# polars.Expr.rank#

Expr.rank(
method: RankMethod = 'average',
*,
descending: bool = False,
seed: = None,
) Self[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.

descending

Rank in descending order.

seed

If method=”random”, use this as seed.

Examples

The ‘average’ method:

```>>> df = pl.DataFrame({"a": [3, 6, 1, 1, 6]})
>>> df.select(pl.col("a").rank())
shape: (5, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 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   │
└─────┘
```

Use ‘rank’ with ‘over’ to rank within groups:

```>>> df = pl.DataFrame({"a": [1, 1, 2, 2, 2], "b": [6, 7, 5, 14, 11]})
>>> df.with_columns(pl.col("b").rank().over("a").alias("rank"))
shape: (5, 3)
┌─────┬─────┬──────┐
│ a   ┆ b   ┆ rank │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ i64 ┆ f64  │
╞═════╪═════╪══════╡
│ 1   ┆ 6   ┆ 1.0  │
│ 1   ┆ 7   ┆ 2.0  │
│ 2   ┆ 5   ┆ 1.0  │
│ 2   ┆ 14  ┆ 3.0  │
│ 2   ┆ 11  ┆ 2.0  │
└─────┴─────┴──────┘
```