Merge branch 'merge-wiki-edits' into master.

4 files changed, 27 insertions, 22 deletions

diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index 91dd96f9c..20a0e7151 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -44,7 +44,7 @@ def ediff1d(ary, to_end=None, to_begin=None):
 
     Returns
     -------
-    ed : ndarray
+    ediff1d : ndarray
         The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
 
     See Also
@@ -212,7 +212,7 @@ def intersect1d(ar1, ar2, assume_unique=False):
 
     Returns
     -------
-    out : ndarray
+    intersect1d : ndarray
         Sorted 1D array of common and unique elements.
 
     See Also
@@ -251,7 +251,7 @@ def setxor1d(ar1, ar2, assume_unique=False):
 
     Returns
     -------
-    xor : ndarray
+    setxor1d : ndarray
         Sorted 1D array of unique values that are in only one of the input
         arrays.
 
@@ -287,7 +287,7 @@ def in1d(ar1, ar2, assume_unique=False):
 
     Parameters
     ----------
-    ar1 : array_like, shape (M,)
+    ar1 : (M,) array_like
         Input array.
     ar2 : array_like
         The values against which to test each value of `ar1`.
@@ -297,8 +297,8 @@ def in1d(ar1, ar2, assume_unique=False):
 
     Returns
     -------
-    mask : ndarray of bools, shape(M,)
-        The values `ar1[mask]` are in `ar2`.
+    in1d : (M,) ndarray, bool
+        The values `ar1[in1d]` are in `ar2`.
 
     See Also
     --------
@@ -365,7 +365,7 @@ def union1d(ar1, ar2):
 
     Returns
     -------
-    union : ndarray
+    union1d : ndarray
         Unique, sorted union of the input arrays.
 
     See Also
@@ -399,7 +399,7 @@ def setdiff1d(ar1, ar2, assume_unique=False):
 
     Returns
     -------
-    difference : ndarray
+    setdiff1d : ndarray
         Sorted 1D array of values in `ar1` that are not in `ar2`.
 
     See Also
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index f3df3b96b..a0781ebf9 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -843,7 +843,7 @@ def gradient(f, *varargs):
 
     Returns
     -------
-    g : ndarray
+    gradient : ndarray
       N arrays of the same shape as `f` giving the derivative of `f` with
       respect to each dimension.
 
@@ -948,7 +948,7 @@ def diff(a, n=1, axis=-1):
 
     Returns
     -------
-    out : ndarray
+    diff : ndarray
         The `n` order differences. The shape of the output is the same as `a`
         except along `axis` where the dimension is smaller by `n`.
 
@@ -1284,6 +1284,11 @@ def extract(condition, arr):
     arr : array_like
         Input array of the same size as `condition`.
 
+    Returns
+    -------
+    extract : ndarray
+        Rank 1 array of values from `arr` where `condition` is True.
+
     See Also
     --------
     take, put, copyto, compress
@@ -1316,9 +1321,10 @@ def place(arr, mask, vals):
     """
     Change elements of an array based on conditional and input values.
 
-    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place`
-    uses the first N elements of `vals`, where N is the number of True values
-    in `mask`, while `copyto` uses the elements where `mask` is True.
+    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
+    `place` uses the first N elements of `vals`, where N is the number of
+    True values in `mask`, while `copyto` uses the elements where `mask`
+    is True.
 
     Note that `extract` does the exact opposite of `place`.
 
@@ -2713,7 +2719,7 @@ def kaiser(M,beta):
 
     A beta value of 14 is probably a good starting point. Note that as beta
     gets large, the window narrows, and so the number of samples needs to be
-    large enough to sample the increasingly narrow spike, otherwise nans will
+    large enough to sample the increasingly narrow spike, otherwise NaNs will
     get returned.
 
     Most references to the Kaiser window come from the signal processing
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index cb14e4963..b1e891f77 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -470,8 +470,7 @@ def savez(file, *args, **kwds):
     --------
     save : Save a single array to a binary file in NumPy format.
     savetxt : Save an array to a file as plain text.
-    numpy.savez_compressed : Save several arrays into a compressed .npz file
-    format
+    savez_compressed : Save several arrays into a compressed .npz file format
 
     Notes
     -----
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index c116c7e4a..e22d63156 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -233,11 +233,10 @@ def isreal(x):
 
 def iscomplexobj(x):
     """
-    Return True if x is a complex type or an array of complex numbers.
+    Check for a complex type or an array of complex numbers.
 
-    The type of the input is checked, not the value. So even if the input
-    has an imaginary part equal to zero, `iscomplexobj` evaluates to True
-    if the data type is complex.
+    The type of the input is checked, not the value. Even if the input
+    has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
 
     Parameters
     ----------
@@ -246,8 +245,9 @@ def iscomplexobj(x):
 
     Returns
     -------
-    y : bool
-        The return value, True if `x` is of a complex type.
+    iscomplexobj : bool
+        The return value, True if `x` is of a complex type or has at least
+        one complex element.
 
     See Also
     --------