Python sets are versatile data structures designed to hold unique, unordered elements. Sets offer several built-in methods to manipulate elements efficiently. Among the most commonly used are add(), remove(), and discard().

This guide will walk you through these three important set methods using clear explanations and practical examples.

1. add(): Adding Elements to a Set

The add() method is used to insert a new element into a set. If the element already exists, it will not be added again (as sets do not allow duplicates).

# Example 1: Using add()
fruits = {"apple", "banana"}
fruits.add("cherry")
print(fruits)  # Output: {'apple', 'banana', 'cherry'}

# Example 2: Adding an existing element
fruits.add("banana")
print(fruits)  # Output remains unchanged

2. remove(): Deleting an Element (Raises Error if Not Found)

The remove() method deletes a specific element from the set. If the element is not found, it raises a KeyError.

# Example 3: Using remove()
colors = {"red", "green", "blue"}
colors.remove("green")
print(colors)  # Output: {'red', 'blue'}

# Example 4: Removing an element not in the set
# colors.remove("yellow")  # ❌ Raises KeyError

3. discard(): Deleting an Element (No Error if Not Found)

The discard() method also deletes an element from the set, but does not raise an error if the element is missing. It's safer to use when you're not sure if the element exists.

# Example 5: Using discard()
numbers = {10, 20, 30}
numbers.discard(20)
print(numbers)  # Output: {10, 30}

# Example 6: Discarding an element not in the set
numbers.discard(40)  # No error
print(numbers)       # Output remains unchanged

4. Summary Table: add() vs remove() vs discard()

5. Best Practices

• Use add() to insert values one at a time into your set.
• Use remove() only when you're certain the element exists.
• Use discard() when you're unsure if the element is present to avoid errors.

6. Final Thoughts

Understanding how to use add(), remove(), and discard() is essential for managing sets effectively. These methods give you control over the contents of a set without needing to write complex code. With practice, they’ll become a natural part of your Python toolkit.