numpy
diff --git a/‎numpy/core/shape_base.py
Lines changed: 86 additions & 1 deletion b/‎numpy/core/shape_base.py
Lines changed: 86 additions & 1 deletion
@@ -1,6 +1,7 @@
 from __future__ import division, absolute_import, print_function
 
-__all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'vstack', 'hstack']
+__all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'vstack', 'hstack',
+           'stack']
 
 from . import numeric as _nx
 from .numeric import array, asanyarray, newaxis
@@ -275,3 +276,87 @@ def hstack(tup):
         return _nx.concatenate(arrs, 0)
     else:
         return _nx.concatenate(arrs, 1)
+
+
+def stack(tup_tup):
+    """
+    Stack arrays similar to matlab's "square bracket stacking".
+
+            [A A; B B]
+
+    Parameters
+    ----------
+    tup_tup : sequence of sequence of ndarrays
+        1-D arrays are treated as row vectors.
+
+    Returns
+    -------
+    stacked : ndarray
+        The 2-D array formed by stacking the given arrays.
+
+    See Also
+    --------
+    hstack : Stack arrays in sequence horizontally (column wise).
+    vstack : Stack arrays in sequence vertically (row wise).
+    dstack : Stack arrays in sequence depth wise (along third dimension).
+    concatenate : Join a sequence of arrays together.
+    vsplit : Split array into a list of multiple sub-arrays vertically.
+
+    Examples
+    --------
+    >>> A = np.array([[1, 2, 3]])
+    >>> B = np.array([[2, 3, 4]])
+    >>> stack([A, B])
+    array([[1, 2, 3, 2, 3, 4]])
+
+    >>> A = np.array([[1, 2, 3]]).T
+    >>> B = np.array([[2, 3, 4]]).T
+    >>> stack([A, B])
+    array([[1, 2],
+           [2, 3],
+           [3, 4]])
+
+    >>> A = np.array([[1, 2, 3]])
+    >>> B = np.array([[2, 3, 4]])
+    >>> stack([[A, A], [B, B]])
+    array([[1, 2, 3, 1, 2, 3],
+           [2, 3, 4, 2, 3, 4]])
+
+    >>> A = np.array([[1, 2, 3]])
+    >>> B = np.array([[2, 3, 4]])
+    >>> stack([[A], [B]])
+    array([[1, 2, 3],
+           [2, 3, 4]])
+
+    >>> # 1-D vectors are treated as row arrays
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> stack([a, b])
+    array([[1, 2, 3, 2, 3, 4]])
+
+    >>> # 1-D vectors are treated as row arrays
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> stack([[a, b], [a, b]])
+    array([[1, 2, 3, 2, 3, 4],
+           [1, 2, 3, 2, 3, 4]])
+
+    >>> # a bit more complex
+    >>> A = np.array([[1, 2, 3]])
+    >>> B = np.array([[2, 3, 4, 5, 6, 7]])
+    >>> c = np.array([8, 3, 4, 5, 6, 7])
+    >>> d1 = np.array([9])
+    >>> d2 = np.array([8, 7, 6, 5, 4])
+    >>> stack([[A, A], [B], [c], [d1, d2]])
+    array([[1, 2, 3, 1, 2, 3],
+           [2, 3, 4, 5, 6, 7],
+           [8, 3, 4, 5, 6, 7],
+           [9, 8, 7, 6, 5, 4]])
+
+
+    """
+    if isinstance(tup_tup[0], list) or isinstance(tup_tup[0], tuple):
+        result = vstack([hstack([a for a in row]) for row in tup_tup])
+    else:
+        result = hstack([a for a in tup_tup])
+    return atleast_2d(result)