numpy.equal(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'not_equal') : This logical function checks for arr1 == arr2 element-wise. Parameters : 

arr1 : [array_like]Input array arr2 : [array_like]Input array out : [ndarray, optional]Output array with same dimensions as Input array, placed with result. **kwargs : allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function. where : [array_like, optional]True value means to calculate the universal functions(ufunc) at that position, False value means to leave the value in the output alone.

Return : 

Returns arr1 == arr2 element-wise

  Code 1 : 

# Python Program illustrating
# numpy.equal() method
import numpy as geek 
 
a  = geek.equal([1., 2.], [1., 3.])
print("Check to be Equal : \n", a, "\n")
 
b = geek.equal([1, 2], [[1, 3],[1, 4]])
print("Check to be Equal : \n", b, "\n")

Output : 

Check to be Equal : 
 [ True False] 

Check to be Equal : 
 [[ True False]
 [ True False]] 

  Code 2 : Comparing data-type using .equal() function 

# Python Program illustrating
# numpy.equal() method
import numpy as geek 
 
# Here we will compare Complex values with int
a = geek.array([0 + 1j, 2])
b = geek.array([1,2])
 
d  = geek.equal(a, b)
print("Comparing complex with int using .equal() : ", d)

Output : 

Comparing complex with int using .equal() :  [False  True]

  Code 3 : 

# Python Program illustrating
# numpy.not_equal() method
import numpy as geek 
 
# Here we will compare Float with int values
a = geek.array([1.1, 1])
b = geek.array([1, 2])
  
d  = geek.not_equal(a, b)
print("\nComparing float with int using .not_equal() : ", d)

Output :

Comparing float with int using .not_equal() :  [ True  True]

Time complexity : 

equal has time complexity of O(N). Where k is the length of list which need to be added.

References : https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.equal.html .