What is numpy.amax() in Python?
Python’s numpy.amax() computes the maximum of an array or of an array along a specified axis.
Syntax
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.
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 array.flattened input converted from multi-dimensional to a one-dimensional array -
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 theamax()method of sub-classes ofndarray. 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.
Return value
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
- 1.a.ndim the number of array dimensions -
If an axis is None, the output is scalar.
-
If one or both of the values being compared is
NaN(Not a Number),NaNis returned.
Examples
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))
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