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# numpy.nanmax

`numpy.``nanmax`(a, axis=None)

  Return the maximum of array elements over the given axis ignoring any NaNs.

  Parameters: | a : array_like Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the maximum is computed.The default is to compute the maximum of the flattened array.
  Returns: | y : ndarray An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. The the same dtype as a is returned.

  See also

  `numpy.amax`
    Maximum across array including any Not a Numbers.
  `numpy.nanmin`
    Minimum across array ignoring any Not a Numbers.
  `isnan`
    Shows which elements are Not a Number (NaN).
  `isfinite`
    Shows which elements are not: Not a Number, positive and negative infinity

  Notes

  Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

  If the input has a integer type, an integer type is returned unless the input contains NaNs and infinity.

  Examples

      >>> a = np.array([[1, 2], [3, np.nan]])
      >>> np.nanmax(a)
      3.0
      >>> np.nanmax(a, axis=0)
      array([ 3.,  2.])
      >>> np.nanmax(a, axis=1)
      array([ 2.,  3.])

  When positive infinity and negative infinity are present:

      >>> np.nanmax([1, 2, np.nan, np.NINF])
      2.0
      >>> np.nanmax([1, 2, np.nan, np.inf])
      inf

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