Incorporate comments

Author: qwhelan

pandas/core/nanops.py

@@ -2,6 +2,7 @@
 import functools
 import itertools
 import operator
+from typing import Any, Optional, Tuple
 import warnings
 
 import numpy as np
@@ -200,7 +201,8 @@ def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
                 return tslibs.iNaT
 
 
-def _maybe_get_mask(values, skipna, mask, invert=False):
+def _maybe_get_mask(values: np.ndarray, skipna: bool,
+                    mask: Optional[np.ndarray]) -> Optional[np.ndarray]:
     """ This function will compute a mask iff it is necessary. Otherwise,
     return the provided mask (potentially None) when a mask does not need to be
     computed.
@@ -217,16 +219,16 @@ def _maybe_get_mask(values, skipna, mask, invert=False):
 
     Parameters
     ----------
-    values : ndarray
-    skipna : bool
-    mask : Optional[ndarray[bool]]
+    values :
+        input array to potentially compute mask for
+    skipna :
+        boolean for whether NaNs should be skipped
+    mask :
         nan-mask if known
-    invert : bool, optional
-        return notna() instead of isna()
 
     Returns
     -------
-    result : Optional[ndarray[bool]]
+    Optional[np.ndarray]
 
     """
 
@@ -236,16 +238,15 @@ def _maybe_get_mask(values, skipna, mask, invert=False):
             return None
 
         if skipna:
-            if not invert:
-                mask = isna(values)
-            else:
-                mask = notna(values)
+            mask = isna(values)
 
     return mask
 
 
-def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
-                mask=None):
+def _get_values(values: np.ndarray, skipna: bool, fill_value: Any = None,
+                fill_value_typ: str = None, mask: Optional[np.ndarray] = None
+                ) -> Tuple[np.ndarray, Optional[np.ndarray], np.dtype,
+                           np.dtype, Any]:
     """ Utility to get the values view, mask, dtype, dtype_max, and fill_value.
 
     If both mask and fill_value/fill_value_typ are not None and skipna is True,
@@ -257,13 +258,15 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
 
     Parameters
     ----------
-    values : ndarray
-    skipna : bool
-    fill_value : Any, optional
+    values :
+        input array to potentially compute mask for
+    skipna :
+        boolean for whether NaNs should be skipped
+    fill_value :
         value to fill NaNs with
-    fill_value_typ : str, optional
+    fill_value_typ :
         Set to '+inf' or '-inf' to handle dtype-specific infinities
-    mask : Optional[ndarray[bool]]
+    mask :
         nan-mask if known
 
     Returns
@@ -274,8 +277,10 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
         Mask for values, if deemed necessary to compute
     dtype : dtype
         dtype for values
-    dtype_max :
-
+    dtype_max : dtype
+        platform independent dtype
+    fill_value : Any
+        fill value used
     """
     mask = _maybe_get_mask(values, skipna, mask)
 
@@ -582,7 +587,7 @@ def nanmedian(values, axis=None, skipna=True, mask=None):
     2.0
     """
     def get_median(x):
-        mask = _maybe_get_mask(x, skipna, None, invert=True)
+        mask = notna(x)
         if mask is not None:
             if not skipna and not mask.all():
                 return np.nan
@@ -1100,7 +1105,7 @@ def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
     """
     mask = _maybe_get_mask(values, skipna, mask)
 
-    if skipna and not is_any_int_dtype(values) and mask is not None:
+    if skipna and mask is not None:
         values = values.copy()
         values[mask] = 1
     result = values.prod(axis)
@@ -1153,7 +1158,7 @@ def _get_counts(values_shape, mask, axis, dtype=float):
         if mask is not None:
             n = mask.size - mask.sum()
         else:
-            n = functools.reduce(operator.mul, values_shape)
+            n = np.prod(values_shape)
         return dtype.type(n)
 
     if mask is not None:
@@ -1187,7 +1192,7 @@ def _maybe_null_out(result, axis, mask, shape, min_count=1):
         if mask is not None:
             null_mask = mask.size - mask.sum()
         else:
-            null_mask = functools.reduce(operator.mul, shape)
+            null_mask = np.prod(shape)
         if null_mask < min_count:
             result = np.nan