DOC: update return type description for average

1 files changed, 13 insertions, 6 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 26ef3e235..48b2df3b8 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -307,10 +307,12 @@ def average(a, axis=None, weights=None, returned=False):
     average, [sum_of_weights] : array_type or double
         Return the average along the specified axis. When returned is `True`,
         return a tuple with the average as the first element and the sum
-        of the weights as the second element. The return type is `Float`
-        if `a` is of integer type, otherwise it is of the same type as `a`.
-        `sum_of_weights` is of the same type as `average`.
-
+        of the weights as the second element.`sum_of_weights` is of the
+        same type as `average`. The result type is the type of lowest precision
+        capable of representing values of both `a` and `weights` or 'float64'
+        if that type would be integral. Otherwise, if `a` is non integral,
+        result will be a `dtype` which is capable of representing both
+        `a.dtype` and `wgt.dtype`
     Raises
     ------
     ZeroDivisionError
@@ -326,7 +328,8 @@ def average(a, axis=None, weights=None, returned=False):
 
     ma.average : average for masked arrays -- useful if your data contains
                  "missing" values
-
+    numpy.result_type : Returns the type that results from applying the
+                        NumPy type promotion rules to the arguments.
     Examples
     --------
     >>> data = range(1,5)
@@ -348,7 +351,11 @@ def average(a, axis=None, weights=None, returned=False):
     Traceback (most recent call last):
     ...
     TypeError: Axis must be specified when shapes of a and weights differ.
-
+    >>> a = np.ones(5, dtype=np.float128)
+    >>> w = np.ones(5, dtype=np.complex64)
+    >>> avg = np.average(a, weights=w)
+    >>> print(avg.dtype)
+    complex256
     """
     a = np.asanyarray(a)