Python Sets: Unordered Collections of Unique Elements

January 18, 2026

Python Sets: Unordered Collections of Unique Elements

Q: How do I create an empty set?

Use my_set = set(). Note that {} creates an empty dictionary, not an empty set.

Q: How can I add elements to a set?

Use the add() method for a single element or the update() method for multiple elements from an iterable.

Q: How do I remove elements from a set?

Use remove() (raises KeyError if element not found) or discard() (does nothing if element not found).

Beginner

Python sets are mutable, unordered collections that store only unique elements. They are highly efficient for membership testing and removing duplicates.

Core Concept

Sets are defined using curly braces `{}` or the `set()` constructor. They automatically discard duplicate values, making them ideal for unique data management.

Basic Example

Basics.py


my_list = [1, 2, 2, 3, 4, 4, 5]
my_set = set(my_list)
print(my_set)

# Output: {1, 2, 3, 4, 5}

new_set = {10, 20, 30, 30, 40}
print(new_set)

# Output: {40, 10, 20, 30} (order may vary)

How It Works

When you create a set from an iterable (like a list), Python iterates through the elements. Each element is checked for existence. If it's not already in the set, it's added. If it's a duplicate, it's ignored. The `set()` constructor or `{}` syntax handles this uniqueness enforcement.

Advanced Example

advance.py


set_a = {1, 2, 3, 4, 5}
set_b = {4, 5, 6, 7, 8}

# Union: elements in either set_a or set_b
print(set_a.union(set_b))       # Output: {1, 2, 3, 4, 5, 6, 7, 8}
print(set_a | set_b)            # Output: {1, 2, 3, 4, 5, 6, 7, 8}

# Intersection: elements common to both set_a and set_b
print(set_a.intersection(set_b)) # Output: {4, 5}
print(set_a & set_b)            # Output: {4, 5}

# Difference: elements in set_a but not in set_b
print(set_a.difference(set_b))  # Output: {1, 2, 3}
print(set_a - set_b)            # Output: {1, 2, 3}

# Symmetric Difference: elements in either set, but not both
print(set_a.symmetric_difference(set_b)) # Output: {1, 2, 3, 6, 7, 8}
print(set_a ^ set_b)                    # Output: {1, 2, 3, 6, 7, 8}

Common Use Cases

Removing duplicate items from a list.
Efficiently checking if an item exists in a collection (membership testing).
Performing mathematical set operations like union, intersection, and difference.
Storing unique identifiers or tags.

Common Pitfalls

Sets are mutable, meaning you can add or remove elements. However, set elements themselves must be immutable (e.g., numbers, strings, tuples). You cannot have a set containing other sets or lists.
Sets are unordered. You cannot rely on the order of elements when iterating or printing.
Accessing elements by index is not possible; use membership testing (`in`) instead.

FAQs

Q: How do I create an empty set?
A: Use `my_set = set()`. Note that `{}` creates an empty dictionary, not an empty set.

Q: Can sets store different data types?
A: Yes, sets can store elements of different immutable data types.

Q: How can I add elements to a set?
A: Use the `add()` method for a single element or the `update()` method for multiple elements from an iterable.

Q: How do I remove elements from a set?
A: Use `remove()` (raises KeyError if element not found) or `discard()` (does nothing if element not found).

Conclusion

Python sets are a powerful tool for managing unique data and performing efficient set operations. Their speed and simplicity make them indispensable for various programming tasks.

Search This Blog

CODE