numpy.amax(a, axis=None, out=None, keepdims=<class numpy._globals._NoValue>)
[source]
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. New in version 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 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 input array. If the default value is passed, then |
---|---|
Returns: |
amax : ndarray or scalar Maximum of |
See also
amin
nanmax
maximum
fmax
argmax
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)
.
>>> 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
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.amax.html