In Python, sets are a fundamental data structure that allow you to store a collection of unique elements. Unlike lists or tuples, sets do not maintain any specific order, making them ideal for scenarios where you need to focus on uniqueness rather than sequence. In this article, we will explore the concept of sets in Python, understand their properties, and demonstrate practical examples to harness their power.
Creating Sets:
Example 1: Creating a set with individual elements
fruits = {"apple", "banana", "orange"}
# Output: {'banana', 'orange', 'apple'} |
Example 2: Creating a set from a list
colors = set(["red", "green", "blue"])
# Output: {'blue', 'red', 'green'} |
Unique Elements:
| numbers = {1, 2, 3, 2, 4, 3}
print(numbers) # Output: {1, 2, 3, 4} |
Set Operations:
Example 1: Union of two sets
set1 = {1, 2, 3}
union = set1.union(set2) print(union) # Output: {1, 2, 3, 4, 5} |
Example 2: Intersection of two sets
| set1 = {1, 2, 3}
set2 = {3, 4, 5} intersection = set1.intersection(set2) print(intersection)# Output: {3} |
Example 3: Difference between two sets
set1 = {1, 2, 3, 4, 5}
difference = set1.difference(set2)
# Output: {1, 2, 3} |
Set Membership:
| cities = {"London", "Paris", "Tokyo"}
if "Paris" in cities: print("Paris is in the set")# Output: Paris is in the set |
Modifying Sets:
numbers = {1, 2, 3}
numbers.remove(2)
# Output: {1, 3, 4} |
Iterating Over Sets:
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Conclusion: Sets provide a powerful tool in Python for managing unique and unordered collections of elements. By leveraging sets, you can easily enforce uniqueness, perform set operations, and efficiently check for membership. Whether you need to handle a distinct list of items, find common elements between multiple sets, or ensure uniqueness in your data, sets offer a versatile solution. Incorporate sets into your Python code to simplify complex tasks and take advantage of their unique properties