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Iteration 68: Add rank() — rankSeries and rankDataFrame
Add src/stats/rank.ts implementing pandas.Series.rank() / DataFrame.rank(): - rankSeries(series, options): assign numerical ranks to Series values - rankDataFrame(df, options): rank columns (axis=0) or rows (axis=1) - Tie methods: average (default), min, max, first, dense - NaN handling: keep (→ NaN), top (lowest ranks), bottom (highest ranks) - Percentage rank: pct=true divides by nValid (keep) or n (top/bottom) Tests: 40+ unit tests + 6 property-based tests (fast-check). Playground: playground/rank.html (8 interactive sections). Metric: 23 → 24 pandas_features_ported. Run: https://github.com/githubnext/tsessebe/actions/runs/24008535770 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎playground/rank.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 — rank</title>
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<script src="playground-runtime.js"></script>
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<style>
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body { font-family: system-ui, sans-serif; max-width: 860px; margin: 2rem auto; padding: 0 1rem; color: #1a1a1a; }
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h1 { font-size: 2rem; margin-bottom: .25rem; }
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.subtitle { color: #555; margin-bottom: 2rem; }
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section { margin-bottom: 2.5rem; }
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h2 { font-size: 1.25rem; border-bottom: 2px solid #e0e0e0; padding-bottom: .35rem; margin-bottom: 1rem; }
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pre { background: #f5f5f5; border-radius: 6px; padding: 1rem; overflow-x: auto; font-size: .875rem; }
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code { font-family: "JetBrains Mono", "Fira Code", monospace; }
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.output { background: #eaf7ea; border-left: 4px solid #4caf50; padding: .75rem 1rem; border-radius: 0 6px 6px 0; margin-top: .5rem; font-size: .875rem; white-space: pre; font-family: monospace; }
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.note { background: #fff8e1; border-left: 4px solid #ffc107; padding: .6rem 1rem; border-radius: 0 6px 6px 0; font-size: .9rem; margin: .75rem 0; }
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nav { margin-bottom: 1.5rem; font-size: .9rem; color: #555; }
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nav a { color: #0366d6; text-decoration: none; }
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nav a:hover { text-decoration: underline; }
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</style>
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</head>
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<body>
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<nav><a href="index.html">← tsb playground</a></nav>
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<h1>rank</h1>
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<p class="subtitle">Assign numerical ranks to values — mirrors <code>pandas.Series.rank()</code> / <code>pandas.DataFrame.rank()</code>.</p>
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<section>
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<h2>1 — Basic ranking</h2>
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<p>By default, <code>rankSeries</code> uses <code>method="average"</code> (tied values share the average of their ranks) and <code>ascending=true</code> (smallest value gets rank 1).</p>
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<pre><code id="ex1-code">import { Series, rankSeries } from "tsb";
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const s = new Series({ data: [3, 1, 4, 1, 5] });
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const r = rankSeries(s);
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console.log([...r.values]); // [3, 1.5, 4, 1.5, 5]
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// 1 appears twice (positions 1 and 3) → average rank (1+2)/2 = 1.5
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</code></pre>
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<div class="output" id="ex1-output">Loading…</div>
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</section>
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<section>
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<h2>2 — Tie-breaking methods</h2>
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<p>Five methods mirror pandas:</p>
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<ul>
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<li><strong>average</strong> (default): tied values share the average rank</li>
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<li><strong>min</strong>: tied values get the lowest rank</li>
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<li><strong>max</strong>: tied values get the highest rank</li>
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<li><strong>first</strong>: breaks ties by order of appearance</li>
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<li><strong>dense</strong>: like min but no rank gaps between distinct values</li>
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</ul>
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<pre><code id="ex2-code">import { Series, rankSeries } from "tsb";
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const s = new Series({ data: [3, 1, 4, 1, 5] });
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console.log([...rankSeries(s, { method: "average" }).values]); // [3, 1.5, 4, 1.5, 5]
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console.log([...rankSeries(s, { method: "min" }).values]); // [3, 1, 4, 1, 5]
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console.log([...rankSeries(s, { method: "max" }).values]); // [3, 2, 4, 2, 5]
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console.log([...rankSeries(s, { method: "first" }).values]); // [3, 1, 4, 2, 5]
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console.log([...rankSeries(s, { method: "dense" }).values]); // [3, 1, 4, 1, 5]
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</code></pre>
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<div class="output" id="ex2-output">Loading…</div>
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</section>
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<section>
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<h2>3 — Descending rank</h2>
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<p>Set <code>ascending: false</code> to give rank 1 to the largest value.</p>
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<pre><code id="ex3-code">import { Series, rankSeries } from "tsb";
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const scores = new Series({ data: [85, 92, 78, 95, 88], index: ["Alice","Bob","Charlie","Dana","Eve"] });
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const rank = rankSeries(scores, { ascending: false });
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console.log([...rank.values]); // [4, 2, 5, 1, 3]
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// Dana (95) gets rank 1, Bob (92) gets rank 2, etc.
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</code></pre>
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<div class="output" id="ex3-output">Loading…</div>
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</section>
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<section>
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<h2>4 — Handling NaN/null values</h2>
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<p>Three strategies mirror pandas <code>na_option</code>:</p>
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<ul>
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<li><strong>keep</strong> (default): missing values → NaN in result</li>
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<li><strong>top</strong>: missing values get the lowest ranks (come first)</li>
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<li><strong>bottom</strong>: missing values get the highest ranks (come last)</li>
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</ul>
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<pre><code id="ex4-code">import { Series, rankSeries } from "tsb";
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const s = new Series({ data: [3, null, 1, null, 2] });
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const keep = rankSeries(s, { naOption: "keep" });
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const top = rankSeries(s, { naOption: "top" });
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const bottom = rankSeries(s, { naOption: "bottom" });
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console.log([...keep.values]); // [3, NaN, 1, NaN, 2]
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console.log([...top.values]); // [5, 1, 3, 2, 4] — nulls rank first
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console.log([...bottom.values]); // [3, 4, 1, 5, 2] — nulls rank last
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</code></pre>
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<div class="output" id="ex4-output">Loading…</div>
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</section>
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<section>
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<h2>5 — Percentage rank (pct)</h2>
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<p><code>pct: true</code> returns fractional ranks in (0, 1].</p>
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<pre><code id="ex5-code">import { Series, rankSeries } from "tsb";
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const s = new Series({ data: [10, 20, 30, 40, 50] });
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const pct = rankSeries(s, { pct: true });
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console.log([...pct.values]); // [0.2, 0.4, 0.6, 0.8, 1.0]
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</code></pre>
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<div class="output" id="ex5-output">Loading…</div>
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</section>
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<section>
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<h2>6 — DataFrame rank by column (axis=0)</h2>
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<p><code>rankDataFrame</code> ranks each column independently by default.</p>
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<pre><code id="ex6-code">import { DataFrame, rankDataFrame } from "tsb";
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const df = DataFrame.fromColumns({
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score: [85, 92, 78],
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time: [12, 8, 15],
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}, { index: ["Alice", "Bob", "Charlie"] });
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const ranked = rankDataFrame(df);
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console.log([...ranked.col("score").values]); // [2, 3, 1]
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console.log([...ranked.col("time").values]); // [2, 1, 3] — lower time = lower rank
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</code></pre>
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<div class="output" id="ex6-output">Loading…</div>
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</section>
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<section>
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<h2>7 — DataFrame rank by row (axis=1)</h2>
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<p>Set <code>axis: 1</code> to rank each row's values relative to each other.</p>
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<pre><code id="ex7-code">import { DataFrame, rankDataFrame } from "tsb";
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const df = DataFrame.fromColumns({
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math: [90, 70, 85],
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english: [80, 95, 75],
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science: [85, 80, 90],
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});
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// For each student, rank their three subject scores
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const ranked = rankDataFrame(df, { axis: 1 });
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console.log([...ranked.col("math").values]); // [3, 1, 2]
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console.log([...ranked.col("english").values]); // [1, 3, 1]
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console.log([...ranked.col("science").values]); // [2, 2, 3]
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</code></pre>
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<div class="output" id="ex7-output">Loading…</div>
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</section>
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<section>
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<h2>8 — Dense rank for competition rankings</h2>
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<p>Dense rank is useful when you want no gaps — e.g., "1st, 2nd, 2nd, 3rd" instead of "1st, 2nd, 2nd, 4th".</p>
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<pre><code id="ex8-code">import { Series, rankSeries } from "tsb";
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const points = new Series({
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data: [100, 85, 85, 72, 72, 60],
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index: ["A", "B", "C", "D", "E", "F"],
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});
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const denseRank = rankSeries(points, { method: "dense", ascending: false });
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console.log([...denseRank.values]); // [1, 2, 2, 3, 3, 4]
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// Tied players share the same position; no position is skipped
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</code></pre>
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<div class="output" id="ex8-output">Loading…</div>
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</section>
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<script>
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window.__tsb_examples__ = [
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{ id: "ex1", code: document.getElementById("ex1-code").textContent },
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{ id: "ex2", code: document.getElementById("ex2-code").textContent },
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{ id: "ex3", code: document.getElementById("ex3-code").textContent },
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{ id: "ex4", code: document.getElementById("ex4-code").textContent },
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{ id: "ex5", code: document.getElementById("ex5-code").textContent },
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{ id: "ex6", code: document.getElementById("ex6-code").textContent },
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{ id: "ex7", code: document.getElementById("ex7-code").textContent },
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{ id: "ex8", code: document.getElementById("ex8-code").textContent },
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];
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</script>
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</body>
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</html>

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