use compat validator

Author: TomAugspurger

pandas/core/arrays/numpy_.py

@@ -1,9 +1,9 @@
-from functools import wraps
 import numbers
 
 import numpy as np
 
 from pandas._libs import lib
+from pandas.compat.numpy import function as nv
 
 from pandas.core.dtypes.common import extract_array
 from pandas.core.dtypes.dtypes import ExtensionDtype
@@ -16,32 +16,6 @@
 from .base import ExtensionArray, ExtensionOpsMixin
 
 
-def _validate_reduction_args(func):
-    """
-    Validate that unused keyword arguments are not set.
-    """
-    check = {'out', 'keepdims', 'initial', 'dtype'}
-
-    msg = (
-        "'{}' does not support the '{}' parameter."
-    )
-
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if len(args) > 1:
-            raise TypeError(
-                "'{}' requires keyword arguments.".format(func.__name__)
-            )
-        for kw, value in kwargs.items():
-            if kw in check and value is not None:
-                raise TypeError(msg.format(func.__name__, kw))
-            elif kw == 'ovewrite_input' and kw is not False:
-                raise TypeError(msg.format(func.__name__, kw))
-
-        return func(*args, **kwargs)
-    return wrapper
-
-
 class PandasDtype(ExtensionDtype):
     """
     A Pandas ExtensionDtype for NumPy dtypes.
@@ -333,72 +307,83 @@ def _reduce(self, name, skipna=True, **kwargs):
             )
             raise TypeError(msg.format(type(self).__name__, name))
 
-    @_validate_reduction_args
-    def any(self, axis=None, out=None, keepdims=None, skipna=True):
+    # @_validate_reduction_args
+    def any(self, axis=None, out=None, keepdims=False, skipna=True):
+        nv.validate_any((), dict(out=out, keepdims=keepdims))
         return nanops.nanany(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def all(self, axis=None, out=None, keepdims=None, skipna=True):
+    # @_validate_reduction_args
+    def all(self, axis=None, out=None, keepdims=False, skipna=True):
+        nv.validate_all((), dict(out=out, keepdims=keepdims))
         return nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def min(self, axis=None, out=None, keepdims=None, initial=None,
-            skipna=True):
+    # @_validate_reduction_args
+    def min(self, axis=None, out=None, keepdims=False, skipna=True):
+        nv.validate_min((), dict(out=out, keepdims=keepdims))
         return nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def max(self, axis=None, out=None, keepdims=None, initial=None,
-            skipna=True):
+    # @_validate_reduction_args
+    def max(self, axis=None, out=None, keepdims=False, skipna=True):
+        nv.validate_max((), dict(out=out, keepdims=keepdims))
         return nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def sum(self, axis=None, dtype=None, out=None, keepdims=None,
+    # @_validate_reduction_args
+    def sum(self, axis=None, dtype=None, out=None, keepdims=False,
             initial=None, skipna=True, min_count=0):
+        nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims,
+                                 initial=initial))
         return nanops.nansum(self._ndarray, axis=axis, skipna=skipna,
                              min_count=min_count)
 
-    @_validate_reduction_args
-    def prod(self, axis=None, dtype=None, out=None, keepdims=None,
+    def prod(self, axis=None, dtype=None, out=None, keepdims=False,
              initial=None, skipna=True, min_count=0):
+        nv.validate_prod((), dict(dtype=dtype, out=out, keepdims=keepdims,
+                                  initial=initial))
         return nanops.nanprod(self._ndarray, axis=axis, skipna=skipna,
                               min_count=min_count)
 
-    @_validate_reduction_args
-    def mean(self, axis=None, dtype=None, out=None, keepdims=None,
+    def mean(self, axis=None, dtype=None, out=None, keepdims=False,
              skipna=True):
+        nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims))
         return nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
     def median(self, axis=None, out=None, overwrite_input=False,
                keepdims=False, skipna=True):
+        nv.validate_median((), dict(out=out, overwrite_input=overwrite_input,
+                                    keepdims=keepdims))
         return nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def std(self, axis=None, dtype=None, out=None, ddof=1, keepdims=None,
+    def std(self, axis=None, dtype=None, out=None, ddof=1, keepdims=False,
             skipna=True):
+        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
+                                            keepdims=keepdims))
         return nanops.nanstd(self._ndarray, axis=axis, skipna=skipna,
                              ddof=ddof)
 
-    @_validate_reduction_args
-    def var(self, axis=None, dtype=None, out=None, ddof=1, keepdims=None,
+    def var(self, axis=None, dtype=None, out=None, ddof=1, keepdims=False,
             skipna=True):
+        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
+                                            keepdims=keepdims))
         return nanops.nanvar(self._ndarray, axis=axis, skipna=skipna,
                              ddof=ddof)
 
-    @_validate_reduction_args
-    def sem(self, axis=None, dtype=None, out=None, ddof=1, keepdims=None,
+    def sem(self, axis=None, dtype=None, out=None, ddof=1, keepdims=False,
             skipna=True):
+        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
+                                            keepdims=keepdims))
         return nanops.nansem(self._ndarray, axis=axis, skipna=skipna,
                              ddof=ddof)
 
-    @_validate_reduction_args
-    def kurt(self, axis=None, dtype=None, out=None, keepdims=None,
+    def kurt(self, axis=None, dtype=None, out=None, keepdims=False,
              skipna=True):
+        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
+                                            keepdims=keepdims))
         return nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
 
-    @_validate_reduction_args
-    def skew(self, axis=None, dtype=None, out=None, keepdims=None,
+    def skew(self, axis=None, dtype=None, out=None, keepdims=False,
              skipna=True):
+        nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
+                                            keepdims=keepdims))
         return nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
 
     # ------------------------------------------------------------------------

pandas/tests/arrays/test_numpy.py

@@ -47,17 +47,10 @@ def test_from_sequence_dtype():
     tm.assert_extension_array_equal(result, expected)
 
 
-def test_validate_reduction_positional_args():
-    arr = PandasArray(np.array([1, 2, 3]))
-
-    with pytest.raises(TypeError, match="'all' requires keyword arguments"):
-        arr.all(0)
-
-
 def test_validate_reduction_keyword_args():
     arr = PandasArray(np.array([1, 2, 3]))
-    msg = "'all' does not support the 'keepdims' parameter"
-    with pytest.raises(TypeError, match=msg):
+    msg = "the 'keepdims' parameter is not supported .*all"
+    with pytest.raises(ValueError, match=msg):
         arr.all(keepdims=True)