NumPy logical_and() function

Overview:

The numpy.logical_and() function applies the logical AND between the elements of the NumPy array-likes and/or scalar operands and returns the result.
For the logical AND opreation only if both the operands evaluate to True the resultant value is True. Otherwise, the resultant value is False.
The numpy.bitwise_and() operator applies the logical AND operation on each bit pair corresponding to the elements of the two NumPy array-likes.

Truth Table:

Operand 1	Operand 2	Operand 1 AND Operand 2
0	0	0
0	1	0
1	0	0
1	1	1

Example 1:

# Example Python program that performs logical AND operation
# between two numpy ndarrays and printing the results

import numpy
from random import randrange

# Fill the n-dimensional array
def fillNDArray(array_in):
   for i in range(0, 3):
       for j in range(0, 3):
           if i == 0 or j == 0:
               array_in[i][j] = 0
           else:
               array_in[i][j] = randrange(100)

# Create two ndarray instances
n1 = numpy.ndarray(shape = (3, 3), dtype = numpy.int32)
n2 = numpy.ndarray(shape = (3, 3), dtype = numpy.int32)

# Fill the arrays
fillNDArray(n1)
fillNDArray(n2)

print("Array 1:")
print(n1)

print("Array 2:")
print(n2)

# Apply logical and
n3 = numpy.logical_and(n1, n2)

# Print the result of logical_and()
print("Array 1 AND Array 2:")
print(n3)

Output:

Array 1:

[[ 0 0 0]

[ 0 88 38]

[ 0 4 92]]

Array 2:

[[ 0 0 0]

[ 0 26 65]

[ 0 23 80]]

Array 1 AND Array 2:

[[False False False]

[False True True]

[False True True]]

Example 2:

While creating the output ndarray, the NumPy broadcasting rules are applied as the dimensions of the two ndarray instances are different.

# Example Python program that performs logical AND operation
# between two ndarrays containing Boolean values
import numpy

# Create and fill ndarrays
n1 = numpy.ndarray(shape = (3, 3),
dtype = numpy.int32)
n2 = numpy.ndarray(shape = (1, 3),
dtype = numpy.int32)
n2[0][0] = True
n2[0][1] = False
n2[0][2] = True

for i in range(0, 3):
for j in range(0,3):
n1[i][j] = numpy.logical_and(i,j)

n3 = numpy.logical_and(n1, n2)
print("Array 1:")
print(n1)
print("Shape:")
print(n1.shape)
print("Array 2:")
print(n2)
print("Shape:")
print(n2.shape)
print("Array 1 AND Array 2:")
print(n3)
print("Shape:")
print(n3.shape)

Output:

Array 1:

[[0 0 0]

[0 1 1]

[0 1 1]]

Shape:

(3, 3)

Array 2:

[[1 0 1]]

Shape:

(1, 3)

Array 1 AND Array 2:

[[False False False]

[False False True]

[False False True]]

Shape:

(3, 3)

Example 3:

# Example Python program that performs logical AND operation
# between the elements of two array-likes containing relational expressions

import numpy

a = 10
b = -2

# Lists of relational expressions
e1 = [a < b, a == b, a > b]
e2 = [a < b, a == b, a > b]

e3 = numpy.logical_and(e1, e2)

print(e3)

Output:

[False False True]