# numpy.ma.masked_array.max

method

ma.masked_array.max(axis=None, out=None, fill_value=None, keepdims=<no value>)

  Return the maximum along a given axis.

  Parameters:
    axis None or int or tuple of ints, optional

      Axis along which to operate. By default, `axis` is None and the flattened input is used. .. versionadded:: 1.7.0 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 array_like, optional

      Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.

    fill_value scalar or None, optional

      Value used to fill in the masked values. If None, use the output of maximum_fill_value().

    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 array.

  Returns:
    amax array_like

      New array holding the result. If `out` was specified, `out` is returned.

  See also

  `ma.maximum_fill_value`

    Returns the maximum filling value for a given datatype.

  Examples

      >>> import numpy.ma as ma
      >>> x = [[-1., 2.5], [4., -2.], [3., 0.]]
      >>> mask = [[0, 0], [1, 0], [1, 0]]
      >>> masked_x = ma.masked_array(x, mask)
      >>> masked_x
      masked_array(
        data=[[-1.0, 2.5],
              [--, -2.0],
              [--, 0.0]],
        mask=[[False, False],
              [ True, False],
              [ True, False]],
        fill_value=1e+20)
      >>> ma.max(masked_x)
      2.5
      >>> ma.max(masked_x, axis=0)
      masked_array(data=[-1.0, 2.5],
                   mask=[False, False],
             fill_value=1e+20)
      >>> ma.max(masked_x, axis=1, keepdims=True)
      masked_array(
        data=[[2.5],
              [-2.0],
              [0.0]],
        mask=[[False],
              [False],
              [False]],
        fill_value=1e+20)
      >>> mask = [[1, 1], [1, 1], [1, 1]]
      >>> masked_x = ma.masked_array(x, mask)
      >>> ma.max(masked_x, axis=1)
      masked_array(data=[--, --, --],
                   mask=[ True,  True,  True],
             fill_value=1e+20,
                  dtype=float64)