COMPAT: Add keepdims and friends to validation

doc/source/whatsnew/v0.24.0.rst

@@ -1398,6 +1398,7 @@ Numeric
 - Added ``log10`` to the list of supported functions in :meth:`DataFrame.eval` (:issue:`24139`)
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
 - Checking PEP 3141 numbers in :func:`~pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
+- Reduction methods like :meth:`Series.sum` now accept the default value of ``keepdims=False`` when called from a NumPy ufunc, rather than raising a ``TypeError``. Full support for ``keepdims`` has not been implemented (:issue:`24356`).
 
  Conversion
 ^^^^^^^^^^

pandas/compat/numpy/function.py

@@ -189,15 +189,16 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
 ALLANY_DEFAULTS = OrderedDict()
 ALLANY_DEFAULTS['dtype'] = None
 ALLANY_DEFAULTS['out'] = None
+ALLANY_DEFAULTS['keepdims'] = False
 validate_all = CompatValidator(ALLANY_DEFAULTS, fname='all',
                                method='both', max_fname_arg_count=1)
 validate_any = CompatValidator(ALLANY_DEFAULTS, fname='any',
                                method='both', max_fname_arg_count=1)
 
-LOGICAL_FUNC_DEFAULTS = dict(out=None)
+LOGICAL_FUNC_DEFAULTS = dict(out=None, keepdims=False)
 validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs')
 
-MINMAX_DEFAULTS = dict(out=None)
+MINMAX_DEFAULTS = dict(out=None, keepdims=False)
 validate_min = CompatValidator(MINMAX_DEFAULTS, fname='min',
                                method='both', max_fname_arg_count=1)
 validate_max = CompatValidator(MINMAX_DEFAULTS, fname='max',
@@ -225,16 +226,32 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
 STAT_FUNC_DEFAULTS = OrderedDict()
 STAT_FUNC_DEFAULTS['dtype'] = None
 STAT_FUNC_DEFAULTS['out'] = None
+
+PROD_DEFAULTS = SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS['keepdims'] = False
+SUM_DEFAULTS['initial'] = None
+
+MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+MEDIAN_DEFAULTS['overwrite_input'] = False
+MEDIAN_DEFAULTS['keepdims'] = False
+
+STAT_FUNC_DEFAULTS['keepdims'] = False
+
 validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS,
                                      method='kwargs')
-validate_sum = CompatValidator(STAT_FUNC_DEFAULTS, fname='sort',
+validate_sum = CompatValidator(SUM_DEFAULTS, fname='sum',
                                method='both', max_fname_arg_count=1)
+validate_prod = CompatValidator(PROD_DEFAULTS, fname="prod",
+                                method="both", max_fname_arg_count=1)
 validate_mean = CompatValidator(STAT_FUNC_DEFAULTS, fname='mean',
                                 method='both', max_fname_arg_count=1)
+validate_median = CompatValidator(MEDIAN_DEFAULTS, fname='median',
+                                  method='both', max_fname_arg_count=1)
 
 STAT_DDOF_FUNC_DEFAULTS = OrderedDict()
 STAT_DDOF_FUNC_DEFAULTS['dtype'] = None
 STAT_DDOF_FUNC_DEFAULTS['out'] = None
+STAT_DDOF_FUNC_DEFAULTS['keepdims'] = False
 validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS,
                                           method='kwargs')
 

pandas/core/generic.py

@@ -10834,7 +10834,12 @@ def _make_min_count_stat_function(cls, name, name1, name2, axis_descr, desc,
     def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
                   min_count=0,
                   **kwargs):
-        nv.validate_stat_func(tuple(), kwargs, fname=name)
+        if name == 'sum':
+            nv.validate_sum(tuple(), kwargs)
+        elif name == 'prod':
+            nv.validate_prod(tuple(), kwargs)
+        else:
+            nv.validate_stat_func(tuple(), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:
@@ -10855,7 +10860,10 @@ def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f,
     @Appender(_num_doc)
     def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
                   **kwargs):
-        nv.validate_stat_func(tuple(), kwargs, fname=name)
+        if name == 'median':
+            nv.validate_median(tuple(), kwargs)
+        else:
+            nv.validate_stat_func(tuple(), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:

pandas/tests/series/test_analytics.py

@@ -1641,6 +1641,42 @@ def test_value_counts_categorical_not_ordered(self):
         tm.assert_series_equal(s.value_counts(normalize=True), exp)
         tm.assert_series_equal(idx.value_counts(normalize=True), exp)
 
+    @pytest.mark.parametrize("func", [np.any, np.all])
+    @pytest.mark.parametrize("kwargs", [
+        dict(keepdims=True),
+        dict(out=object()),
+    ])
+    @td.skip_if_np_lt_115
+    def test_validate_any_all_out_keepdims_raises(self, kwargs, func):
+        s = pd.Series([1, 2])
+        param = list(kwargs)[0]
+        name = func.__name__
+
+        msg = "the '{}' parameter .* {}".format(param, name)
+        with pytest.raises(ValueError, match=msg):
+            func(s, **kwargs)
+
+    @td.skip_if_np_lt_115
+    def test_validate_sum_initial(self):
+        s = pd.Series([1, 2])
+        with pytest.raises(ValueError, match="the 'initial' .* sum"):
+            np.sum(s, initial=10)
+
+    def test_validate_median_initial(self):
+        s = pd.Series([1, 2])
+        with pytest.raises(ValueError,
+                           match="the 'overwrite_input' .* median"):
+            # It seems like np.median doesn't dispatch, so we use the
+            # method instead of the ufunc.
+            s.median(overwrite_input=True)
+
+    @td.skip_if_np_lt_115
+    def test_validate_stat_keepdims(self):
+        s = pd.Series([1, 2])
+        with pytest.raises(ValueError,
+                           match="the 'keepdims'"):
+            np.sum(s, keepdims=True)
+
 
 main_dtypes = [
     'datetime',