From 3edb890f8010802df9bc070f8d2d64b95af39e7e Mon Sep 17 00:00:00 2001
From: Daniel Saxton <dsaxton@pm.me>
Date: Sat, 25 Apr 2020 09:23:57 -0500
Subject: [PATCH] CLN: Pass numpy args as kwargs

---
 pandas/compat/numpy/function.py |  7 ++++++-
 pandas/core/arrays/numpy_.py    | 30 ++++--------------------------
 2 files changed, 10 insertions(+), 27 deletions(-)

diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py
index 30768f9bf2b06..d7a14c28cc9ca 100644
--- a/pandas/compat/numpy/function.py
+++ b/pandas/compat/numpy/function.py
@@ -251,11 +251,16 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
 STAT_FUNC_DEFAULTS["dtype"] = None
 STAT_FUNC_DEFAULTS["out"] = None
 
-PROD_DEFAULTS = SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
 SUM_DEFAULTS["axis"] = None
 SUM_DEFAULTS["keepdims"] = False
 SUM_DEFAULTS["initial"] = None
 
+PROD_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+PROD_DEFAULTS["axis"] = None
+PROD_DEFAULTS["keepdims"] = False
+PROD_DEFAULTS["initial"] = None
+
 MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
 MEDIAN_DEFAULTS["overwrite_input"] = False
 MEDIAN_DEFAULTS["keepdims"] = False
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index e9950e0edaffb..6806ed2afcf5c 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -365,36 +365,14 @@ def max(self, skipna: bool = True, **kwargs) -> Scalar:
         )
         return result
 
-    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)
-        )
+    def sum(self, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar:
+        nv.validate_sum((), kwargs)
         return nanops.nansum(
             self._ndarray, axis=axis, skipna=skipna, min_count=min_count
         )
 
-    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)
-        )
+    def prod(self, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar:
+        nv.validate_prod((), kwargs)
         return nanops.nanprod(
             self._ndarray, axis=axis, skipna=skipna, min_count=min_count
         )