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 isused. 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. Mustbe of the same shape and buffer length as the expected output.Seedoc.ufuncs (Section “Output arguments”) for more details. - keepdims:bool, optional
If this is set to True, the axes which are reduced are leftin 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, thenkeepdims will not bepassed through to theamax method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised. - initial:scalar, optional
The minimum value of an output element. Must be present to allowcomputation on empty slice. Seereduce for details.
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| Returns: | - amax:ndarray or scalar
Maximum ofa. Ifaxis is None, the result is a scalar value.Ifaxis is given, the result is an array of dimensiona.ndim-1.
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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, thecorresponding max value will be NaN as well. To ignore NaN values(MATLAB behavior), please use nanmax.
Don’t useamax for element-wise 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))>>>aarray([[0, 1], [2, 3]])>>>np.amax(a)# Maximum of the flattened array3>>>np.amax(a,axis=0)# Maxima along the first axisarray([2, 3])>>>np.amax(a,axis=1)# Maxima along the second axisarray([1, 3])
>>>b=np.arange(5,dtype=float)>>>b[2]=np.NaN>>>np.amax(b)nan>>>np.nanmax(b)4.0
You can use an initial value to compute the maximum of an empty slice, orto 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 themaximum is determined, unlike for the default argument Python’s maxfunction, which is only used for empty iterables.
>>>np.max([5],initial=6)6>>>max([5],default=6)5
