numpy.float_power() in Python

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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_power differs 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 arr2

Code 1 : arr1 raised to arr2

# 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 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 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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