numpy.amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
[source]
Return the maximum of an array or maximum along an axis.
Parameters: 


Returns: 

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 elementwise 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]) >>> np.amax(a, where=[False, True], initial=1, axis=0) array([1, 3]) >>> b = np.arange(5, dtype=float) >>> b[2] = np.NaN >>> np.amax(b) nan >>> np.amax(b, where=~np.isnan(b), initial=1) 4.0 >>> np.nanmax(b) 4.0
You can use an initial value to compute the maximum of an empty slice, or to initialize it to a different value:
>>> np.max([[50], [10]], axis=1, initial=0) array([ 0, 10])
Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.
>>> np.max([5], initial=6) 6 >>> max([5], default=6) 5
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.amax.html