# numpy.full_like

numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None)

  Return a full 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.

    fill_value array_like

      Fill value.

    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.

      New in version 1.17.0.

  Returns:
    out ndarray

      Array of fill_value 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.

  `zeros_like`

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

  `full`

    Return a new array of given shape filled with value.

  Examples

      >>> x = np.arange(6, dtype=int)
      >>> np.full_like(x, 1)
      array([1, 1, 1, 1, 1, 1])
      >>> np.full_like(x, 0.1)
      array([0, 0, 0, 0, 0, 0])
      >>> np.full_like(x, 0.1, dtype=np.double)
      array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
      >>> np.full_like(x, np.nan, dtype=np.double)
      array([nan, nan, nan, nan, nan, nan])

      >>> y = np.arange(6, dtype=np.double)
      >>> np.full_like(y, 0.1)
      array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])

      >>> y = np.zeros([2, 2, 3], dtype=int)
      >>> np.full_like(y, [0, 0, 255])
      array([[[  0,   0, 255],
              [  0,   0, 255]],
             [[  0,   0, 255],
              [  0,   0, 255]]])