#### Previous topic

numpy.full

#### Next topic

numpy.array

# numpy.full_like

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

  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 : scalar 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.
  Returns: | out : ndarray Array of fill_value with the same shape and type as a.

  See also

  `zeros_like`
    Return an array of zeros with shape and type of input.
  `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.
  `full`
    Fill a new array.

  Examples

      >>> x = np.arange(6, dtype=np.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])