Revert removal of args/kwargs

pandas-dev · jreback · Jun 5, 2022 · May 30, 2022 · Jun 4, 2022 · Jun 4, 2022
commit 73bac86da7b17c83446abe8021ac22a9ea9b5081
diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py
@@ -371,6 +371,24 @@ def validate_groupby_func(name, args, kwargs, allowed=None) -> None:
         )
 
 
+RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
+
+
+def validate_resampler_func(method: str, args, kwargs) -> None:
+    """
+    'args' and 'kwargs' should be empty because all of their necessary
+    parameters are explicitly listed in the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        if method in RESAMPLER_NUMPY_OPS:
+            raise UnsupportedFunctionCall(
+                "numpy operations are not valid with resample. "
+                f"Use .resample(...).{method}() instead"
+            )
+        else:
+            raise TypeError("too many arguments passed in")
+
+
 def validate_minmax_axis(axis: int | None, ndim: int = 1) -> None:
     """
     Ensure that the axis argument passed to min, max, argmin, or argmax is zero

diff --git a/pandas/core/resample.py b/pandas/core/resample.py
@@ -33,6 +33,7 @@
     TimestampConvertibleTypes,
     npt,
 )
+from pandas.compat.numpy import function as nv
 from pandas.errors import (
     AbstractMethodError,
     DataError,
@@ -936,7 +937,7 @@ def asfreq(self, fill_value=None):
         """
         return self._upsample("asfreq", fill_value=fill_value)
 
-    def std(self, ddof=1, numeric_only: bool = False):
+    def std(self, ddof=1, numeric_only: bool = False, *args, **kwargs):
         """
         Compute standard deviation of groups, excluding missing values.
 
@@ -954,9 +955,10 @@ def std(self, ddof=1, numeric_only: bool = False):
         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=1, numeric_only: bool = False):
+    def var(self, ddof=1, numeric_only: bool = False, *args, **kwargs):
         """
         Compute variance of groups, excluding missing values.
 
@@ -975,6 +977,7 @@ def var(self, ddof=1, numeric_only: bool = False):
         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.size)
@@ -1063,7 +1066,10 @@ def f(
             self,
             numeric_only: bool | lib.NoDefault = lib.no_default,
             min_count: int = 0,
+            *args,
+            **kwargs,
         ):
+            nv.validate_resampler_func(name, args, kwargs)
             if numeric_only is lib.no_default and name != "sum":
                 # For DataFrameGroupBy, set it to be False for methods other than `sum`.
                 numeric_only = False
@@ -1075,8 +1081,9 @@ def f(
     elif args == ("numeric_only",):
         # error: All conditional function variants must have identical signatures
         def f(  # type: ignore[misc]
-            self, numeric_only: bool | lib.NoDefault = lib.no_default
+            self, numeric_only: bool | lib.NoDefault = lib.no_default, *args, **kwargs
         ):
+            nv.validate_resampler_func(name, args, kwargs)
             return self._downsample(name, numeric_only=numeric_only)
 
     elif args == ("ddof", "numeric_only"):
@@ -1085,14 +1092,20 @@ def f(  # type: ignore[misc]
             self,
             ddof: int = 1,
             numeric_only: bool | lib.NoDefault = lib.no_default,
+            *args,
+            **kwargs,
         ):
+            nv.validate_resampler_func(name, args, kwargs)
             return self._downsample(name, ddof=ddof, numeric_only=numeric_only)
 
     else:
         # error: All conditional function variants must have identical signatures
         def f(  # type: ignore[misc]
             self,
+            *args,
+            **kwargs,
         ):
+            nv.validate_resampler_func(name, args, kwargs)
             return self._downsample(name)
 
     f.__doc__ = getattr(docs_class, name).__doc__

diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
@@ -9,6 +9,7 @@
 
 from pandas._libs import lib
 from pandas._typing import DatetimeNaTType
+from pandas.errors import UnsupportedFunctionCall
 
 import pandas as pd
 from pandas import (
@@ -225,6 +226,20 @@ 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):
+    # see gh-12811
+    s = Series([1, 2, 3, 4, 5], index=date_range("20130101", periods=5, freq="s"))
+    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():
     # GH#7929
     data = np.arange(5, dtype=np.int64)