NumPy Array – Logical Operations
Last Updated :
15 Mar, 2021
Logical operations are used to find the logical relation between two arrays or lists or variables. We can perform logical operations using NumPy between two data. Below are the various logical operations we can perform on Numpy arrays:
AND
The numpy module supports the logical_and operator. It is used to relate between two variables. If two variables are 0 then output is 0, if two variables are 1 then output is 1 and if one variable is 0 and another is 1 then output is 0.
Syntax:
numpy.logical_and(var1,var2)
Where, var1 and var2 are a single variable or a list/array.
Return type: Boolean value (True or False)
Example 1:
This code gives demo on boolean operations with logical_and operator.
Python3
import numpy as np
list1 = [ True , False , True , False ]
list2 = [ True , True , False , True ]
print ( 'Operation between two lists = ' ,
np.logical_and(list1, list2))
|
Output:
Example 2:
Python3
import numpy as np
list1 = [ 1 , 2 , 3 , 4 , 5 , 0 ]
list2 = [ 0 , 1 , 2 , 3 , 4 , 0 ]
print ( 'Operation between two lists:' ,
np.logical_and(list1, list2))
|
Output:
OR
The NumPy module supports the logical_or operator. It is also used to relate between two variables. If two variables are 0 then output is 0, if two variables are 1 then output is 1 and if one variable is 0 and another is 1 then output is 1.
Syntax:
numpy.logical_or(var1,var2)
Where, var1 and var2 are a single variable or a list/array.
Return type: Boolean value (True or False)
Example:
Python3
import numpy as np
print ( 'logical_or operation = ' ,
np.logical_or( True , False ))
a = 2
b = 6
print ( 'logical or Operation between two variables = ' ,
np.logical_or(a, b))
a = 0
b = 0
print ( 'logical or Operation between two variables = ' ,
np.logical_or(a, b))
list1 = [ 1 , 2 , 3 , 4 , 5 , 0 ]
list2 = [ 0 , 1 , 2 , 3 , 4 , 0 ]
print ( 'Operation between two lists = ' ,
np.logical_or(list1, list2))
|
Output:
NOT
The logical_not operation takes one value and converts it into another value. If the value is 0, then output is 1, if value is greater than or equal to 1 output is 0.
Syntax:
numpy.logical_not(var1)
Where, var1is a single variable or a list/array.
Return type: Boolean value (True or False)
Example:
Python3
import numpy as np
print ( 'logical_not operation = ' ,
np.logical_not( True ))
a = 2
b = 6
print ( 'logical_not Operation = ' ,
np.logical_not(a))
print ( 'logical_not Operation = ' ,
np.logical_not(b))
list1 = [ 1 , 2 , 3 , 4 , 5 , 0 ]
print ( 'Operation in list = ' ,
np.logical_not(list1))
|
Output:
XOR
The logical_xor performs the xor operation between two variables or lists. In this operation, if two values are same it returns 0 otherwise 1.
Syntax:
numpy.logical_xor(var1,var2)
Where, var1 and var2 are a single variable or a list/array.
Return type: Boolean value (True or False)
Example:
Python3
import numpy as np
print ( 'Operation between true and true ( 1 and 1) = ' ,
np.logical_xor( True , True ))
print ( 'Operation between true and false ( 1 and 0) = ' ,
np.logical_xor( True , False ))
print ( 'Operation between false and true ( 0 and 1) = ' ,
np.logical_xor( False , True ))
print ( 'Operation between false and false (0 and 0)= ' ,
np.logical_xor( False , False ))
list1 = [ True , False , True , False ]
list2 = [ True , True , False , True ]
print ( 'Operation between two lists = ' ,
np.logical_xor(list1, list2))
list1 = [ 1 , 2 , 3 , 4 , 5 , 0 ]
list2 = [ 0 , 1 , 2 , 3 , 4 , 0 ]
print ( 'Operation between two lists = ' ,
np.logical_xor(list1, list2))
|
Output:
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