{"spec_id":"histogram-overlapping","library":"seaborn","language":"python","code":"\"\"\" anyplot.ai\nhistogram-overlapping: Overlapping Histograms\nLibrary: seaborn 0.13.2 | Python 3.13.15\nQuality: 92/100 | Updated: 2026-08-18\n\"\"\"\n\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom scipy.stats import skewnorm\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# Imprint palette — canonical order, first series always #009E73\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\"]\n\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\": 0.15,\n        \"legend.facecolor\": ELEVATED_BG,\n        \"legend.edgecolor\": INK_SOFT,\n    },\n)\n\n# Data - employee response times (ms) by department\n# Marketing carries a mild right skew (occasional slow tickets) so the overlap\n# also demonstrates a shape difference, not just a shift in mean/spread.\nnp.random.seed(42)\ngroup_order = [\"Engineering\", \"Marketing\", \"Sales\"]\nengineering = np.random.normal(450, 80, 200)\nmarketing = skewnorm.rvs(a=4, loc=445, scale=90, size=180, random_state=42)\nsales = np.random.normal(480, 60, 160)\n\ndf = pd.DataFrame(\n    {\n        \"values\": np.concatenate([engineering, marketing, sales]),\n        \"group\": [\"Engineering\"] * len(engineering) + [\"Marketing\"] * len(marketing) + [\"Sales\"] * len(sales),\n    }\n)\n\n# Shared bin edges so all three distributions compare on the same grid\nbin_edges = np.histogram_bin_edges(df[\"values\"], bins=25)\n\n# Create plot\nfig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)\n\n# Idiomatic long-form overlapping histogram: one call, seaborn's hue/multiple machinery\nsns.histplot(\n    data=df,\n    x=\"values\",\n    hue=\"group\",\n    hue_order=group_order,\n    multiple=\"layer\",\n    bins=bin_edges,\n    palette=IMPRINT[:3],\n    alpha=0.55,\n    edgecolor=PAGE_BG,\n    linewidth=0.5,\n    ax=ax,\n)\n\n# Labels and styling\nax.set_xlabel(\"Response Time (ms)\", fontsize=10)\nax.set_ylabel(\"Count\", fontsize=10)\nax.set_title(\"histogram-overlapping · python · seaborn · anyplot.ai\", fontsize=12, fontweight=\"bold\")\nax.tick_params(axis=\"both\", labelsize=8)\n\n# Spines\nsns.despine(ax=ax)\n\n# Grid\nax.set_axisbelow(True)\nax.yaxis.grid(True, linewidth=0.8)\n\n# Legend (seaborn auto-builds it from hue; drop the \"group\" title, restyle to match theme)\nsns.move_legend(ax, \"upper right\", title=None, fontsize=8, frameon=True)\nlegend = ax.get_legend()\nlegend.get_frame().set_alpha(1)\nfor text in legend.get_texts():\n    text.set_color(INK)\n\n# Storytelling: call out the fastest department's average response time\ngroup_means = df.groupby(\"group\")[\"values\"].mean()\nfastest_group = group_means.idxmin()\nfastest_mean = group_means[fastest_group]\nfastest_color = IMPRINT[group_order.index(fastest_group)]\n\nbar_top = ax.get_ylim()[1]\nax.set_ylim(top=bar_top * 1.18)\nax.axvline(fastest_mean, color=fastest_color, linestyle=\"--\", linewidth=1.2, alpha=0.8, ymax=0.82)\nax.annotate(\n    f\"{fastest_group}: fastest avg ({fastest_mean:.0f} ms)\",\n    xy=(fastest_mean, bar_top),\n    xytext=(fastest_mean, bar_top * 1.08),\n    fontsize=8,\n    fontweight=\"bold\",\n    color=fastest_color,\n    ha=\"center\",\n    va=\"bottom\",\n)\n\nplt.tight_layout()\nplt.savefig(f\"plot-{THEME}.png\", dpi=400, facecolor=PAGE_BG)\n"}