# numpy.empty_like¶

`numpy.``empty_like`(prototype, dtype=None, order='K', subok=True)

Return a new array with the same shape and type as a given array.

Parameters: prototype : array_like The shape and data-type of prototype define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. 1.6.0 新版功能. order : {‘C’, ‘F’, ‘A’, or ‘K’}, optional Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if `prototype` is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of `prototype` as closely as possible. 1.6.0 新版功能. subok : bool, optional. If True, then the newly created array will use the sub-class type of ‘a’, otherwise it will be a base-class array. Defaults to True. out : ndarray Array of uninitialized (arbitrary) data with the same shape and type as prototype.

`ones_like`
Return an array of ones with shape and type of input.
`zeros_like`
Return an array of zeros with shape and type of input.
`full_like`
Return a new array with shape of input filled with value.
`empty`
Return a new uninitialized array.

Notes

This function does not initialize the returned array; to do that use `zeros_like` or `ones_like` instead. It may be marginally faster than the functions that do set the array values.

Examples

```>>> a = ([1,2,3], [4,5,6])                         # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821,           3],    #random
[          0,           0, -1073741821]])
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
[  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])
```