
    /_i                        d dl mZ d dlmZmZmZmZ d dlmZ d dl	m
Z
 d dlmZ d dlmZ erd dlmZmZmZ d dlmZ d d	lmZ d d
lmZ  edd      Z G d dee         Z G d dee         Zy)    )annotations)TYPE_CHECKINGAnyGenericTypeVaris_scalar_like)tupleify)InvalidOperationError)
DataFrameT)IterableIteratorSequence)CompliantExprAny)	LazyFrame)Expr
LazyFrameTzLazyFrame[Any])boundc                  4    e Zd Z	 	 	 	 	 	 	 	 ddZddZddZy)GroupByc                  || _         || _        | j                   j                  j                  | j                  |      | _        y N)drop_null_keys_df_keys_compliant_framegroup_by_groupedselfdfkeysr   s       P/var/www/html/land_sniper/venv/lib/python3.12/site-packages/narwhals/group_by.py__init__zGroupBy.__init__   <      "
11::JJ~ ; 
    c                     | j                   j                  |i |}t        d |D              sd}t        |      | j                   j	                   | j
                  j                  |       S )u  Compute aggregations for each group of a group by operation.

        Arguments:
            aggs: Aggregations to compute for each group of the group by operation,
                specified as positional arguments.
            named_aggs: Additional aggregations, specified as keyword arguments.

        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
        c              3  2   K   | ]  }t        |        y wNr   .0xs     r$   	<genexpr>zGroupBy.agg.<locals>.<genexpr>L        =>!$=   Found expression which does not aggregate.

All expressions passed to GroupBy.agg must aggregate.
For example, `df.group_by('a').agg(nw.col('b').sum())` is valid,
but `df.group_by('a').agg(nw.col('b'))` is not.r   _flatten_and_extractallr   _with_compliantr   aggr!   aggs
named_aggscompliant_aggsmsgs        r$   r6   zGroupBy.agg#   sm    P 766K
K=n==B  (,,xx''(9(9(9>(JKKr'   c              #  h    K    fd j                   j                         D        E d {    y 7 w)Nc              3  p   K   | ]-  \  }}t        |      j                  j                  |      f / y wr*   )r
   r   r5   )r,   keyr"   r!   s      r$   r.   z#GroupBy.__iter__.<locals>.<genexpr>W   s5      
b c]DHH44R89
s   36)r   __iter__)r!   s   `r$   r?   zGroupBy.__iter__V   s+     
!]]335
 	
 	
s   '202N)r"   r   r#   *Sequence[str] | Sequence[CompliantExprAny]r   boolreturnNone)r8   Expr | Iterable[Expr]r9   r   rB   r   )rB   z Iterator[tuple[Any, DataFrameT]])__name__
__module____qualname__r%   r6   r?    r'   r$   r   r      s9    

 9
 
 

1Lf
r'   r   c                  ,    e Zd Z	 	 	 	 	 	 	 	 ddZddZy)LazyGroupByc                  || _         || _        | j                   j                  j                  | j                  |      | _        y r   r   r    s       r$   r%   zLazyGroupBy.__init__^   r&   r'   c                     | j                   j                  |i |}t        d |D              sd}t        |      | j                   j	                   | j
                  j                  |       S )u  Compute aggregations for each group of a group by operation.

        Arguments:
            aggs: Aggregations to compute for each group of the group by operation,
                specified as positional arguments.
            named_aggs: Additional aggregations, specified as keyword arguments.

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

            >>> import polars as pl
            >>> import narwhals as nw
            >>> from narwhals.typing import IntoFrameT
            >>> lf_native = pl.LazyFrame(
            ...     {
            ...         "a": ["a", "b", "a", "b", "c"],
            ...         "b": [1, 2, 1, 3, 3],
            ...         "c": [5, 4, 3, 2, 1],
            ...     }
            ... )
            >>> lf = nw.from_native(lf_native)
            >>>
            >>> nw.to_native(lf.group_by("a").agg(nw.col("b").sum()).sort("a")).collect()
            shape: (3, 2)
            ┌─────┬─────┐
            │ a   ┆ b   │
            │ --- ┆ --- │
            │ str ┆ i64 │
            ╞═════╪═════╡
            │ a   ┆ 2   │
            │ b   ┆ 5   │
            │ c   ┆ 3   │
            └─────┴─────┘
            >>>
            >>> lf.group_by("a", "b").agg(nw.sum("c")).sort("a", "b").collect()
            ┌───────────────────┐
            |Narwhals DataFrame |
            |-------------------|
            |shape: (4, 3)      |
            |┌─────┬─────┬─────┐|
            |│ a   ┆ b   ┆ c   │|
            |│ --- ┆ --- ┆ --- │|
            |│ str ┆ i64 ┆ i64 │|
            |╞═════╪═════╪═════╡|
            |│ a   ┆ 1   ┆ 8   │|
            |│ b   ┆ 2   ┆ 4   │|
            |│ b   ┆ 3   ┆ 2   │|
            |│ c   ┆ 3   ┆ 1   │|
            |└─────┴─────┴─────┘|
            └───────────────────┘
        c              3  2   K   | ]  }t        |        y wr*   r   r+   s     r$   r.   z"LazyGroupBy.agg.<locals>.<genexpr>   r/   r0   r1   r2   r7   s        r$   r6   zLazyGroupBy.aggl   sm    j 766K
K=n==B  (,,xx''(9(9(9>(JKKr'   N)r"   r   r#   r@   r   rA   rB   rC   )r8   rD   r9   r   rB   r   )rE   rF   rG   r%   r6   rH   r'   r$   rJ   rJ   ]   s3    

 9
 
 

>Lr'   rJ   N)
__future__r   typingr   r   r   r   narwhals._expression_parsingr	   narwhals._utilsr
   narwhals.exceptionsr   narwhals.typingr   collections.abcr   r   r   narwhals._compliant.typingr   narwhals.dataframer   narwhals.exprr   r   r   rJ   rH   r'   r$   <module>rX      sb    " 7 7 7 $ 5 &<<;,"\)9:
F
gj! F
RML'*% MLr'   