numpy.float_power () in Python

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 ( " Output array: " , out) < / p>

Output:

 arr1 : [2, 2, 2, 2, 2] arr1: [2, 3, 4, 5, 6] Output array: [4. 8. 16. 32. 64.] 

Code 2: arr1 elements raised to power of 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 ( "Output array:" , out)

Output:

 arr1: [0 1 2 3 4 5 6 7] Output array: [0. 1. 4 . 9. 16. 25. 36. 49.] 

Code 3: results of processing float_power 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 ( "Output 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]  

Links:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/ numpy.float_power.html # numpy.float_power
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