Python’s numpy.amax()
computes the maximum of an array or of an array along a specified axis.
numpy.amax()
is declared as follows:
numpy.amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
In the syntax given above, a
is the non-optional parameter, and the rest are optional parameters.
numpy.amax()
takes the following non-optional parameter:
a
[array-like] - input array.numpy.amax()
takes the following optional parameters:
axis
[None, int, tuples of int] - axis along which we want the maximum value to be computed. The default is the
out
[ndarray] - represents the location into which the output is stored.
keepdims
[boolean] - True value ensures that the reduced axes are left as dimensions with size one in the output. This ensures that the output is broadcasted correctly against the input array. If a non-default value is passed, keepdims will be passed through to the amax()
method of sub-classes of ndarray
. In the case of the default value, this will not be done.
initial
[scalar] - minimum value of the output element.
where
[array_like of bool] - represents the elements to compare for the maximum.
numpy.amax()
returns the maximum of an array. The return type is ndarray or scalar, depending on the input.
If an axis is specified, the output is an array of dimension
If an axis is None, the output is scalar.
If one or both of the values being compared is NaN
(Not a Number), NaN
is returned.
The following example outputs the maximum value of the array arr
where the axis
parameter is not specified:
import numpy as nparr = np.array([1,2,5,6,0])print(np.amax(arr))
The example below outputs the maximum of the array arr1
where axis
is specified as 0 and 1.
import numpy as nparr1 = np.array([[2,4,5], [7,10,1]])#Maximum values along the first axisprint(np.amax(arr1, axis = 0))#Maximum values along the second axisprint(np.amax(arr1, axis = 1))