Python Set with Examples: A Step-by-Step Guide


Python Set with Examples

When working with data in Python, you often need to manage collections of items. Python offers various data structures for this purpose, and one of the most versatile is the set in python. In this blog post, we will explore Python sets step by step, discussing what sets are, how to create and manipulate them, and providing examples along the way.

What is a Python Set?

A Python set is an unordered collection of unique elements. Unlike lists or tuples, where elements can be repeated, a set ensures that each element is distinct. Sets are defined by enclosing a comma-separated sequence of elements within curly 'braces {}' or by using the built-in 'set()' function. Let's explore the basic syntax of creating a set:

Creating a Set : Example

1. Creating an Empty Set

To create an empty set, you can use the 'set()' constructor:

empty_set = set()

Creating a Set with Initial Elements

To create a set with initial elements, you can enclose the elements in curly braces {}:

fruits = {"apple", "banana", "cherry"}

Key Characteristics of Python Sets

1. Uniqueness

The most distinctive feature of a set is that it contains only unique elements. If you attempt to add a duplicate element, it will be automatically ignored. This uniqueness property makes sets particularly useful in scenarios where you need to eliminate duplicates from a collection.

2. Unordered

Sets are unordered, which means they do not have a specific order of elements like lists or tuples. You cannot access elements by indexing, but this is compensated by the rapid lookup and membership testing provided by sets.

3. Mutable

Unlike strings or tuples, sets are mutable, which means you can add or remove elements after creating the set. This makes them adaptable for various dynamic use cases.

Creating and Manipulating Sets

Creating Set

We have already seen how to create sets, but let's dive deeper into some common methods:

1. Using Curly Braces

my_set = {'apple', 'banana', 'cherry'}

2. Using the set() Constructor

fruits = set(['apple', 'banana', 'cherry'])

Basic Set Operations

Adding Elements

To add an element to a set, you can use the add() method:

fruits = {"apple", "banana", "cherry"}
print(fruits)  # {'cherry', 'apple', 'orange', 'banana'}

Removing Elements

To remove an element from a set, you can use the remove() method. If the element doesn't exist, it raises a KeyError. To avoid this, you can use the discard() method, which doesn't raise an error if the element is not found:

fruits = {"apple", "banana", "cherry"}

Traceback (most recent call last):
  File "<string>", line 4, in <module>
KeyError: 'mango'

Note: when we use the remove() method. the element doesn't exist in the list and then it should raise a KeyError. To avoid the KeyError, you can use the discard() method. In case the element is not found, the KeyError doesn't raise an error.

fruits = {"apple", "banana", "cherry"}
print(fruits) #{'banana', 'cherry', 'apple'}

Set Operations : Union, Intersection, and Difference

Python sets support a variety of operations that can be performed on sets, including union, intersection, and difference operations. These operations are useful for comparing and combining sets.

Union of Sets

The union of two sets returns a new set containing all unique elements from both sets:

set1 = {1, 2, 3}
set2 = {3, 4, 5}
union_set = set1.union(set2)
print(union_set) #output {1, 2, 3, 4, 5}

Intersection of Sets ('&')

The intersection of two sets returns a new set containing elements that are common to both sets:

set1 = {1, 2, 3}
set2 = {3, 4, 5}
intersection_set = set1.intersection(set2)
print(union_set)  #output {3}

set1 = {"apple", "banana", "cherry","orange","grape"}
set2 = {"orange", "grape", "guava"}
intersection_set = set1.intersection(set2)

Difference of Sets

The difference between two sets returns a new set containing elements that are unique to the first set and not present in the second set:

set1 = {1, 2, 3}
set2 = {3, 4, 5}
difference_set = set1.difference(set2)
print(difference_set)  # output {1, 2}

set1 = {"apple", "banana", "cherry","orange","grape"}
set2 = {"orange", "grape", "guava"}
difference_set = set1.difference(set2)
print(difference_set)  #output {'cherry', 'banana', 'apple'}

Use Cases of Python Sets

1. Removing Duplicates

Sets are excellent for removing duplicate elements from a list. By simply converting the list to a set and back to a list, duplicates are automatically eliminated.

my_list = [11, 2, 2, 33, 44, 4, 5]
unique_list = list(set(my_list))
print(unique_list)  #output [33, 2, 4, 5, 11, 44]

2. Membership Testing

Checking whether an element exists in a collection can be significantly faster with sets than with lists. Sets use a hash-based mechanism for quick lookups.

my_set = {'apple', 'banana', 'cherry'}
if 'banana' in my_set:

my_set = {'apple', 'banana', 'cherry'}
if "apple" in my_set:
    print("Apple is in the set")


To get the number of elements in a set, you can use the len() function:

fruits = {'apple', 'banana', 'cherry'}
num_fruits = len(fruits)

Iterating Over a Set

You can loop through the elements of a set using a for loop:

fruits = {'apple', 'banana', 'cherry'}
for fruit in fruits:

Set Comprehensions

Just like list comprehensions, you can create sets using set comprehensions:

even_numbers = {x for x in range(10) if x % 2 == 0}
print(even_numbers) #{0, 2, 4, 6, 8}


In this blog post, we've covered the basics of Python sets, including how to create and manipulate them. Sets are a powerful tool for working with unique collections of data, and understanding their operations can help you write more efficient and expressive Python code. So, the next time you need to work with unique elements, consider using sets to simplify your task!.

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