What is a 'set' in Python?

In Python, a set is a collection that is both unordered and unindexed. But its most important characteristic is that a set can only contain unique elements. Any duplicate items will be automatically removed.

Sets are modeled on the mathematical concept of a set, and they provide powerful and efficient methods for performing standard set operations like union, intersection, and difference.

Creating a Set

You can create a set by placing a comma-separated sequence of items inside curly braces {}.

# Create a set from a list of numbers
numbers = {1, 2, 3, 4, 4, 4} # Duplicates are automatically removed

print(numbers) # {1, 2, 3, 4}

To create an empty set, you must use the set() function. Using empty curly braces {} will create an empty dictionary, not an empty set.

empty_set = set()
empty_dict = {}

Key Properties of Sets

• Unordered: The items in a set do not have a defined order. You cannot access items by an index.
• Unique: A set cannot have two items with the same value.
• Mutable: You can add and remove items from a set.

Adding and Removing Items

• add(): Adds a single element to the set. If the element is already in the set, it does nothing.

  my_set = {1, 2, 3}
  my_set.add(4)
  

• update(): Adds all the items from another iterable (like a list or another set) to the set.

  my_set.update([4, 5, 6])
  

• remove(): Removes a specified element. It will raise a KeyError if the item is not found.

  my_set.remove(3)
  

• discard(): Also removes a specified element, but it will not raise an error if the item is not found.

  my_set.discard(99) # Does nothing, no error
  

Common Use Cases

1. Removing Duplicates from a List

This is one of the most common and elegant use cases for a set. You can quickly remove all duplicate items from a list by converting it to a set and then back to a list.

my_list = [1, 2, 2, 3, 4, 4, 5, 5, 5]

# Convert to a set to remove duplicates, then back to a list
unique_list = list(set(my_list))

print(unique_list) # [1, 2, 3, 4, 5]

2. Membership Testing

Checking if an item exists in a set is incredibly fast and efficient (average time complexity of O(1)). This is much faster than checking for an item in a list (O(n)).

my_set = {1, 2, 3, 4, 5}

print(3 in my_set)    # True
print(10 in my_set)   # False

If you need to frequently check for the existence of items in a large collection, a set is a much better choice than a list.

Set Operations

Sets support standard mathematical operations.

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

• Union (|): Returns a new set containing all items from both sets.

  a | b  # {1, 2, 3, 4, 5, 6}
  

• Intersection (&): Returns a new set containing only the items present in both sets.

  a & b  # {3, 4}
  

• Difference (-): Returns a new set containing items in the first set but not in the second set.

  a - b  # {1, 2}
  

• Symmetric Difference (^): Returns a new set with items in either set, but not both.

  a ^ b  # {1, 2, 5, 6}
  

Conclusion

While lists and dictionaries are often the go-to collection types, Python's set provides a powerful and efficient tool for specific tasks. Its ability to enforce uniqueness and perform high-speed membership tests makes it perfect for removing duplicates and checking for existence. Furthermore, its support for mathematical set operations provides a clean and readable syntax for comparing and combining collections.