numpy.amax¶

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
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 input array.
If the default value is passed, then keepdims will not be passed through to the
amax
method of subclasses ofndarray
, however any nondefault value will be. If the subclassessum
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
 Elementwise maximum of two arrays, propagating any NaNs.
fmax
 Elementwise maximum of two arrays, ignoring any NaNs.
argmax
 Return the indices of the maximum values.
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 elementwise comparison of 2 arrays; whena.shape[0]
is 2,maximum(a[0], a[1])
is faster thanamax(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=float) >>> b[2] = np.NaN >>> np.amax(b) nan >>> np.nanmax(b) 4.0