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# numpy.amax

`numpy.``amax`(a, axis=None, out=None, keepdims=False)

  Return the maximum of an array or maximum along an axis.

  Parameters : | a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. out : ndarray, optional Alternate output array in which to place the result. Must be of the same shape and buffer length as the expected output. See `doc.ufuncs` (Section “Output arguments”) for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.
  Returns : | amax : ndarray or scalar Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension `a.ndim - 1`.

  See also

  `nanmax`
    NaN values are ignored instead of being propagated.
  `fmax`
    same behavior as the C99 fmax function.
  `argmax`
    indices of the maximum values.

  Notes

  NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

  Examples

      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> np.amax(a)
      3
      >>> np.amax(a, axis=0)
      array([2, 3])
      >>> np.amax(a, axis=1)
      array([1, 3])

      >>> b = np.arange(5, dtype=np.float)
      >>> b[2] = np.NaN
      >>> np.amax(b)
      nan
      >>> np.nanmax(b)
      4.0

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