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
 prototypearray_like
The shape and datatype of prototype define these same attributes of the returned array.
 dtypedatatype, 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 of prototype as closely as possible.
New in version 1.6.0.
 subokbool, optional.
If True, then the newly created array will use the subclass type of prototype, otherwise it will be a baseclass array. Defaults to True.
 shapeint 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
 outndarray
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]])