin(aes(fill=\"Department\"), alpha=0.15, trim=False, show_legend=False)\n    + geom_sina(aes(color=\"Department\", fill=\"Department\"), size=4, alpha=0.7, seed=42, scale=\"width\")\n    + geom_crossbar(aes(x=\"Department\", y=\"mean\", ymin=\"mean\", ymax=\"mean\"), data=means, width=0.5, size=1.5, color=INK)\n    + scale_color_manual(values=IMPRINT_PALETTE)\n    + scale_fill_manual(values=IMPRINT_PALETTE)\n    + labs(x=\"Department\", y=\"Performance Score (0-100)\", title=title)\n    + theme_minimal()\n    + theme(\n        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n        panel_border=element_blank(),\n        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),\n        plot_title=element_text(size=16, face=\"bold\", color=INK),\n        axis_title=element_text(size=12, color=INK),\n        axis_text=element_text(size=10, color=INK_SOFT),\n        axis_line=element_line(color=INK_SOFT),\n        legend_position=\"none\",\n        panel_grid_major_x=element_blank(),\n        panel_grid_minor=element_blank(),\n        panel_grid_major_y=element_line(color=INK_SOFT, size=0.3),\n    )\n    + ggsize(800, 450)\n)\n\n# Save PNG (scale=4 gives 3200x1800)\nggsave(plot, f\"plot-{THEME}.png\", path=\".\", scale=4)\n\n# Save HTML for interactive version\nggsave(plot, f\"plot-{THEME}.html\", path=\".\")\n"}