The numpy.float_power() function in NumPy is used to return a result, such that each element of an array x1 is raised to the power of each element of another array x2.
numpy.float_power
numpy.float_power(x1, x2, /, out=None, *, where=True)
The numpy.float_power() function takes the following parameter values:
x1: This represents an input array whose elements are the bases. This is a required parameter value.x2: This represents an array whose elements are the exponents. This is a required parameter value.out: This represents the location where the result is stored. This is an optional parameter value.where: This is the condition over which the input is being broadcast. At a given location where this condition is True, the resulting array will be set to the ufunc result. Otherwise, the resulting array will retain its original value. This is an optional parameter value.**kwargs: This represents the other keyword arguments. This is an optional parameter value.import numpy as np# creating input arraysx1 = np.array([1.1, 2.2, 3.6])x2 = np.array([2.1, 1.1, 2.5])# implementing the float_power() functionmyarray = np.float_power(x1, x2)print(x1)print(x2)print("The float_power values are: ", myarray)
numpy module.x1 and x2\, using the array() function.numpy.float_power() function on the input arrays. We assign the result to a variable called myarray.x1 and x2to the console.myarray to the console.