polars.DataFrame.group_by#

DataFrame.group_by(

*by: IntoExpr | Iterable[IntoExpr],

maintain_order: bool = False,

**named_by: IntoExpr,

) → GroupBy[source]#

Start a group by operation.

Parameters:

*by: Column(s) to group by. Accepts expression input. Strings are parsed as column names.
maintain_order: Ensure that the order of the groups is consistent with the input data. This is slower than a default group by. Settings this to True blocks the possibility to run on the streaming engine.

Note

Within each group, the order of rows is always preserved, regardless of this argument.
**named_by: Additional columns to group by, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:

GroupBy: Object which can be used to perform aggregations.

Examples

Group by one column and call agg to compute the grouped sum of another column.

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "c"],
...         "b": [1, 2, 1, 3, 3],
...         "c": [5, 4, 3, 2, 1],
...     }
... )
>>> df.group_by("a").agg(pl.col("b").sum())  
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ a   ┆ 2   │
│ b   ┆ 5   │
│ c   ┆ 3   │
└─────┴─────┘

Set maintain_order=True to ensure the order of the groups is consistent with the input.

>>> df.group_by("a", maintain_order=True).agg(pl.col("c"))
shape: (3, 2)
┌─────┬───────────┐
│ a   ┆ c         │
│ --- ┆ ---       │
│ str ┆ list[i64] │
╞═════╪═══════════╡
│ a   ┆ [5, 3]    │
│ b   ┆ [4, 2]    │
│ c   ┆ [1]       │
└─────┴───────────┘

Group by multiple columns by passing a list of column names.

>>> df.group_by(["a", "b"]).agg(pl.max("c"))  
shape: (4, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘

Or use positional arguments to group by multiple columns in the same way. Expressions are also accepted.

>>> df.group_by("a", pl.col("b") // 2).agg(pl.col("c").mean())  
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════╪═════╪═════╡
│ a   ┆ 0   ┆ 4.0 │
│ b   ┆ 1   ┆ 3.0 │
│ c   ┆ 1   ┆ 1.0 │
└─────┴─────┴─────┘

The GroupBy object returned by this method is iterable, returning the name and data of each group.

>>> for name, data in df.group_by("a"):  
...     print(name)
...     print(data)
a
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ a   ┆ 1   ┆ 3   │
└─────┴─────┴─────┘
b
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
└─────┴─────┴─────┘
c
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘