1 files changed, 99 insertions, 3 deletions

diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index fae3e3cbc..9a1448991 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -1,9 +1,10 @@
 """
-Set operations for 1D numeric arrays based on sorting.
+Set operations for arrays based on sorting.
 
 :Contains:
-  ediff1d,
   unique,
+  isin,
+  ediff1d,
   intersect1d,
   setxor1d,
   in1d,
@@ -31,7 +32,7 @@ import numpy as np
 
 __all__ = [
     'ediff1d', 'intersect1d', 'setxor1d', 'union1d', 'setdiff1d', 'unique',
-    'in1d'
+    'in1d', 'isin'
     ]
 
 
@@ -380,6 +381,7 @@ def setxor1d(ar1, ar2, assume_unique=False):
     flag2 = flag[1:] == flag[:-1]
     return aux[flag2]
 
+
 def in1d(ar1, ar2, assume_unique=False, invert=False):
     """
     Test whether each element of a 1-D array is also present in a second array.
@@ -387,6 +389,8 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
     Returns a boolean array the same length as `ar1` that is True
     where an element of `ar1` is in `ar2` and False otherwise.
 
+    We recommend using :func:`isin` instead of `in1d` for new code.
+
     Parameters
     ----------
     ar1 : (M,) array_like
@@ -411,6 +415,8 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
 
     See Also
     --------
+    isin                  : Version of this function that preserves the
+                            shape of ar1.
     numpy.lib.arraysetops : Module with a number of other functions for
                             performing set operations on arrays.
 
@@ -481,6 +487,96 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
     else:
         return ret[rev_idx]
 
+
+def isin(element, test_elements, assume_unique=False, invert=False):
+    """
+    Calculates `element in test_elements`, broadcasting over `element` only.
+    Returns a boolean array of the same shape as `element` that is True
+    where an element of `element` is in `test_elements` and False otherwise.
+
+    Parameters
+    ----------
+    element : array_like
+        Input array.
+    test_elements : array_like
+        The values against which to test each value of `element`.
+        This argument is flattened if it is an array or array_like.
+        See notes for behavior with non-array-like parameters.
+    assume_unique : bool, optional
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation.  Default is False.
+    invert : bool, optional
+        If True, the values in the returned array are inverted, as if
+        calculating `element not in test_elements`. Default is False.
+        ``np.isin(a, b, invert=True)`` is equivalent to (but faster
+        than) ``np.invert(np.isin(a, b))``.
+
+    Returns
+    -------
+    isin : ndarray, bool
+        Has the same shape as `element`. The values `element[isin]`
+        are in `test_elements`.
+
+    See Also
+    --------
+    in1d                  : Flattened version of this function.
+    numpy.lib.arraysetops : Module with a number of other functions for
+                            performing set operations on arrays.
+    Notes
+    -----
+
+    `isin` is an element-wise function version of the python keyword `in`.
+    ``isin(a, b)`` is roughly equivalent to
+    ``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences.
+
+    `element` and `test_elements` are converted to arrays if they are not
+    already. If `test_elements` is a set (or other non-sequence collection)
+    it will be converted to an object array with one element, rather than an
+    array of the values contained in `test_elements`. This is a consequence
+    of the `array` constructor's way of handling non-sequence collections.
+    Converting the set to a list usually gives the desired behavior.
+
+    .. versionadded:: 1.13.0
+
+    Examples
+    --------
+    >>> element = 2*np.arange(4).reshape((2, 2))
+    >>> element
+    array([[0, 2],
+           [4, 6]])
+    >>> test_elements = [1, 2, 4, 8]
+    >>> mask = np.isin(element, test_elements)
+    >>> mask
+    array([[ False,  True],
+           [ True,  False]], dtype=bool)
+    >>> element[mask]
+    array([2, 4])
+    >>> mask = np.isin(element, test_elements, invert=True)
+    >>> mask
+    array([[ True, False],
+           [ False, True]], dtype=bool)
+    >>> element[mask]
+    array([0, 6])
+
+    Because of how `array` handles sets, the following does not
+    work as expected:
+
+    >>> test_set = {1, 2, 4, 8}
+    >>> np.isin(element, test_set)
+    array([[ False, False],
+           [ False, False]], dtype=bool)
+
+    Casting the set to a list gives the expected result:
+
+    >>> np.isin(element, list(test_set))
+    array([[ False,  True],
+           [ True,  False]], dtype=bool)
+    """
+    element = np.asarray(element)
+    return in1d(element, test_elements, assume_unique=assume_unique,
+                invert=invert).reshape(element.shape)
+
+
 def union1d(ar1, ar2):
     """
     Find the union of two arrays.