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numpy.empty_like

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

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.

New in version 1.6.0.

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.

New in version 1.6.0.

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.

shape : int or sequence of ints, optional.

Overrides the shape of the result. If order=’K’ and the number of dimensions is unchanged, will try to keep order, otherwise, order=’C’ is implied.

New in version 1.17.0.

Returns:
out : ndarray

Array of uninitialized (arbitrary) data with the same shape and type as prototype.

See also

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],    # uninitialized
       [          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], # uninitialized
       [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])