DEPR: remove *args, **kwargs on resample methods

Author: kostyafarber

pandas/core/resample.py

@@ -37,7 +37,6 @@
     TimestampConvertibleTypes,
     npt,
 )
-from pandas.compat.numpy import function as nv
 from pandas.errors import (
     AbstractMethodError,
     DataError,
@@ -899,75 +898,54 @@ def sum(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("sum", args, kwargs)
         return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.prod)
     def prod(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("prod", args, kwargs)
         return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
 
     def min(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("min", args, kwargs)
         return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
 
     def max(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("max", args, kwargs)
         return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.first)
     def first(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("first", args, kwargs)
         return self._downsample("first", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.last)
     def last(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("last", args, kwargs)
         return self._downsample("last", numeric_only=numeric_only, min_count=min_count)
 
     @doc(GroupBy.median)
-    def median(self, numeric_only: bool = False, *args, **kwargs):
-        nv.validate_resampler_func("median", args, kwargs)
+    def median(self, numeric_only: bool = False):
         return self._downsample("median", numeric_only=numeric_only)
 
     def mean(
         self,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute mean of groups, excluding missing values.
@@ -986,15 +964,12 @@ def mean(
         DataFrame or Series
             Mean of values within each group.
         """
-        nv.validate_resampler_func("mean", args, kwargs)
         return self._downsample("mean", numeric_only=numeric_only)
 
     def std(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute standard deviation of groups, excluding missing values.
@@ -1017,15 +992,12 @@ def std(
         DataFrame or Series
             Standard deviation of values within each group.
         """
-        nv.validate_resampler_func("std", args, kwargs)
         return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
 
     def var(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute variance of groups, excluding missing values.
@@ -1049,36 +1021,26 @@ def var(
         DataFrame or Series
             Variance of values within each group.
         """
-        nv.validate_resampler_func("var", args, kwargs)
         return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
 
     @doc(GroupBy.sem)
     def sem(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("sem", args, kwargs)
         return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
 
     @doc(GroupBy.ohlc)
     def ohlc(
         self,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("ohlc", args, kwargs)
         return self._downsample("ohlc")
 
     @doc(SeriesGroupBy.nunique)
     def nunique(
         self,
-        *args,
-        **kwargs,
     ):
-        nv.validate_resampler_func("nunique", args, kwargs)
         return self._downsample("nunique")
 
     @doc(GroupBy.size)

pandas/tests/resample/test_datetime_index.py

@@ -9,7 +9,6 @@
 
 from pandas._libs import lib
 from pandas._typing import DatetimeNaTType
-from pandas.errors import UnsupportedFunctionCall
 
 import pandas as pd
 from pandas import (
@@ -243,22 +242,6 @@ def _ohlc(group):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("func", ["min", "max", "sum", "prod", "mean", "var", "std"])
-def test_numpy_compat(func, unit):
-    # see gh-12811
-    s = Series(
-        [1, 2, 3, 4, 5], index=date_range("20130101", periods=5, freq="s").as_unit(unit)
-    )
-    r = s.resample("2s")
-
-    msg = "numpy operations are not valid with resample"
-
-    with pytest.raises(UnsupportedFunctionCall, match=msg):
-        getattr(r, func)(func, 1, 2, 3)
-    with pytest.raises(UnsupportedFunctionCall, match=msg):
-        getattr(r, func)(axis=1)
-
-
 def test_resample_how_callables(unit):
     # GH#7929
     data = np.arange(5, dtype=np.int64)