narwhals.GroupBy

agg(*aggs: Expr | Iterable[Expr], **named_aggs: Expr) -> DataFrameT

Compute aggregations for each group of a group by operation.

Parameters:

Name	Type	Description	Default
aggs	Expr | Iterable[Expr]	Aggregations to compute for each group of the group by operation, specified as positional arguments.	()
named_aggs	Expr	Additional aggregations, specified as keyword arguments.	{}

Returns:

Type	Description
DataFrameT	A new Dataframe.

Examples:

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

>>> import pandas as pd
>>> import narwhals as nw
>>> df_native = pd.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "c"],
...         "b": [1, 2, 1, 3, 3],
...         "c": [5, 4, 3, 2, 1],
...     }
... )
>>> df = nw.from_native(df_native)
>>>
>>> df.group_by("a").agg(nw.col("b").sum()).sort("a")
┌──────────────────┐
|Narwhals DataFrame|
|------------------|
|        a  b      |
|     0  a  2      |
|     1  b  5      |
|     2  c  3      |
└──────────────────┘
>>>
>>> df.group_by("a", "b").agg(nw.col("c").sum()).sort("a", "b").to_native()
   a  b  c
0  a  1  8
1  b  2  4
2  b  3  2
3  c  3  1