mypy

Author: sreeja97

pandas/core/groupby/generic.py

@@ -17,7 +17,6 @@
     TYPE_CHECKING,
     Any,
     Literal,
-    Self,
     TypeAlias,
     TypeVar,
     cast,
@@ -114,63 +113,20 @@
 
 
 @set_module("pandas")
-<<<<<<< HEAD
 @dataclasses.dataclass
 class NamedAgg:
-=======
-class NamedAgg(tuple):
-    __slots__ = ()
-
-    def __new__(
-        cls,
-        column: Hashable,
-        aggfunc: Callable[..., Any] | str,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Self:
-        if (
-            callable(aggfunc)
-            and not getattr(aggfunc, "_is_wrapped", False)
-            and (args or kwargs)
-        ):
-            original_func = aggfunc
-
-            def wrapped(*call_args: Any, **call_kwargs: Any) -> Any:
-                series = call_args[0]
-                final_args = call_args[1:] + args
-                final_kwargs = {**kwargs, **call_kwargs}
-                return original_func(series, *final_args, **final_kwargs)
-
-            wrapped._is_wrapped = True  # type: ignore[attr-defined]
-            aggfunc = wrapped
-        return super().__new__(cls, (column, aggfunc))
-
->>>>>>> 4e8ccf6958 (code impl and examples)
     """
-    Helper for column specific aggregation with with flexible argument passing and
-    control over output column names.
-
-<<<<<<< HEAD
-=======
-    Subclass of tuple that wraps an aggregation function.
+    Helper for column specific aggregation with control over output column names.
 
->>>>>>> 4e8ccf6958 (code impl and examples)
     Parameters
     ----------
     column : Hashable
         Column label in the DataFrame to apply aggfunc.
     aggfunc : function or str
         Function to apply to the provided column. If string, the name of a built-in
         pandas function.
-<<<<<<< HEAD
     *args, **kwargs :
         Optional positional and keyword arguments passed to ``aggfunc``.
-=======
-    *args : tuple, optional
-        Positional arguments to pass to `aggfunc` when it is called.
-    **kwargs : dict, optional
-        Keyword arguments to pass to `aggfunc` when it is called.
->>>>>>> 4e8ccf6958 (code impl and examples)
 
     See Also
     --------
@@ -187,36 +143,19 @@ def wrapped(*call_args: Any, **call_kwargs: Any) -> Any:
     1          -1      10.5
     2           1      12.0
 
-<<<<<<< HEAD
     >>> def n_between(ser, low, high, **kwargs):
     ...     return ser.between(low, high, **kwargs).sum()
 
     >>> agg_between = pd.NamedAgg("a", n_between, 0, 1)
     >>> df.groupby("key").agg(count_between=agg_between)
         count_between
-=======
-    def n_between(ser, low, high, **kwargs):
-        return ser.between(low, high, **kwargs).sum()
-
-    Using positional arguments
-    agg_between = pd.NamedAgg("a", n_between, 0, 1)
-    df.groupby("key").agg(count_between=agg_between)
-                count_between
->>>>>>> 4e8ccf6958 (code impl and examples)
     key
     1               1
     2               1
 
-<<<<<<< HEAD
     >>> agg_between_kw = pd.NamedAgg("a", n_between, 0, 1, inclusive="both")
     >>> df.groupby("key").agg(count_between_kw=agg_between_kw)
         count_between_kw
-=======
-    Using both positional and keyword arguments
-    agg_between_kw = pd.NamedAgg("a", n_between, 0, 1, inclusive="both")
-    df.groupby("key").agg(count_between_kw=agg_between_kw)
-                    count_between_kw
->>>>>>> 4e8ccf6958 (code impl and examples)
     key
     1                   1
     2                   1