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.
The shape and data-type of prototype define these same attributes of the returned array.
Overrides the data type of the result.
New in version 1.6.0.
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.
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.
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.
Array of uninitialized (arbitrary) data with the same shape and type as prototype.
See also
ones_likeReturn an array of ones with shape and type of input.
zeros_likeReturn an array of zeros with shape and type of input.
full_likeReturn a new array with shape of input filled with value.
emptyReturn 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]])