# numpy.ma.empty_like¶

numpy.ma.empty_like(a, dtype=None, order='K', subok=True) = <numpy.ma.core._convert2ma instance at 0x274add0>

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

Parameters : a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. 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 a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. 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 a.

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.
empty
Return a new uninitialized array.
ones
Return a new array setting values to one.
zeros
Return a new array setting values to zero.

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]])
```

numpy.ma.empty