Stop Using Python Sets Like Lists — Here’s What You’re Missing

Most Developers Use This Data Structure Wrong — And It’s Costing Them Performance

Stop Using Python Sets Like Lists — Here’s What You’re Missing

You’re using sets because someone said they’re faster. But if you’re treating them like lists, you’re leaving serious performance and clarity on the table.

Introduction: You’re Doing It Wrong (But You’re Not Alone)

Python sets are awesome. They’re fast, simple, and perfect for when you need uniqueness. But here’s the catch:

Most developers use sets like lists — and that’s a huge missed opportunity.

If you’re looping over sets like they’re just unordered lists, or storing them where a list makes more sense, you’re not just writing confusing code — you’re potentially making your app slower and harder to maintain.

In this article, we’ll break down:

• The real strengths of Python sets
• What not to use sets for
• Where sets shine over lists — and where they don’t
• Code examples that will make you rethink your defaults

1. Sets Aren’t Just Unordered Lists

Let’s start here:

my_set = {1, 2, 3, 4} 
my_list = [1, 2, 3, 4]

These may look similar, but under the hood, they’re entirely different beasts.

Sets:

• Are unordered
• Cannot contain duplicates
• Have O(1) average lookup time
• Are built for membership testing and set operations (union, intersection, difference)

Lists:

• Are ordered
• Can contain duplicates
• Lookup is O(n)
• Great for preserving order, indexing, and slicing

If you’re using a set but relying on order or index access, you’re misusing the structure.

2. Sets = Speed for Lookups

The #1 reason to use sets over lists: membership checks.

# Slow 
5 in [1, 2, 3, 4, 5, 6, 7, 8]  # O(n) 
 
# Fast 
5 in {1, 2, 3, 4, 5, 6, 7, 8}  # O(1)

If you’re checking for presence repeatedly (think deduplication, filtering, validation), a set will blow a list out of the water in terms of speed.

3. Don’t Loop Over Sets Just to “Get the Items”

Many devs do this:

my_set = {1, 2, 3} 
for item in my_set: 
    print(item)

Technically fine — but ask yourself: Do you care about order?
If you do, you should probably be using a list.

Sets are unordered by design. Relying on iteration order in a set is a dangerous trap.

Python doesn’t guarantee set order in the same way it does for lists (even though CPython 3.7+ appears to preserve insertion order — this is an implementation detail, not a spec).

4. Set Operations > List Comprehensions (Sometimes)

Why filter and loop when you can just use math-like set operations?

Say you have two collections:

a = {1, 2, 3, 4} 
b = {3, 4, 5, 6}

Here’s what you can do with sets:

• a & b → intersection: {3, 4}
• a | b → union: {1, 2, 3, 4, 5, 6}
• a - b → difference: {1, 2}
• a ^ b → symmetric difference: {1, 2, 5, 6}

Compare that to writing nested for loops or list comprehensions with if conditions — this is cleaner, faster, and more readable.

5. Don’t Use Sets When You Need Order or Duplicates

Sets are not a drop-in replacement for lists.

If your app logic needs:

• Preserving insertion order
• Duplicates (e.g. tallying frequency)
• Indexing or slicing

…then stick with lists. Or try other structures like collections.Counter, OrderedDict, or even deque.

Remember: sets are specialized tools, not Swiss Army knives.

6. Real-World Example: Email Deduplication

You’re processing user input like this:

emails = ['a@example.com', 'b@example.com', 'a@example.com', 'c@example.com'] 
unique_emails = list(set(emails))

Looks fine, right?

But did you realize this removes duplicates but also randomizes the order?

If the order matters (say, preserving the first occurrence), this is better:

seen = set() 
unique_emails = [] 
for email in emails: 
    if email not in seen: 
        seen.add(email) 
        unique_emails.append(email)

Still uses a set for fast lookup, but maintains the list order.

Conclusion: Sets Deserve More Respect

Python sets aren’t just lists with extra rules — they’re a powerful, underused tool when used correctly. But misusing them as unordered lists leads to bugs, performance hits, and messy code.

So next time you’re:

• Checking for duplicates
• Filtering items
• Comparing collections
• Writing complex loops to “intersect” things…

Ask yourself: Can a set do this better?

You might be surprised how much cleaner and faster your Python code becomes.

If you learned something new, hit the 👏 clap button and follow for more Python tips! Got questions or examples where you used sets smartly? Drop them in the comments.