numpy.empty_like — NumPy v1.11 Manual (original) (raw)
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 data-type of a define these same attributes of the returned array. dtype : data-type, 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 C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. New in version 1.6.0. subok : bool, optional. 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. |
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| Returns: | out : ndarray Array of uninitialized (arbitrary) data with the same shape and type as a. |
See also
Return an array of ones with shape and type of input.
Return an array of zeros with shape and type of input.
Return a new uninitialized array.
Return a new array setting values to one.
Return a new array setting values to zero.
Notes
This function does not initialize the returned array; to do that usezeros_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], #random [ 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],#random [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])