The equal() function of NumPy module

Overview:

The numpy.equal() function checks whether two NumPy array-likes are equal element-wise. If the pair of elements match the result is True. The result is False otherwise. The results are returned as an ndarray containing Boolean values. A single Boolean value is returned if the compared input types are scalars.

Example 1:

# Example Python program that uses numpy.equal()
# to compare the elements of two ndarray instances
import numpy
import random

# Function to fill a 3x3 ndarray with double-precision
# floating point numbers
def fillDPArray(arr_in):
for x in range(0, 3):
for y in range(0, 3):
arr_in[x][y] = random.uniform(1, 2)

# Create ndarray instances of double-precision floating point numbers
dpArray1 = numpy.ndarray(shape = (3, 3), dtype = numpy.float64)
dpArray2 = numpy.ndarray(shape = (3, 3), dtype = numpy.float64)

fillDPArray(dpArray1)
fillDPArray(dpArray2)

# Do not want all the results as False, as
# we do not want to rely on the likelyhood of having
# random.uniform() generating two equal numbers in short
# span of time/interval
dpArray2[0, 0] = dpArray1[0, 0]

print("Array1:")
print(dpArray1)

print("Array2:")
print(dpArray2)

print("Array1 = Array2")
print("(Element-wise):")
results = numpy.equal(dpArray1, dpArray2)
print(results)

Output:

Array1:

[[1.78418893 1.34502285 1.84007858]

[1.29815828 1.17697561 1.67392455]

[1.63705878 1.85393845 1.24246788]]

Array2:

[[1.78418893 1.73576206 1.47579266]

[1.44167562 1.47260249 1.42296846]

[1.07237369 1.01274364 1.12999644]]

Array1 = Array2

(Element-wise):

[[ True False False]

[False False False]

[False False False]]

Example 2:

# Example Python program to check for equality between
# elements pairs drawn from two NumPy ndarray instances
import numpy

exps1 = numpy.array([[5,    10.1, 15/5],
               [-20, round(25.11), 30 * 2],
               [pow(10, 2), len(range(1, 5)), sum([-1, -2, -3])]])
print(exps1)

exps2 = numpy.array([[5,    10.12, 15/5],
               [-20, round(25.112), 30 * 2],
               [pow(10, 2), len(range(1, 5)), sum((-1, -2, -3))]])
print(exps2)
results = numpy.equal(exps1, exps2)
print(results)

Output:

[[ 5. 10.1 3. ]

[-20. 25. 60. ]

[100. 4. -6. ]]

[[ 5. 10.12 3. ]

[-20. 25. 60. ]

[100. 4. -6. ]]

[[ True False True]

[ True True True]

[ True True True]]

Example 3:

# Example Python program that checks whether each
# element of two NumPy array-likes are equal

import numpy

# Create two ndarray instances and populate them with values
numbers1 = numpy.ndarray(shape = (2, 2), dtype=complex)

numbers1[0][0] = 1.1
numbers1[0][1] = 2
numbers1[1][0] = 3.14
numbers1[1][1] = 4+2j

numbers2 = numpy.ndarray(shape = (2, 2), dtype=complex)
numbers2[0][0] = 1.11
numbers2[0][1] = 2.0
numbers2[1][0] = 3.141
numbers2[1][1] = 4-2j

results = numpy.equal(numbers1, numbers2)
print("Array1:")
print(numbers1)

print("Array2:")
print(numbers2)

print("Array1 == Array2:")
print(results)

Output:

Array1:

[[1.1 +0.j 2. +0.j]

[3.14+0.j 4. +2.j]]

Array2:

[[1.11 +0.j 2. +0.j]

[3.141+0.j 4. -2.j]]

Array1 == Array2:

[[False True]

[False False]]