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_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]])