polars.Expr.rank#

Expr.rank(method: RankMethod = 'average', reverse: bool = False) → Expr[source]#

Assign ranks to data, dealing with ties appropriately.

Parameters:
method{‘average’, ‘min’, ‘max’, ‘dense’, ‘ordinal’, ‘random’}

The method used to assign ranks to tied elements. The following methods are available (default is ‘average’):

  • ‘average’ : The average of the ranks that would have been assigned to all the tied values is assigned to each value.

  • ‘min’ : The minimum of the ranks that would have been assigned to all the tied values is assigned to each value. (This is also referred to as “competition” ranking.)

  • ‘max’ : The maximum of the ranks that would have been assigned to all the tied values is assigned to each value.

  • ‘dense’ : Like ‘min’, but the rank of the next highest element is assigned the rank immediately after those assigned to the tied elements.

  • ‘ordinal’ : All values are given a distinct rank, corresponding to the order that the values occur in the Series.

  • ‘random’ : Like ‘ordinal’, but the rank for ties is not dependent on the order that the values occur in the Series.

reverse

Reverse the operation.

Examples

The ‘average’ method:

>>> df = pl.DataFrame({"a": [3, 6, 1, 1, 6]})
>>> df.select(pl.col("a").rank())
shape: (5, 1)
┌─────┐
│ a   │
│ --- │
│ f32 │
╞═════╡
│ 3.0 │
├╌╌╌╌╌┤
│ 4.5 │
├╌╌╌╌╌┤
│ 1.5 │
├╌╌╌╌╌┤
│ 1.5 │
├╌╌╌╌╌┤
│ 4.5 │
└─────┘

The ‘ordinal’ method:

>>> df = pl.DataFrame({"a": [3, 6, 1, 1, 6]})
>>> df.select(pl.col("a").rank("ordinal"))
shape: (5, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 3   │
├╌╌╌╌╌┤
│ 4   │
├╌╌╌╌╌┤
│ 1   │
├╌╌╌╌╌┤
│ 2   │
├╌╌╌╌╌┤
│ 5   │
└─────┘