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Iteration 221: Add stats/quantile.ts — quantileSeries and quantileDataFrame
Implements pandas Series.quantile() and DataFrame.quantile() with full feature parity: - quantileSeries(series, options): number | Series<Scalar> - q: scalar or array of quantile levels in [0, 1] - interpolation: linear, lower, higher, midpoint, nearest - skipna: true (default) ignores null/NaN - quantileDataFrame(df, options): Series<Scalar> | DataFrame - axis=0 (default): per-column quantiles - axis=1: per-row quantiles - numericOnly: true (default) skips non-numeric columns - multi-q returns DataFrame; scalar q returns Series - 46 unit + 4 property-based tests (fast-check) - monotonicity, q=0/1 min/max invariants, lower<=linear<=higher - playground/quantile.html — 7 interactive demos Metric: 56 (+1 vs 55) Run: https://github.com/githubnext/tsessebe/actions/runs/24299797044 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎playground/quantile.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>tsb — quantile</title>
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<style>
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body { font-family: system-ui, sans-serif; max-width: 900px; margin: 40px auto; padding: 0 20px; background: #f9fafb; color: #1a1a2e; }
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h1 { color: #4f46e5; }
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h2 { color: #374151; border-bottom: 2px solid #e5e7eb; padding-bottom: 6px; }
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.demo { background: white; border: 1px solid #e5e7eb; border-radius: 8px; padding: 20px; margin: 16px 0; }
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pre { background: #1e1e2e; color: #cdd6f4; padding: 16px; border-radius: 6px; overflow-x: auto; font-size: 14px; }
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.output { background: #f0fdf4; border: 1px solid #bbf7d0; border-radius: 6px; padding: 12px; font-family: monospace; white-space: pre; margin-top: 10px; }
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a { color: #4f46e5; }
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table { border-collapse: collapse; margin-top: 8px; }
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th, td { border: 1px solid #e5e7eb; padding: 6px 12px; text-align: right; }
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th { background: #f3f4f6; }
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</style>
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</head>
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<body>
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<p><a href="index.html">← tsb playground</a></p>
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<h1>📐 quantile</h1>
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<p>
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<strong><code>quantileSeries</code></strong> / <strong><code>quantileDataFrame</code></strong> —
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compute quantile(s) / percentile(s), mirroring
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.quantile.html"><code>Series.quantile()</code></a> and
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<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html"><code>DataFrame.quantile()</code></a>.
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</p>
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<p>Equivalent Python: <code>series.quantile(q=0.5)</code> / <code>df.quantile(q=0.5)</code></p>
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<h2>1 · Scalar quantile (median)</h2>
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<div class="demo">
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<pre>const s = new Series({ data: [1, 2, 3, 4, 5] });
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quantileSeries(s); // default q=0.5 → 3
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quantileSeries(s, { q: 0.25 }); // → 2
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quantileSeries(s, { q: 0.75 }); // → 4</pre>
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<div class="output" id="demo1"></div>
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</div>
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<h2>2 · Multiple quantile levels</h2>
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<div class="demo">
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<pre>const s = new Series({ data: [1, 2, 3, 4, 5] });
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const q = quantileSeries(s, { q: [0.25, 0.5, 0.75] });
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// Series indexed by q-values: { 0.25: 2, 0.5: 3, 0.75: 4 }</pre>
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<div class="output" id="demo2"></div>
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</div>
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<h2>3 · Interpolation methods</h2>
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<div class="demo">
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<pre>const s = new Series({ data: [0, 10] });
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// q=0.5 → position 0.5 between indices 0 and 1
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quantileSeries(s, { q: 0.5, interpolation: "linear" }); // 5
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quantileSeries(s, { q: 0.5, interpolation: "lower" }); // 0
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quantileSeries(s, { q: 0.5, interpolation: "higher" }); // 10
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quantileSeries(s, { q: 0.5, interpolation: "midpoint" }); // 5
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quantileSeries(s, { q: 0.5, interpolation: "nearest" }); // 0</pre>
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<div class="output" id="demo3"></div>
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</div>
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<h2>4 · NaN handling (skipna=true by default)</h2>
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<div class="demo">
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<pre>const s = new Series({ data: [1, null, 3, NaN, 5] });
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quantileSeries(s, { q: 0.5 }); // ignores null/NaN → 3
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quantileSeries(s, { q: 0.5, skipna: false }); // NaN propagates → NaN</pre>
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<div class="output" id="demo4"></div>
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</div>
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<h2>5 · DataFrame — axis=0 (per-column quantiles)</h2>
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<div class="demo">
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<pre>const df = DataFrame.fromColumns({ a: [1, 2, 3, 4], b: [10, 20, 30, 40] });
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quantileDataFrame(df, { q: 0.5 });
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// Series { a: 2.5, b: 25 }
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quantileDataFrame(df, { q: [0.25, 0.5, 0.75] });
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// DataFrame 3×2: rows=[0.25, 0.5, 0.75], cols=[a, b]</pre>
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<div class="output" id="demo5"></div>
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</div>
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<h2>6 · DataFrame — axis=1 (per-row quantiles)</h2>
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<div class="demo">
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<pre>const df = DataFrame.fromColumns({ a: [1, 2, 3], b: [3, 4, 5], c: [5, 6, 7] });
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quantileDataFrame(df, { axis: 1, q: 0.5 });
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// Series — median of each row: [3, 4, 5]</pre>
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<div class="output" id="demo6"></div>
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</div>
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<h2>7 · Q=[0, 0.25, 0.5, 0.75, 1] summary table</h2>
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<div class="demo">
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<pre>const df = DataFrame.fromColumns({ score: [55, 70, 80, 88, 92, 95, 99] });
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quantileDataFrame(df, { q: [0, 0.25, 0.5, 0.75, 1] });
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// → summary statistics table</pre>
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<div id="demo7"></div>
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</div>
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<script type="module">
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import { Series, DataFrame, quantileSeries, quantileDataFrame } from "./tsb.js";
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function show(id, val) {
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document.getElementById(id).textContent = JSON.stringify(val, null, 2);
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}
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// Demo 1
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{
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const s = new Series({ data: [1, 2, 3, 4, 5] });
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show("demo1", {
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"q=0.5 (default)": quantileSeries(s),
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"q=0.25": quantileSeries(s, { q: 0.25 }),
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"q=0.75": quantileSeries(s, { q: 0.75 }),
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});
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}
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// Demo 2
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{
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const s = new Series({ data: [1, 2, 3, 4, 5] });
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const result = quantileSeries(s, { q: [0.25, 0.5, 0.75] });
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const idx = [...result.index.values];
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const vals = [...result.values];
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const obj = {};
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for (let i = 0; i < idx.length; i++) obj[idx[i]] = vals[i];
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show("demo2", obj);
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}
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// Demo 3
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{
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const s = new Series({ data: [0, 10] });
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show("demo3", {
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linear: quantileSeries(s, { q: 0.5, interpolation: "linear" }),
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lower: quantileSeries(s, { q: 0.5, interpolation: "lower" }),
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higher: quantileSeries(s, { q: 0.5, interpolation: "higher" }),
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midpoint: quantileSeries(s, { q: 0.5, interpolation: "midpoint" }),
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nearest: quantileSeries(s, { q: 0.5, interpolation: "nearest" }),
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});
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}
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// Demo 4
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{
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const s = new Series({ data: [1, null, 3, NaN, 5] });
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show("demo4", {
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"skipna=true": quantileSeries(s, { q: 0.5 }),
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"skipna=false": quantileSeries(s, { q: 0.5, skipna: false }),
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});
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}
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// Demo 5
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{
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const df = DataFrame.fromColumns({ a: [1, 2, 3, 4], b: [10, 20, 30, 40] });
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const scalar = quantileDataFrame(df, { q: 0.5 });
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const multi = quantileDataFrame(df, { q: [0.25, 0.5, 0.75] });
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show("demo5", {
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"axis=0 single q=0.5": { a: [...scalar.col("a").values][0] ?? scalar.values[0], b: scalar.values[1] },
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"axis=0 multi-q shape": multi.shape,
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"axis=0 multi-q row 0.25 a": multi.col("a").values[0],
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"axis=0 multi-q row 0.5 a": multi.col("a").values[1],
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"axis=0 multi-q row 0.75 a": multi.col("a").values[2],
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});
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}
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// Demo 6
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{
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const df = DataFrame.fromColumns({ a: [1, 2, 3], b: [3, 4, 5], c: [5, 6, 7] });
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const r = quantileDataFrame(df, { axis: 1, q: 0.5 });
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show("demo6", { "axis=1 q=0.5 per row": [...r.values] });
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}
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// Demo 7 — summary table
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{
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const df = DataFrame.fromColumns({ score: [55, 70, 80, 88, 92, 95, 99] });
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const result = quantileDataFrame(df, { q: [0, 0.25, 0.5, 0.75, 1] });
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const table = document.createElement("table");
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const labels = ["min (q=0)", "Q1 (q=0.25)", "median (q=0.5)", "Q3 (q=0.75)", "max (q=1)"];
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const vals = [...result.col("score").values];
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let html = "<thead><tr><th>Statistic</th><th>score</th></tr></thead><tbody>";
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for (let i = 0; i < labels.length; i++) {
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html += `<tr><td>${labels[i]}</td><td>${vals[i]}</td></tr>`;
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}
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html += "</tbody>";
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table.innerHTML = html;
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document.getElementById("demo7").appendChild(table);
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}
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</script>
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</body>
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</html>

‎src/index.ts‎

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AnyAllSeriesOptions,
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AnyAllDataFrameOptions,
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} from "./stats/index.ts";
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export { quantileSeries, quantileDataFrame } from "./stats/index.ts";
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export type {
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QuantileInterpolation,
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QuantileSeriesOptions,
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QuantileDataFrameOptions,
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} from "./stats/index.ts";

‎src/stats/index.ts‎

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AnyAllSeriesOptions,
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AnyAllDataFrameOptions,
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} from "./nunique.ts";
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export { quantileSeries, quantileDataFrame } from "./quantile.ts";
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export type {
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QuantileInterpolation,
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QuantileSeriesOptions,
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QuantileDataFrameOptions,
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} from "./quantile.ts";

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