添加numpy数组axis的几种方法（Colon,None,sliceinnumpyarrayindexers）

阅读python程序时遇到一个对numpy数据x的操作x[:,none,none]，当时被搞糊涂了。经google查到原来是扩展数组坐标轴（或shape)的另一种表达。查阅了python官方手册，引用在下面。官方手册对数据shape的扩展解释并不详尽，新手很难准确掌握，随便对此作更深入一点的讲解，希望能给大家提供点帮助。

一、官方手册：

numpy.expand_dims

numpy. expand_dims ( a,  axis ) [source]

Expand the shape of an array.

Insert a new axis that will appear at the axis position in the expanded array shape.

Note

Previous to NumPy 1.13.0, neither axis <</span> -a.ndim - 1 nor axis > a.ndim raised errors or put the new axis where documented. Those axis values are now deprecated and will raise an AxisError in the future.

Parameters:	a  :  array_like Input array. axis  :  int Position in the expanded axes where the new axis is placed.
Returns:	res  :  ndarray Output array. The number of dimensions is one greater than that of the input array.

See also

squeeze

The inverse operation, removing singleton dimensions

reshape

Insert, remove, and combine dimensions, and resize existing ones

doc.indexing, atleast_1d, atleast_2d, atleast_3d

Examples

>>>

>>> x = np.array([1,2])
>>> x.shape
(2,)

The following is equivalent to x[np.newaxis,:] or x[np.newaxis]:

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>>>

>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)

>>>

>>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,np.newaxis]
>>> y
array([[1],
       [2]])
>>> y.shape
(2, 1)

Note that some examples may use None instead of np.newaxis. These are the same objects:

>>>

>>> np.newaxis is None
Truer

二、更深入解释：

三、reshape官方手册

numpy.reshape

numpy. reshape ( a,  newshape,  order='C' ) [source]

Gives a new shape to an array without changing its data.

Parameters:

a  :  array_like Array to be reshaped. newshape  :  int or tuple of ints The new shape should be compatible with the original shape. If 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’, ‘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. ‘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 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. 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. >>> c = b.view() >>> c.shape = (20) AttributeError: incompatible shape for a non-contiguous array The order keyword gives the index ordering both for fetching the values from a, and then placing the values into the output array. For example, let’s say you have an array: >>> >>> 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]]) >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape array([[0, 1, 2], [3, 4, 5]]) >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering array([[0, 4, 3], [2, 1, 5]]) >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F') array([[0, 4, 3], [2, 1, 5]])