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 datatype of prototype define these same attributes of the returned array.
 dtype : datatype, 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 Corder, ‘F’ means Forder, ‘A’ means ‘F’ if
prototype
is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout ofprototype
as closely as possible.New in version 1.6.0.
 subok : bool, optional.
If True, then the newly created array will use the subclass type of ‘a’, otherwise it will be a baseclass 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
orones_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 arraylike >>> 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, 1.48219694e323, 2.00000572e+000], # uninitialized [ 4.38791518e305, 2.00000715e+000, 4.17269252e309]])