[GitHub] [incubator-mxnet] zoeygxy commented on a change in pull request #15390: [Numpy fix-doc]modify numpy doc

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zoeygxy commented on a change in pull request #15390: [Numpy  fix-doc]modify numpy doc
URL: https://github.com/apache/incubator-mxnet/pull/15390#discussion_r298458646
 
 

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 File path: python/mxnet/_numpy_op_doc.py
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 @@ -32,24 +33,70 @@ def _np_reshape(a, newshape, order='C'):
         an integer, then the result will be a 1-D array of that length.
         One shape dimension can be -1. In this case, the value is
         inferred from the length of the array and remaining dimensions.
-    order : {'C'}, optional
+    order : {'C', 'F', 'A'}, optional
         Read the elements of `a` using this index order, and place the
         elements into the reshaped array using this index order.  'C'
         means to read / write the elements using C-like index order,
         with the last axis index changing fastest, back to the first
-        axis index changing slowest. Other order types such as 'F'/'A'
-        may be added in the future.
+        axis index changing slowest. 'F' means to read / write the
+        elements using Fortran-like index order, with the first index
+        changing fastest, and the last index changing slowest. Note that
+        the 'C' and 'F' options take no account of the memory layout of
+        the underlying array, and only refer to the order of indexing.
+        'A' means to read / write the elements in Fortran-like index
+        order if `a` is Fortran *contiguous* in memory, C-like order
+        otherwise.
 
     Returns
     -------
     reshaped_array : ndarray
-        It will be always a copy of the original array. This behavior is different
-        from the official NumPy package where views of the original array may be
-        generated.
+        This will be a new view object if possible; otherwise, it will
+        be a copy.  Note there is no guarantee of the *memory layout* (C- or
+        Fortran- contiguous) of the returned array.
 
-    See Also
+
+    Notes
+    -----
+    It is not always possible to change the shape of an array without
+    copying the data. If you want an error to be raised when the data is copied,
+    you should assign the new shape to the shape attribute of the array::
+
+     >>> a = np.zeros((10, 2))
+     # A transpose makes the array non-contiguous
+     >>> b = a.T
+     # Taking a view makes it possible to modify the shape without modifying
+     # the initial object.
+
+    >>> a = np.arange(6).reshape((3, 2))
+    >>> a
+    array([[0., 1.],
+       [2., 3.],
+       [4., 5.]])
+
+    You can think of reshaping as first raveling the array (using the given
+    index order), then inserting the elements from the raveled array into the
+    new array using the same kind of index ordering as was used for the
+    raveling.
+
+    >>> np.reshape(a, (2, 3)) # C-like index ordering
+    array([[0., 1., 2.],
+       [3., 4., 5.]])
+
+    - order only support C-order
+    - input not support scalar
+    - not support zero-size shape
+
+    Examples
     --------
-    ndarray.reshape : Equivalent method.
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> np.reshape(a, 6)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
 
 Review comment:
   My output shows float type here.
   <img width="195" alt="image" src="https://user-images.githubusercontent.com/33736316/60319731-fde9ee00-99a9-11e9-83c1-03e7ead8847c.png">
   

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