Last Updated : 29 Nov, 2018
numpy.float_power(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) :Array element from first array is raised to the power of element from second element(all happens element-wise). Both arr1 and arr2 must have same shape.
float_powerdiffers from the power function in that integers, float16, and float32 are promoted to floats with a minimum precision of float64 such that result is always inexact. This function will return a usable result for negative powers and seldom overflow for +ve powers.
Parameters :
arr1 : [array_like]Input array or object which works as base.
arr2 : [array_like]Input array or object which works as exponent.
out : [ndarray, optional]Output array with same dimensions as Input array,
placed with result.
**kwargs : Allows you to pass keyword variable length of argument to a function.
It is used when we want to handle named argument in a function.
where : [array_like, optional]True value means to calculate the universal
functions(ufunc) at that position, False value means to leave the
value in the output alone.
Return :
An array with elements of arr1 raised to exponents in arr2Code 1 : arr1 raised to arr2 Python
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = [2, 2, 2, 2, 2]
arr2 = [2, 3, 4, 5, 6]
print ("arr1 : ", arr1)
print ("arr1 : ", arr2)
# output_array
out = np.float_power(arr1, arr2)
print ("\nOutput array : ", out)
Output :
arr1 : [2, 2, 2, 2, 2] arr1 : [2, 3, 4, 5, 6] Output array : [ 4. 8. 16. 32. 64.]Code 2 : elements of arr1 raised to exponent 2 Python
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = np.arange(8)
exponent = 2
print ("arr1 : ", arr1)
# output_array
out = np.float_power(arr1, exponent)
print ("\nOutput array : ", out)
Output :
arr1 : [0 1 2 3 4 5 6 7] Output array : [ 0. 1. 4. 9. 16. 25. 36. 49.]Code 3 : float_power handling results if arr2 has -ve elements Python
# Python program explaining
# float_power() function
import numpy as np
# input_array
arr1 = [2, 2, 2, 2, 2]
arr2 = [2, -3, 4, -5, 6]
print ("arr1 : ", arr1)
print ("arr2 : ", arr2)
# output_array
out = np.float_power(arr1, arr2)
print ("\nOutput array : ", out)
Output :
arr1 : [2, 2, 2, 2, 2]
arr2 : [2, -3, 4, -5, 6]
Output array : [ 4.00000000e+00 1.25000000e-01 1.60000000e+01
3.12500000e-02 6.40000000e+01]References : https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.float_power.html#numpy.float_power
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