Help on function amax in module numpy.core.fromnumeric:
amax(a, axis=None, out=None, keepdims=<class numpy._globals._NoValue>)
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.
.. 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 : 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`.
If the default value is passed, then `keepdims` will not be
passed through to the `amax` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
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