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# numpy.zeros_like

numpy.zeros_like(a, dtype=None, order='K', subok=True, shape=None, *, device=None)

  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.

    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.

    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.

    shape int 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.

    device str, optional

      The device on which to place the created array. Default: None. For Array-API interoperability only, so must be `"cpu"` if passed.

      New in version 2.0.0.

  Returns:
    out ndarray

      Array of zeros with the same shape and type as a.

  See also

  `empty_like`

    Return an empty array with shape and type of input.

  `ones_like`

    Return an array of ones with shape and type of input.

  `full_like`

    Return a new array with shape of input filled with value.

  `zeros`

    Return a new array setting values to zero.

  Examples

      >>> import numpy as np
      >>> 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.float64)
      >>> y
      array([0., 1., 2.])
      >>> np.zeros_like(y)
      array([0.,  0.,  0.])