polars.Series.rank

Series.rank(
    method: RankMethod = 'average',
    *,
    descending: bool = False,
    seed: int | None = None,
) → Series

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.

descending

Rank in descending order.

seed

If method="random", use this as seed.

Notes

If you’re coming from SQL, you may be expecting null values to be ranked last. Polars, however, only ranks non-null values and preserves the null ones.

Examples

The ‘average’ method:

>>> s = pl.Series("a", [3, 6, 1, 1, 6])
>>> s.rank()
shape: (5,)
Series: 'a' [f64]
[
    3.0
    4.5
    1.5
    1.5
    4.5
]

The ‘ordinal’ method:

>>> s = pl.Series("a", [3, 6, 1, 1, 6])
>>> s.rank("ordinal")
shape: (5,)
Series: 'a' [u32]
[
    3
    4
    1
    2
    5
]