simplify EA._reduce

Author: jreback

pandas/core/arrays/base.py

@@ -712,23 +712,17 @@ def _add_comparison_ops(cls):
         cls.__le__ = cls._create_comparison_method(operator.le)
         cls.__ge__ = cls._create_comparison_method(operator.ge)
 
-    def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
-                filter_type=None, **kwargs):
+    def _reduce(self, name, skipna=True, **kwargs):
         """Return a scalar result of performing the op
 
         Parameters
         ----------
-        op : callable
-            function to apply to the array
         name : str
             name of the function
         axis : int, default 0
             axis over which to apply, defined as 0 currently
         skipna : bool, default True
             if True, skip NaN values
-        numeric_only : bool, optional
-            if True, only perform numeric ops
-        filter_type : str, optional
         kwargs : dict
 
         Returns

pandas/core/arrays/integer.py

@@ -8,6 +8,7 @@
 from pandas.compat import u, range, string_types
 from pandas.compat import set_function_name
 
+from pandas.core import nanops
 from pandas.core.dtypes.cast import astype_nansafe
 from pandas.core.dtypes.generic import ABCSeries, ABCIndexClass
 from pandas.core.dtypes.common import (
@@ -529,8 +530,7 @@ def cmp_method(self, other):
         name = '__{name}__'.format(name=op.__name__)
         return set_function_name(cmp_method, name, cls)
 
-    def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
-                filter_type=None, **kwds):
+    def _reduce(self, name, axis=0, skipna=True, **kwargs):
         data = self._data
         mask = self._mask
 
@@ -539,6 +539,7 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
             data = self._data.astype('float64')
             data[mask] = self._na_value
 
+        op = getattr(nanops, 'nan' + name)
         result = op(data, axis=axis, skipna=skipna)
 
         # if we have a boolean op, provide coercion back to a bool

pandas/core/series.py

@@ -3342,10 +3342,16 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
 
         """
         delegate = self._values
-        if isinstance(delegate, np.ndarray):
-            # Validate that 'axis' is consistent with Series's single axis.
-            if axis is not None:
-                self._get_axis_number(axis)
+
+        if axis is not None:
+            self._get_axis_number(axis)
+
+        # dispatch to ExtensionArray interface
+        if isinstance(delegate, ExtensionArray):
+            return delegate._reduce(name, skipna=skipna, **kwds)
+
+        # dispatch to numpy arrays
+        elif isinstance(delegate, np.ndarray):
             if numeric_only:
                 raise NotImplementedError('Series.{0} does not implement '
                                           'numeric_only.'.format(name))

pandas/tests/extension/decimal/array.py

@@ -137,30 +137,16 @@ def _na_value(self):
     def _concat_same_type(cls, to_concat):
         return cls(np.concatenate([x._data for x in to_concat]))
 
-    def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
-                filter_type=None, **kwds):
-        """Return a scalar result of performing the op
-
-        Parameters
-        ----------
-        op : callable
-            function to apply to the array
-        name : str
-            name of the function
-        axis : int, default 0
-            axis over which to apply, defined as 0 currently
-        skipna : bool, default True
-            if True, skip NaN values
-        numeric_only : bool, optional
-            if True, only perform numeric ops
-        filter_type : str, optional
-        kwds : dict
+    def _reduce(self, name, axis=0, skipna=True, **kwargs):
 
-        Returns
-        -------
-        scalar
-        """
-        return op(self.data, axis=axis, skipna=skipna)
+        # select the nan* ops
+        if skipna:
+            op = getattr(self.data, 'nan' + name)
+
+        # don't skip nans
+        else:
+            op = getattr(self.data, name)
+        return op(axis=axis)
 
 
 DecimalArray._add_arithmetic_ops()