Python Sets

✅ What is a Set in Python?

A set is a built-in data type in Python used to store multiple items in a single variable.

A set is:

• Unordered: Items do not have a specific position or index.
• Unchangeable: You cannot change items once they're added, but you can add or remove items.
• Unindexed: You cannot access items using indexes.
• No duplicates allowed: Every element in a set is unique.

Sets are mainly used when you want to:

• Remove duplicate values
• Perform mathematical operations like union, intersection, etc.

🟨 How to Create a Set

You can create a set using curly braces {} or the set() constructor.

# Using curly braces
fruits = {"apple", "banana", "cherry"}

# Using set() function
numbers = set([1, 2, 3])

Note: An empty set must be created using set() and not {} (which creates a dictionary).

empty_set = set()   # Correct
wrong_set = {}      # This is a dictionary

🟩 Properties of Sets

Unordered

colors = {"red", "green", "blue"}
print(colors)  # Output order can be different every time

No Duplicates

items = {"pen", "pencil", "pen", "eraser"}
print(items)  # Output: {'pen', 'pencil', 'eraser'}

🟧 Accessing Set Items

You cannot access items by index. Instead, use a loop.

for item in fruits:
    print(item)

🟦 Adding Items to a Set

1. add() – Adds a single item

fruits.add("orange")

2. update() – Adds multiple items

fruits.update(["grape", "melon"])

🟥 Removing Items from a Set

1. remove() – Removes the item; gives error if not found

fruits.remove("banana")

2. discard() – Removes the item; does not give error if not found

fruits.discard("mango")

3. pop() – Removes a random item

item = fruits.pop()
print("Removed item:", item)

4. clear() – Empties the set

fruits.clear()

5. del – Deletes the entire set

del fruits

🟫 Set Operations (Very Important for Data Science)

Let's take two sets:

A = {1, 2, 3, 4}
B = {3, 4, 5, 6}

1. Union – Combines both sets without duplicates

print(A | B)        # {1, 2, 3, 4, 5, 6}
print(A.union(B))

2. Intersection – Common elements

print(A & B)        # {3, 4}
print(A.intersection(B))

3. Difference – Items in A not in B

print(A - B)        # {1, 2}
print(A.difference(B))

4. Symmetric Difference – Items in A or B but not both

print(A ^ B)        # {1, 2, 5, 6}
print(A.symmetric_difference(B))

🟪 Set Methods (Summary Table)

Method	Description
add(item)	Adds a single item
update(iterable)	Adds multiple items
remove(item)	Removes item (error if not found)
discard(item)	Removes item (no error if not found)
pop()	Removes random item
clear()	Empties the set
union(set)	Returns all unique items from both sets
intersection(set)	Returns common items
difference(set)	Items in current set but not in another
symmetric_difference(set)	Items not common in both sets

🔷 Use Cases of Sets in Data Science

Remove Duplicates in Data

data = [1, 2, 2, 3, 3, 3, 4]
unique_data = set(data)
print(unique_data)  # {1, 2, 3, 4}

Find Common Features/Items

features_A = {"height", "weight", "age"}
features_B = {"age", "blood_pressure"}
common = features_A & features_B  # {'age'}

Fast Lookup

Set operations like membership tests are faster than lists.

print("apple" in fruits)  # Very fast

🧠 Key Points to Remember

• Sets do not allow duplicate values.
• Sets are unordered — you can't rely on the position of elements.
• Use sets for efficient membership testing and mathematical set operations.
• Sets are mutable — you can add or remove items, but the items themselves must be immutable (e.g., no list inside a set).

📝 Practice Questions

1. Create a set of even numbers between 1 and 10.
2. Given two sets A and B, find:
   • Elements common to both
   • Elements only in A
   • Elements in either A or B but not both
3. Write a function that removes duplicate words from a sentence using sets.