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numpy.invert() in Python

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numpy.invert() function is used to Compute the bit-wise Inversion of an array element-wise. It computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays.

For signed integer inputs, the two’s complement is returned. In a two’s-complement system negative numbers are represented by the two’s complement of the absolute value.

Syntax : numpy.invert(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, ufunc ‘invert’)

Parameters :
x : [array_like] Input array.
out : [ndarray, optional] A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
**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 : [ndarray or scalar] Result. This is a scalar if x is scalar.

Code #1 : Working




# Python program explaining
# invert() function
  
import numpy as geek
in_num = 10
print ("Input  number : ", in_num)
    
out_num = geek.invert(in_num) 
print ("inversion of 10 : ", out_num) 


Output :

Input  number :  10
inversion of 10 :  -11

 
Code #2 :




# Python program explaining
# invert() function
  
import numpy as geek
  
in_arr = [2, 0, 25]
print ("Input array : ", in_arr)
    
out_arr = geek.invert(in_arr) 
print ("Output array after inversion: ", out_arr) 


Output :

Input array :  [2, 0, 25]
Output array after inversion:  [ -3  -1 -26]

 
Code #3 :




# Python program explaining
# invert() function
  
import numpy as geek
  
in_arr = [True, False]
print("Input array : ", in_arr) 
  
    
out_arr = geek.invert(in_arr) 
print ("Output array after inversion: ", out_arr) 


Output :

Input array :  [True, False]
Output array after inversion:  [False  True]


Last Updated : 29 Nov, 2018
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