polars.arg_sort_by#

polars.arg_sort_by( exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool | Sequence[bool] = False, multithreaded: bool = True, maintain_order: bool = False, ) → Expr[source]#

Return the row indices that would sort the column(s).

Parameters:

exprs: Column(s) to arg sort by. Accepts expression input. Strings are parsed as column names.
*more_exprs: Additional columns to arg sort by, specified as positional arguments.
descending: Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.
nulls_last: Place null values last.
multithreaded: Sort using multiple threads.
maintain_order: Whether the order should be maintained if elements are equal.

See also

Expr.gather: Take values by index.
Expr.rank: Get the rank of each row.

Examples

Pass a single column name to compute the arg sort by that column.

>>> df = pl.DataFrame(
...     {
...         "a": [0, 1, 1, 0],
...         "b": [3, 2, 3, 2],
...         "c": [1, 2, 3, 4],
...     }
... )
>>> df.select(pl.arg_sort_by("a"))
shape: (4, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 0   │
│ 3   │
│ 1   │
│ 2   │
└─────┘

Compute the arg sort by multiple columns by either passing a list of columns, or by specifying each column as a positional argument.

>>> df.select(pl.arg_sort_by(["a", "b"], descending=True))
shape: (4, 1)
┌─────┐
│ a   │
│ --- │
│ u32 │
╞═════╡
│ 2   │
│ 1   │
│ 0   │
│ 3   │
└─────┘

Use gather to apply the arg sort to other columns.

>>> df.select(pl.col("c").gather(pl.arg_sort_by("a")))
shape: (4, 1)
┌─────┐
│ c   │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 4   │
│ 2   │
│ 3   │
└─────┘