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# Numpy Logical Functions - OR | AND | NOT | XOR with examples

In this numpy tutorial, we will discuss about different logical functions performed on the numpy array:

• numpy logical or & np.logical_or examples,
• numpy logical and & numpy logical and example,
• numpy logical xor & numpy.logical xor example,
• numpy logical not

Introduction to Numpy

numpy stands for numeric python which is used to perform mathematical operations on arrays.

It is a module in which we have to import from the python.

Syntax to import:

``import numpy``

We can also use alias for the module

For example,

``import numpy as np``

We can directly use np to call the numpy module.

Numpy Array

An array is an one dimensional data structure used to store single data type data.

I.E It will only store all integer data or all string type data.or all float type data.

We can create an numpy array by using array() function.

Syntax:

``numpy.array(elements)``

where, elements are the input data elements.

### numpy logical or → logical_or()

numpy logical or is performed on the two numpy arrays in which element wise computation is performed.

So, it will check the two elements from the both the arrays and return the boolean value - True/False.

Let's see the Internal working of this function - logical_or().

Scenario:

``````array1=[1,2,3,0]

array2=[0,1,2,0]

Working:

It will return True if any element is greater than 0, otherwise False

[ 1 logical_or 0] - True

[ 2 logical_or 1] - True

[ 3 logical_or 2] - True

[ 0 logical_or 0] - False

Result:

[ True True True False]``````

Example: np.logical_or examples

In this numpy logical or example, we are creating two numpy arrays with 5 integer elements and perform logical_or() function.

``````#importing numpy module
import numpy

#creating array1
arraydata1=numpy.array([1,2,3,4,0])

#creating array2
arraydata2=numpy.array([1,2,10,4,0])

#display two arrays
print(arraydata1,arraydata2)

#perform logical_or() logical operation
print(numpy.logical_or(arraydata1,arraydata2))
``````

Outputnumpy logical or

``````[1 2 3 4 0] [ 1  2 10  4  0]
[ True  True  True  True False]``````

### numpy logical and → logical_and()

numpy logical and is performed on the two numpy arrays in which element wise computation is performed.

So, it will check the two elements from the both the arrays and return the boolean value - True/False.

Let's see the Internal working of this function - numpy logical_and().

Scenario:

``````array1=[1,2,3,0]

array2=[0,1,2,0]

Working:

It will return True if both the elements are greater than 0, otherwise False

[ 1 logical_or 0] - False

[ 2 logical_or 1] - True

[ 3 logical_or 2] - True

[ 0 logical_or 0] - False

Result:

[ False True True False]``````

Example: numpy logical and example

In this example, we are creating two numpy arrays with 5 integer elements and perform logical_and() function.

``````#importing numpy module
import numpy

#creating array1
arraydata1=numpy.array([1,2,3,4,0])

#creating array2
arraydata2=numpy.array([1,2,10,4,0])

#display two arrays
print(arraydata1,arraydata2)

#perform logical_and() logical operation
print(numpy.logical_and(arraydata1,arraydata2))
``````

Outputnumpy logical and

``````[1 2 3 4 0] [ 1  2 10  4  0]
[ True  True  True  True False]``````

### numpy logical xor → logical_xor()

numpy logical xor is performed on the two numpy arrays in which element wise computation is performed.

So, it will check the two elements from the both the arrays and return the boolean value - True/False.

Let's see the Internal working of this function.

Scenario:

``````array1=[1,2,3,0]

array2=[0,1,2,0]

Working:

It will return True if one element is greater than 0 and other is 0, otherwise False

[ 1 logical_or 0] - True

[ 2 logical_or 1] - False

[ 3 logical_or 2] - False

[ 0 logical_or 0] - False

Result:

[ True False False False]``````

Example: numpy.logical xor example

In this numpy.logical xor example, we are creating two numpy arrays with 5 integer elements and perform logical_xor() function.

``````#importing numpy module
import numpy

#creating array1
arraydata1=numpy.array([0,2,3,4,0])

#creating array2
arraydata2=numpy.array([1,2,10,4,0])

#display two arrays
print(arraydata1,arraydata2)

#perform logical_xor() logical operation
print(numpy.logical_xor(arraydata1,arraydata2))
``````

Outputnumpy.logical xor example

``````[0 2 3 4 0] [ 1  2 10  4  0]
[ True False False False False]``````

Lets learn about numpy logical not.

### numpy logical not →logical_not()

numpy logical not is performed on the one numpy array in which element wise computation is performed.

So, it will check the each element in the array and return the boolean value - True/False.

Let's see the Internal working of this function - numpy logical not.

Scenario:

``````array=[1,2,3,0]

Working:

It will return True if the element is equal to 0, otherwise False

[ logical_not 1] - False
[ logical_not 2] - False
[ logical_not 3] - False
[ logical_not 0] - True

Result:

[ False False False True]``````

Example: numpy logical not operator

In this numpy logical not operator example, we are creating two numpy arrays with 5 integer elements and perform logical_not() function on two arrays separately.

``````#importing numpy module
import numpy

#creating array1
arraydata1=numpy.array([0,2,3,4,0])

#creating array2
arraydata2=numpy.array([1,2,10,4,0])

#display two arrays
print(arraydata1,arraydata2)

#perform logical_not() logical operation on first array
print(numpy.logical_not(arraydata1))

#perform logical_not() logical operation on second array
print(numpy.logical_not(arraydata2))``````

Output: numpy logical not operator

``````[0 2 3 4 0] [ 1  2 10  4  0]
[ True False False False  True]
[False False False False  True]``````

This concludes our session numpy logical or, np.logical_or examples, numpy logical and, numpy logical and example, numpy logical xor, numpy.logical xor example, numpy logical not, numpy logical not operator.

Numpy

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