numpy.empty_like¶

numpy.
empty_like
(a, dtype=None, order='K', subok=True)¶ Return a new array with the same shape and type as a given array.
Parameters: a : array_like
The shape and datatype of a 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
a
is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout ofa
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.
Returns: out : ndarray
Array of uninitialized (arbitrary) data with the same shape and type as a.
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.
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
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], #random [ 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],#random [ 4.38791518e305, 2.00000715e+000, 4.17269252e309]])