polars.Expr.sort_by

Expr.sort_by(
    by: IntoExpr | Iterable[IntoExpr],
    *more_by: IntoExpr,
    descending: bool | Sequence[bool] = False,
    nulls_last: bool | Sequence[bool] = False,
    multithreaded: bool = True,
    maintain_order: bool = False,
) → Expr

Sort this column by the ordering of other columns.

engine:In MemoryPartially Streaming

When used in a projection/selection context, the whole column is sorted. When used in a group by context, the groups are sorted.

Parameters:

by

Column(s) to sort by. Accepts expression input. Strings are parsed as column names.

*more_by

Additional columns to 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; can specify a single boolean applying to all columns or a sequence of booleans for per-column control.

multithreaded

Sort using multiple threads.

maintain_order

Whether the order should be maintained if elements are equal.

Examples

Pass a single column name to sort by that column.

>>> df = pl.DataFrame(
...     {
...         "group": ["a", "a", "b", "b"],
...         "value1": [1, 3, 4, 2],
...         "value2": [8, 7, 6, 5],
...     }
... )
>>> df.select(pl.col("group").sort_by("value1"))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ a     │
│ b     │
│ a     │
│ b     │
└───────┘

Sorting by expressions is also supported.

>>> df.select(pl.col("group").sort_by(pl.col("value1") + pl.col("value2")))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ b     │
│ a     │
│ a     │
│ b     │
└───────┘

Sort by multiple columns by passing a list of columns.

>>> df.select(pl.col("group").sort_by(["value1", "value2"], descending=True))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ b     │
│ a     │
│ b     │
│ a     │
└───────┘

Or use positional arguments to sort by multiple columns in the same way.

>>> df.select(pl.col("group").sort_by("value1", "value2"))
shape: (4, 1)
┌───────┐
│ group │
│ ---   │
│ str   │
╞═══════╡
│ a     │
│ b     │
│ a     │
│ b     │
└───────┘

When sorting in a group by context, the groups are sorted.

>>> df.group_by("group").agg(
...     pl.col("value1").sort_by("value2")
... )  
shape: (2, 2)
┌───────┬───────────┐
│ group ┆ value1    │
│ ---   ┆ ---       │
│ str   ┆ list[i64] │
╞═══════╪═══════════╡
│ a     ┆ [3, 1]    │
│ b     ┆ [2, 4]    │
└───────┴───────────┘

Take a single row from each group where a column attains its minimal value within that group.

>>> df.group_by("group").agg(
...     pl.all().sort_by("value2").first()
... )  
shape: (2, 3)
┌───────┬────────┬────────┐
│ group ┆ value1 ┆ value2 |
│ ---   ┆ ---    ┆ ---    │
│ str   ┆ i64    ┆ i64    |
╞═══════╪════════╪════════╡
│ a     ┆ 3      ┆ 7      |
│ b     ┆ 2      ┆ 5      |
└───────┴────────┴────────┘