Guido van Rossum created Python, an effective, high-level, object-oriented software platform. It’s simple to learn and is quickly becoming one of the best beginning programming languages for beginners.

We would then learn how to compare two arrays in Python in this guide. So, let us learn more about this without wasting any further time.

To compare two arrays in python

In this guide, we would then look at various methodologies for comparing these two arrays in Python and determining whether or not those are equal. 

If the dimensions and values of the two configurations are all the same, they would be equal. If two arrays use the same principles and not the same sequence, the arrays are not considered to be equal.

You need to follow the two common methods outlined below to resolve this issue.

Solution 1: Use “numpy.allclose()”

The first method is very simple. Arrays can be compared with “numpy.allclose()”. Thus, without further hesitation, let us learn more about this by using the example below:

import numpy as np 
ary1 = np.array([11,12,14,16,17]) 
ary2 = np.array([11,13,14,15,17]) 
ary3 = np.array([11,13,14.00001,15,17]) 
print(np.allclose(ary1,ary2)) 
print(np.allclose(ary3,ary2))

Output

False 
True

Solution 2:  Use “numpy.all()” and “==”

We also could use “numpy.all()” and “==”  to compare two arrays in Python. Hence, without further hemming and hawing, let’s consider the following example to understand more about it:

import numpy as np 
ary1 = np.array([11,12,14,16,17]) 
ary2 = np.array([11,13,14,15,17]) 
ary3 = np.array([11,13,14.00001,15,17]) 
print((ary1==ary2).all()) 
print((ary3==ary2).all())

Output

False 
False

Solution 3: Use numpy.array_equal() 

Excepting the above methods, there is another solution that is as effective as the 2 mentioned solutions. It is using numpy.array_equal() method to compare two arrays in Python.

The numpy.array_equal(a1, a2, equal_nan=False) considers a1 and a2 arrays as the input. If both of them have the identical shape and the same number of elements, the method will give back True, if not, it returns False. If we wish the method to regard two NaN values equally, we must set the equal_ nan= keyword argument’s default value, which is False, to True. Look at this example:

import numpy as np

a1 = np.array([1,2,4,6,7])
a2 = np.array([1,3,4,5,7])
print(np.array_equal(a1,a1))
print(np.array_equal(a1,a2))

Output

True 
False

Conclusion

Python is completely dynamically typed and uses automatic memory allocation. Python has powerful high-level data structures and a simple yet effective approach to object-oriented programming. Finally, the two techniques presented are simple methods to the aforementioned major issue: “How to compare Two Arrays in Python“. You can leave your suggestions and ideas in the comments section if you have any further concerns. I wish users all a productive day with the Python tool.

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