To check if two unordered lists `x`

and `y`

are identical, compare the converted sets with `set(x) == set(y)`

. However, this loses all information about duplicated elements. To consider duplicates, compare the sorted lists with `sorted(x) == sorted(y)`

. Due to the efficient merge-sort-like implementation of the `sorted()`

function, this is quite fast for almost-sorted lists.

**Problem**: Given are two lists `x`

and `y`

. You want to return `True`

if both lists contain the same elements, and otherwise `False`

. A variant of this problem is to ignore duplicates (which makes this problem far simpler).

**Examples**:

x = [1, 2, 3, 4, 5] y = [1, 2, 3] # compare(x, y) --> False x = [1, 2, 3, 4, 5] y = [1, 2, 3, 5, 4] # compare(x, y) --> True x = [1, 2, 3, 4, 5] y = [1, 2, 3, 4, 5] # compare(x, y) --> True

Let’s discuss the most Pythonic ways of solving this problem. Here’s a quick interactive code overview:

**Exercise**: Glance over all methods and run the code. What questions come to mind? Do you understand each method?

Read on to learn about each method in detail!

## Method 1: Set Conversion

This method assumes that you ignore duplicates. So, the lists `[1, 1, 1]`

and `[1]`

are considered to be identical:

################### # 1. Set Conversion ################### def method_1(x, y): return set(x) == set(y) print(method_1([1, 2, 3], [1, 2])) # False print(method_1([1, 2], [2, 1])) # True

Converting the list to a set has linear runtime complexity. Comparing two sets for equality also has linear runtime complexity (due to the constant runtime complexity of set membership). So, overall, the runtime complexity of this method is linear in the number of elements in the larger list.

However, a set doesn’t contain any information about the number of times each element is represented. To consider this information, you’ll need a multiset data structure.

## Method 2: Multiset with Collections Counter

In Python, there are some multiset packages that are capable of considering the number of times each element is represented in the original list. One of them is the `collections.Counter`

class.

################### # 2. Collections Counter ################### import collections def method_2(x, y): return collections.Counter(x) == collections.Counter(y) print(method_2([1, 1, 1], [1, 1])) # False print(method_2([1, 2, 3], [2, 1, 3])) # True

This method is also efficient and it hides implementation details which leads to a higher degree of decoupling in your Python application. However, you may not like that it requires to import another dependency.

## Method 3: Sorting

Sorting a list in Python uses a highly efficient algorithm based on mergesort. This means that if the list is “almost” sorted, the sorting routine is very fast. Only in the absolute worst case, the computational complexity is *O(n log n)* to sort a list.

As soon as both lists are sorted, you can go on and use the element-wise comparison operator `x==y`

to check identity of two ordered lists `x`

and `y`

.

################### # 3. Sorting ################### def method_3(x, y): return sorted(x) == sorted(y) print(method_2([1, 1, 1], [1, 1])) # False print(method_2([1, 2, 3], [2, 1, 3])) # True

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## Related Video

This video is related to the problem: checking if two ** ordered **lists are identical.

## Where to Go From Here?

Enough theory. Let’s get some practice!

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While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

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