MAINT: remove unused argument in private function

Author: mhvk

numpy/lib/function_base.py

@@ -3714,16 +3714,15 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         indexer[axis] = slice(index-1, index+1)
     indexer = tuple(indexer)
 
+    # Use mean in both odd and even case to coerce data type,
+    # using out array if needed.
+    rout = mean(part[indexer], axis=axis, out=out)
     # Check if the array contains any nan's
     if np.issubdtype(a.dtype, np.inexact) and sz > 0:
-        # warn and return nans like mean would
-        rout = mean(part[indexer], axis=axis, out=out)
-        return np.lib.utils._median_nancheck(part, rout, axis, out)
-    else:
-        # if there are no nans
-        # Use mean in odd and even case to coerce data type
-        # and check, use out array.
-        return mean(part[indexer], axis=axis, out=out)
+        # If nans are possible, warn and replace by nans like mean would.
+        rout = np.lib.utils._median_nancheck(part, rout, axis)
+
+    return rout
 
 
 def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,

numpy/lib/utils.py

@@ -1002,7 +1002,7 @@ def safe_eval(source):
     return ast.literal_eval(source)
 
 
-def _median_nancheck(data, result, axis, out):
+def _median_nancheck(data, result, axis):
     """
     Utility function to check median result from data for NaN values at the end
     and return NaN in that case. Input result can also be a MaskedArray.
@@ -1012,16 +1012,16 @@ def _median_nancheck(data, result, axis, out):
     data : array
         Input data to median function
     result : Array or MaskedArray
-        Result of median function
+        Result of median function.
     axis : int
         Axis along which the median was computed.
-    out : ndarray, optional
-        Output array in which to place the result.
 
     Returns
     -------
-    median : scalar or ndarray
-        Median or NaN in axes which contained NaN in the input.
+    result : scalar or ndarray
+        Median or NaN in axes which contained NaN in the input.  If the input
+        was an array, NaN will be inserted in-place.  If a scalar, either the
+        input itself or a scalar NaN.
     """
     if data.size == 0:
         return result

numpy/ma/extras.py

@@ -750,7 +750,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
             s = mid.sum(out=out)
             if not odd:
                 s = np.true_divide(s, 2., casting='safe', out=out)
-            s = np.lib.utils._median_nancheck(asorted, s, axis, out)
+            s = np.lib.utils._median_nancheck(asorted, s, axis)
         else:
             s = mid.mean(out=out)
 
@@ -790,7 +790,7 @@ def replace_masked(s):
         s = np.ma.sum(low_high, axis=axis, out=out)
         np.true_divide(s.data, 2., casting='unsafe', out=s.data)
 
-        s = np.lib.utils._median_nancheck(asorted, s, axis, out)
+        s = np.lib.utils._median_nancheck(asorted, s, axis)
     else:
         s = np.ma.mean(low_high, axis=axis, out=out)