numpy.equal() in Python
Last Updated :
21 Jun, 2022
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 :
Python3
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")
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Output :
Check to be Equal :
[ True False]
Check to be Equal :
[[ True False]
[ True False]]
Code 2 : Comparing data-type using .equal() function
Python3
import numpy as geek
a = geek.array([ 0 + 1j , 2 ])
b = geek.array([ 1 , 2 ])
d = geek.equal(a, b)
print ("Comparing complex with int using .equal() : ", d)
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Output :
Comparing complex with int using .equal() : [False True]
Code 3 :
Python3
import numpy as geek
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)
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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 .
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