What is numpy.power() in Python?
Python’s numpy.power() raises the first array elements to the power of second array elements. The illustration below shows this functionality:
Syntax
numpy.power() is declared as follows:
numpy.power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'power'>
In the syntax given above, x1 and x2 are non-optional parameters, and the rest are optional parameters.
A universal function (ufunc) is a function that operates on ndarrays in an element-by-element fashion. The
power()method is a universal function.
Parameters
The numpy.power() method takes the following compulsory parameters:
-
x1[array-like] - input array elements that act as the base. -
x2[array-like] - input array elements that acts as the exponent. If theshape the shape of an array is the number of elements in each dimension x1andx2is different, they must be broadcastable to a common shape for representing the output.
The numpy.power() method takes the following optional parameters:
Parameter | Description |
out | represents the location into which the output of the method is stored. |
where | True value indicates that a universal function should be calculated at this position. |
casting | controls the type of datacasting that should occur. The same_kind option indicates that safe casting or casting within the same kind should take place. |
order | controls the memory layout order of the output function. The option K means reading the elements in the order they occur in memory. |
dtype | represents the desired data type of the array. |
subok | decides if subclasses should be made or not. If True, subclasses will be passed through. |
Return value
numpy.power() returns the bases in x1 raised to the exponents in x1.
-
If
x2is negative, aValueErroris raised. -
If both
x1andx2are scalars, the return type is scalar.
Examples
In the example below, the bases in array arr1 are raised to the exponents in the array arr2:
import numpy as nparr1 = np.array([2, 3, 4])arr2 = np.array([2, 2, 2])print (np.power(arr1, arr2))
In the example below, the bases in array arr3 are raised to the exponents in the array arr4:
import numpy as nparr3 = np.array([[1, 2, 3], [4, 5, 6]])arr4 = np.array([[1, 2, 3], [4, 5, 6]])print (np.power(arr3, arr4))
The following example shows that the ValueError is raised when an exponent is a negative number.
import numpy as nparr5 = np.array([1, 2, 3])arr6 = np.array([-1, 3, -5])print (np.power(arr5, arr6))
