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# numpy.zeros_like

`numpy.``zeros_like`(a, dtype=None, order='K', subok=True)

  Return an array of zeros 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 zeros with the same shape and type as a.

  See also

  `ones_like`
    Return an array of ones with shape and type of input.
  `empty_like`
    Return an empty array with shape and type of input.
  `zeros`
    Return a new array setting values to zero.
  `ones`
    Return a new array setting values to one.
  `empty`
    Return a new uninitialized array.

  Examples

      >>> x = np.arange(6)
      >>> x = x.reshape((2, 3))
      >>> x
      array([[0, 1, 2],
             [3, 4, 5]])
      >>> np.zeros_like(x)
      array([[0, 0, 0],
             [0, 0, 0]])

      >>> y = np.arange(3, dtype=np.float)
      >>> y
      array([ 0.,  1.,  2.])
      >>> np.zeros_like(y)
      array([ 0.,  0.,  0.])