numpy.amax

numpy.amax(a, axis=None, out=None, keepdims=False)

Return the maximum of an array or maximum along an axis.

Parameters:

a : array_like

Input data.

axis : int, optional

Axis along which to operate. By default, flattened input is used.

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.

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