#### Previous topic

numpy.amin

#### Next topic

numpy.nanmin

# 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 : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative 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

  `amin`
    The minimum value of an array along a given axis, propagating any NaNs.
  `nanmax`
    The maximum value of an array along a given axis, ignoring any NaNs.
  `maximum`
    Element-wise maximum of two arrays, propagating any NaNs.
  `fmax`
    Element-wise maximum of two arrays, ignoring any NaNs.
  `argmax`
    Return the indices of the maximum values.

  `nanmin`, `minimum`, `fmin`

  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.

  Don’t use `amax` for element-wise comparison of 2 arrays; when `a.shape[0]` is 2, `maximum(a[0], a[1])` is faster than `amax(a, axis=0)`.

  Examples

      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> np.amax(a)           # Maximum of the flattened array
      3
      >>> np.amax(a, axis=0)   # Maxima along the first axis
      array([2, 3])
      >>> np.amax(a, axis=1)   # Maxima along the second axis
      array([1, 3])

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