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:" , a, "" )   b = geek.equal ([ 1 , 2 ], [[ 1 , 3 ], [ 1 , 4 ]]) print ( "Check to be Equal:" , b, " " )

Exit :

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

Code 2: Comparison data type using the .equal () function

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

 

Output:

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

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