🏅 rank — Interactive Playground

1 · Basic ranking

By default, rankSeries uses method="average" (tied values share the average of their ranks) and ascending=true (smallest value gets rank 1).

2 · Tie-breaking methods

Five methods mirror pandas: average (default), min, max, first, and dense.

3 · Descending rank

Set ascending: false to give rank 1 to the largest value.

4 · Handling NaN/null values

Three strategies mirror pandas na_option: keep (default, NaN in result), top (nulls get lowest ranks), and bottom (nulls get highest ranks).

5 · Percentage rank (pct)

pct: true returns fractional ranks in the range (0, 1].

6 · DataFrame rank by column (axis=0)

rankDataFrame ranks each column independently by default.

7 · DataFrame rank by row (axis=1)

Set axis: 1 to rank each row's values relative to each other.

8 · Dense rank for competition rankings

Dense rank is useful when you want no gaps — e.g., "1st, 2nd, 2nd, 3rd" instead of "1st, 2nd, 2nd, 4th".

API Reference

Rank values along an axis. Use rankSeries for a single Series and rankDataFrame for an entire DataFrame.

// Series
rankSeries(series, {
  method?:    "average" | "min" | "max" | "first" | "dense",
  ascending?: boolean,   // default true  — smallest gets rank 1
  naOption?:  "keep" | "top" | "bottom",
  pct?:       boolean,   // default false — return fractional ranks
}): Series<number>

// DataFrame
rankDataFrame(df, {
  method?:    "average" | "min" | "max" | "first" | "dense",
  ascending?: boolean,
  naOption?:  "keep" | "top" | "bottom",
  pct?:       boolean,
  axis?:      0 | 1,    // 0 = rank each column, 1 = rank each row
}): DataFrame