Python Sets: What They Are and How to Use Them

Sets are like lists, but with two key differences: they are unordered and they guarantee uniqueness. Only one of each value can exist in a set. If you need to track unique items or remove duplicates, sets are the tool for the job.

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How Do You Create a Set in Python?

Sets are declared using curly braces — the same braces used for dictionaries, but without key: value pairs:

fruits = {"apple", "banana", "grape"}
print(type(fruits))  # <class 'set'>

If you include duplicates, they’re silently dropped:

fruits = {"apple", "banana", "grape", "apple"}
print(fruits)  # {'banana', 'grape', 'apple'}

There’s one gotcha: {} creates an empty dictionary, not an empty set. To create an empty set, use the set() constructor:

fruits = set()

How Do You Add and Remove Items From a Set?

The .add() method adds a single value to a set — think of it like .append() for lists:

fruits = {"apple", "banana", "grape"}
fruits.add("pear")
print(fruits)  # {'pear', 'banana', 'grape', 'apple'}

Adding a value that already exists doesn’t raise an error — the set just stays the same.

To remove a value, use .remove():

fruits = {"apple", "banana", "grape"}
fruits.remove("apple")
print(fruits)  # {'banana', 'grape'}

Be careful — .remove() raises a KeyError if the value doesn’t exist. You can use .discard() instead if you want to silently ignore missing values.

How Do You Loop Over a Set?

You can iterate over a set with a for loop just like a list:

fruits = {"apple", "banana", "grape"}
for fruit in fruits:
    print(fruit)

The order of iteration is not guaranteed. If you need a consistent order, convert the set to a sorted list first with sorted().

How Do You Remove Duplicates With a Set?

One of the most common uses of sets is deduplication. You can convert a list to a set and back to strip out duplicates:

spells = ["fireball", "ice_blast", "fireball", "heal", "ice_blast"]
unique_spells = list(set(spells))
print(unique_spells)  # ['heal', 'fireball', 'ice_blast']

The set() constructor removes duplicates, and list() converts it back. Keep in mind the order may change since sets are unordered.

You can also do this manually with a loop if you need to preserve order:

spells = ["fireball", "ice_blast", "fireball", "heal"]
seen = set()
unique = []
for spell in spells:
    if spell not in seen:
        seen.add(spell)
        unique.append(spell)
print(unique)  # ['fireball', 'ice_blast', 'heal']

The in check on a set is very fast — much faster than checking a list.

How Do You Check if a Value Is in a Set?

Use the in keyword — same syntax as lists and dictionaries:

vowels = {"a", "e", "i", "o", "u"}
print("e" in vowels)  # True
print("x" in vowels)  # False

Membership checks on sets are much faster than on lists. Under the hood, sets use a hash table, so lookups are roughly O(1) regardless of size. If you’re checking membership in a loop against a large collection, converting it to a set first can dramatically speed things up.

What Operations Can You Do With Sets?

Sets support mathematical operations. The most common is subtraction — removing all values in one set from another:

set1 = {"apple", "banana", "grape"}
set2 = {"apple", "banana"}
set3 = set1 - set2
print(set3)  # {'grape'}

This is handy for finding what’s missing. For example, finding which IDs from one list are absent in another:

required = set([1, 2, 3, 4, 5])
present = set([2, 4])
missing = required - present
print(missing)  # {1, 3, 5}

Python sets also support union and intersection:

a = {1, 2, 3}
b = {3, 4, 5}
print(a | b)  # {1, 2, 3, 4, 5}  (union — all items from both)
print(a & b)  # {3}               (intersection — only shared items)

Union combines everything, intersection keeps only what’s in common. These operations are what make sets so powerful for comparing collections of data.

What Should You Learn After Python Sets?

You now know all four of Python’s core data structures: lists, dictionaries, tuples, and sets. From here, most learners dig deeper into functions and start tackling more complex problems that combine these structures.

If you want to keep going through the full Python curriculum with hands-on exercises, check out the Learn to Code in Python course on Boot.dev.