#### Previous topic

numpy.ma.empty

#### Next topic

numpy.ma.masked_all

# numpy.ma.empty_like

`numpy.ma.``empty_like`(a, dtype=None, order='K', subok=True) = <numpy.ma.core._convert2ma instance at 0x52b6262c>

  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.
  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 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]])