The all() function of numpy

Overview:

The numpy.all() function reduces an NumPy array-like either column-wise or row-wise using logical AND.
When the value of the axis parameter is 0 the reduction is column-wise. When it is 1 the reduction is row-wise. When axis = None the entire array is reduced to a Boolean value and returned as given by the Example 2.
The behaviour of numpy.all() is in contrast to the numpy.any() function. The numpy.any() returns True, when any of the elements are True for a given axis or the entire array. The all() and any() methods of numpy.ndarray provides Python code examples that compares and contrasts the ndarray equivalent of the numpy.all() and numpy.any() functions.

Example 1:

# Example program that uses numpy.all()
# to get the Boolean AND results of the ndarray
# elements - row-wise (axis = 1), column-wise (axis = 0)
import numpy
from random import randrange

# Create an NDArray
n1 = numpy.ndarray(shape = (4, 3), dtype = numpy.int32)

# Fill in the array with random Boolean values
for i in range(0, 4):
for j in range(0, 3):
n1[i][j] = randrange(0, 2)

print("The ndarray elements:")
print(n1)
print(n1.shape)

# Reduce through logical AND - row based
print("Logical AND results when axis = 1:")
x = numpy.all(n1, 1)
print(x)
print(x.shape)

# Reduce through logical AND - column based
print("Logical AND results when axis = 0:")
x = numpy.all(n1, 0)
print(x)
print(x.shape)

Output:

The ndarray elements:

[[0 1 0]

[0 1 0]

[1 1 0]

[0 1 0]]

(4, 3)

Logical AND results when axis = 1:

[False False False False]

(4,)

Logical AND results when axis = 0:

[False True False]

(3,)

Example 2 - Reducing the whole ndarray using logical AND:

# Example Python program that reduces the whole ndarray
# by applying logical AND on its elements - (axis = None)
import numpy
from random import randrange

# Create an NDArray
n1 = numpy.ndarray(shape = (3, 3), dtype = numpy.int32)

# Fill in the array
for i in range(0, 3):
for j in range(0, 3):
n1[i][j] = randrange(0, 2)

print("Array elements:")
print(n1)
print(n1.shape)

# Reduce the whole array using logical AND
print("Logical AND results when axis = None:")
result = numpy.all(n1)
print(result)
print(type(result))

# The result will be True only when all the elements of the
# ndarray evaluate to True
n1[:,:] = 1
print("The ndarray with all ones and axis = None:")
print(n1)
print("Result:")
result = numpy.all(n1)
print(result)

Output:

Array elements:

[[1 1 0]

[0 1 1]

[0 0 1]]

(3, 3)

Logical AND results when axis = None:

False

The ndarray with all ones and axis = None:

[[1 1 1]

[1 1 1]

[1 1 1]]

Result:

True