# 5 Best Ways to Check Whether Two String Arrays Are Equivalent in Python

Rate this post

π‘ Problem Formulation: How can you determine if two string arrays in Python contain the same elements in any order? For example, given two arrays `arr1 = ["apple", "banana"]` and `arr2 = ["banana", "apple"]`, a program should determine that these arrays are indeed equivalent.

## Method 1: Using Sorted Function

Sorting both arrays and comparing them is an intuitive method to check equivalency. The `sorted()` function in Python returns a new sorted list from the items in an iterable. If two sorted arrays are the same, the original arrays are equivalent.

Here’s an example:

```arr1 = ["apple", "banana"]
arr2 = ["banana", "apple"]
print(sorted(arr1) == sorted(arr2))```

Output:

`True`

This snippet sorts both arrays and compares them. If the arrays have the same strings, the sorted versions will match, indicating equivalency.

## Method 2: Using Collections Counter

The `collections.Counter` method in Python is useful for tallying elements and comparing multiset or “bag” equivalence. It constructs a counter object which is a dictionary that maps elements to the number of occurrences.

Here’s an example:

```from collections import Counter

arr1 = ["apple", "banana"]
arr2 = ["banana", "apple"]
print(Counter(arr1) == Counter(arr2))```

Output:

`True`

We construct a `Counter` for each array, which effectively counts the occurrences of each string. We then compare the counters to determine equivalency.

## Method 3: Comparing Sets

If the arrays do not contain duplicates, we can cast them to sets and check for equality. It’s important to note that this method only works correctly if all elements are unique, as converting to sets removes duplicates.

Here’s an example:

```arr1 = ["apple", "banana"]
arr2 = ["banana", "apple"]
print(set(arr1) == set(arr2))```

Output:

`True`

By converting each array to a set, we can perform a direct comparison. This works under the assumption there are no repeated elements within each array, as sets cannot have duplicates.

## Method 4: Manual Tally Check

Iterating over each array to manually count elements and compare tallies is another option. Though less efficient, it’s a method that does not require any additional functions or imports.

Here’s an example:

```arr1 = ["apple", "banana"]
arr2 = ["banana", "apple"]

def are_equivalent(arr1, arr2):
tally1 = {item: arr1.count(item) for item in arr1}
tally2 = {item: arr2.count(item) for item in arr2}
return tally1 == tally2

print(are_equivalent(arr1, arr2))```

Output:

`True`

This custom function creates a tally dictionary for each array and then compares the dictionaries. If both have identical keys and values (element counts), the arrays are equivalent.

## Bonus One-Liner Method 5: Using All and Count

A compact way to express the equivalency check is using `all()` in a list comprehension with `count()` to ensure all elements and their counts match.

Here’s an example:

```arr1 = ["apple", "banana"]
arr2 = ["banana", "apple"]
print(all(arr1.count(elem) == arr2.count(elem) for elem in arr1))```

Output:

`True`

This one-liner checks that for every element in `arr1`, its count matches the count in `arr2`. This confirms equivalence if `True` for all elements.

## Summary/Discussion

• Method 1: Sorted Function. Simple and intuitive. Performance hit with larger lists due to sorting.
• Method 2: Collections Counter. Efficient for comparing multisets. Requires additional import from collections module.
• Method 3: Comparing Sets. Quick and easy for unique elements. Does not work for arrays with duplicates.
• Method 4: Manual Tally Check. No imports required. Can be less efficient with large arrays due to multiple passes for counts.
• Method 5: Using All and Count. One-liner elegance. Can be inefficient as it also performs multiple counts.