{"spec_id":"frequency-polygon-basic","library":"seaborn","language":"python","code":"\"\"\" anyplot.ai\nfrequency-polygon-basic: Frequency Polygon for Distribution Comparison\nLibrary: seaborn 0.13.2 | Python 3.13.13\nQuality: 95/100 | Updated: 2026-05-17\n\"\"\"\n\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\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 always #009E73\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\"]\n\n# Data - Test scores by class\nnp.random.seed(42)\nn_per_group = 200\n\n# Class A: well-distributed performance centered around 75\nclass_a = np.random.normal(loc=75, scale=12, size=n_per_group)\n\n# Class B: higher achieving class centered around 82\nclass_b = np.random.normal(loc=82, scale=10, size=n_per_group)\n\n# Class C: bimodal - mix of high performers and struggling students\nclass_c = np.concatenate(\n    [\n        np.random.normal(loc=70, scale=11, size=n_per_group // 2),\n        np.random.normal(loc=88, scale=9, size=n_per_group // 2),\n    ]\n)\n\n# Align bin edges across all groups for accurate comparison\nall_scores = np.concatenate([class_a, class_b, class_c])\nbins = np.linspace(max(0, all_scores.min() - 5), min(100, all_scores.max() + 5), 20)\nbin_centers = (bins[:-1] + bins[1:]) / 2\n\n# Compute frequencies for each class (extend to zero at ends for closed polygon)\nclass_a_counts, _ = np.histogram(class_a, bins=bins)\nclass_a_x = np.concatenate([[bins[0]], bin_centers, [bins[-1]]])\nclass_a_y = np.concatenate([[0], class_a_counts, [0]])\n\nclass_b_counts, _ = np.histogram(class_b, bins=bins)\nclass_b_x = np.concatenate([[bins[0]], bin_centers, [bins[-1]]])\nclass_b_y = np.concatenate([[0], class_b_counts, [0]])\n\nclass_c_counts, _ = np.histogram(class_c, bins=bins)\nclass_c_x = np.concatenate([[bins[0]], bin_centers, [bins[-1]]])\nclass_c_y = np.concatenate([[0], class_c_counts, [0]])\n\n# Create figure with theme-aware styling\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        \"legend.facecolor\": ELEVATED_BG,\n        \"legend.edgecolor\": INK_SOFT,\n    },\n)\n\nfig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)\nax.set_facecolor(PAGE_BG)\n\n# Plot frequency polygons using seaborn's lineplot\n# Class A - Okabe-Ito position 1 (brand green)\nsns.lineplot(\n    x=class_a_x,\n    y=class_a_y,\n    ax=ax,\n    linewidth=3,\n    color=IMPRINT[0],\n    label=\"Class A\",\n    marker=\"o\",\n    markersize=8,\n    markevery=slice(1, -1),\n)\nax.fill_between(class_a_x, class_a_y, alpha=0.15, color=IMPRINT[0])\n\n# Class B - Okabe-Ito position 2 (vermillion)\nsns.lineplot(\n    x=class_b_x,\n    y=class_b_y,\n    ax=ax,\n    linewidth=3,\n    color=IMPRINT[1],\n    label=\"Class B\",\n    marker=\"s\",\n    markersize=8,\n    markevery=slice(1, -1),\n)\nax.fill_between(class_b_x, class_b_y, alpha=0.15, color=IMPRINT[1])\n\n# Class C - Okabe-Ito position 3 (blue)\nsns.lineplot(\n    x=class_c_x,\n    y=class_c_y,\n    ax=ax,\n    linewidth=3,\n    color=IMPRINT[2],\n    label=\"Class C\",\n    marker=\"^\",\n    markersize=8,\n    markevery=slice(1, -1),\n)\nax.fill_between(class_c_x, class_c_y, alpha=0.15, color=IMPRINT[2])\n\n# Styling\nax.set_xlabel(\"Test Score\", fontsize=20, color=INK)\nax.set_ylabel(\"Frequency\", fontsize=20, color=INK)\nax.set_title(\"frequency-polygon-basic · seaborn · anyplot.ai\", fontsize=24, fontweight=\"medium\", color=INK)\nax.tick_params(axis=\"both\", labelsize=16, colors=INK_SOFT)\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)\nax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)\nax.legend(fontsize=16, loc=\"upper right\", framealpha=0.95, edgecolor=INK_SOFT)\nax.set_ylim(bottom=0)\n\nplt.tight_layout()\nplt.savefig(f\"plot-{THEME}.png\", dpi=300, bbox_inches=\"tight\", facecolor=PAGE_BG)\n"}