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Python Sets and Set Operations

Python Sets and Set Operations
Shahid Ali
Technical writer
Python
27.09.2024
Reading time: 6 min

A set in Python is an unordered collection of unique elements. It is one of the fundamental data types in Python, offering flexibility in how data is stored and accessed. Unlike lists or tuples, sets do not allow duplicate elements, making them an ideal choice for handling unique values. Sets are often used in situations where operations such as membership testing, union, intersection, and difference are frequently performed.

This tutorial will cover the basics of Python sets, how to create them, and how to use Python set operations effectively. By the end, you’ll understand how to leverage sets in your Python projects for optimal performance and readability.

Why Use Sets in Python?

  • Sets ensure that there are no duplicate values.
  • They are useful for membership tests, eliminating duplicates, and performing set operations (union, intersection, etc.).
  • The operations on sets in Python are optimized for performance.

Creating Sets in Python

In Python, sets are created using curly braces {} or the set() constructor. If you use curly braces, you can define a set directly with its elements, while the set() constructor can be used to create an empty set or a set from an iterable.

Example 1: Creating a Set Using Curly Braces

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

Example 2: Creating an Empty Set

empty_set = set()
print(empty_set)

Using {} without any elements creates an empty dictionary, not a set. To create an empty set, always use set().

Basic Set Operations

Python sets support various operations that allow developers to handle collections of data efficiently. Below are some of the most commonly used set operations.

Adding Elements to a Set

To add an element to a set, use the add() method. If the element already exists in the set, the set remains unchanged.

fruits = {'apple', 'banana'}
fruits.add('orange')
print(fruits)  # Output: {'apple', 'banana', 'orange'}

Removing Elements from a Set

You can remove elements using the remove() or discard() methods. The difference between the two is that remove() raises an error if the element is not found, while discard() does not.

fruits.remove('banana')
print(fruits)  # Output: {'apple', 'orange'}

# Using discard() to remove a non-existent element
fruits.discard('grape')  # No error is raised

Set Union

The union operation combines elements from two sets. The result contains all unique elements from both sets.

set_a = {1, 2, 3}
set_b = {3, 4, 5}
union_set = set_a.union(set_b)
print(union_set)  # Output: {1, 2, 3, 4, 5}

Set Intersection

Intersection returns only the elements that are present in both sets.

set_a = {1, 2, 3}
set_b = {2, 3, 4}
intersection_set = set_a.intersection(set_b)
print(intersection_set)  # Output: {2, 3}

Set Difference

The difference operation returns elements that are in one set but not in the other.

set_a = {1, 2, 3}
set_b = {2, 3, 4}
difference_set = set_a.difference(set_b)
print(difference_set)  # Output: {1}

Advanced Set Methods

Python sets provide several advanced methods that make them powerful tools for handling collections of data.

issubset()

The issubset() method checks if all elements of one set are present in another set.

Example:

# Define two sets
set_a = {1, 2, 3}
set_b = {1, 2, 3, 4, 5}

# Check if set_a is a subset of set_b
result = set_a.issubset(set_b)

# Print the result
print(result)  # Output: True

In this example:

  • set_a contains {1, 2, 3}, and all these elements are present in set_b, which contains {1, 2, 3, 4, 5}.
  • Since set_a is fully contained within set_b, issubset() returns True.

issuperset()

The issuperset() method checks if a set contains all elements of another set.

set_a = {1, 2, 3, 4}
set_b = {1, 2}
print(set_a.issuperset(set_b))  # Output: True

Symmetric Difference

The symmetric difference returns all elements that are in either of the sets but not in both.

set_a = {1, 2, 3}
set_b = {3, 4, 5}
symmetric_diff = set_a.symmetric_difference(set_b)
print(symmetric_diff)  # Output: {1, 2, 4, 5}

Use Cases of Sets in Python

Removing Duplicates from a List

One of the simplest use cases for sets is to remove duplicate items from a list.

my_list = [1, 2, 2, 3, 4, 4, 5]
unique_set = set(my_list)
unique_list = list(unique_set)
print(unique_list)  # Output: [1, 2, 3, 4, 5]

Membership Testing

Sets are highly optimized for membership testing, i.e., checking if an element is in the set.

my_set = {'apple', 'banana', 'cherry'}
print('banana' in my_set)  # Output: True

Mathematical Set Operations

Sets can be used to perform complex mathematical operations such as union, intersection, and difference.

Example:

# Define two sets
set_a = {1, 2, 3, 4}
set_b = {3, 4, 5, 6}

# 1. Union: Elements from both sets
union_result = set_a.union(set_b)
print(f"Union: {union_result}")  # Output: {1, 2, 3, 4, 5, 6}

# 2. Intersection: Common elements between both sets
intersection_result = set_a.intersection(set_b)
print(f"Intersection: {intersection_result}")  # Output: {3, 4}

# 3. Difference: Elements in set_a but not in set_b
difference_result = set_a.difference(set_b)
print(f"Difference: {difference_result}")  # Output: {1, 2}

# 4. Symmetric Difference: Elements that are in either set, but not both
sym_diff_result = set_a.symmetric_difference(set_b)
print(f"Symmetric Difference: {sym_diff_result}")  # Output: {1, 2, 5, 6}

In this example:

  • Union gives {1, 2, 3, 4, 5, 6}.

  • Intersection gives {3, 4}.

  • Difference gives {1, 2} (elements only in set_a).

  • Symmetric Difference gives {1, 2, 5, 6} (elements unique to each set).

Best Practices for Working with Sets

  • Use Sets for Unique Elements: Since sets automatically remove duplicates, use them when you need a collection of unique values.

  • Avoid Using Sets for Ordered Data: Sets do not maintain the order of elements. If order is important, consider using a list or tuple.

  • Leverage Set Operations: Use built-in set operations like union, intersection, and difference to simplify code that deals with data comparisons.

Conclusion

Python sets provide a powerful, easy-to-use tool for managing collections of unique elements. Whether you're performing membership tests, eliminating duplicates, or conducting set operations, Python sets are a must-have in any developer’s toolbox. Understanding and utilizing set operations will enhance your ability to write clean, efficient, and maintainable Python code.

By following the steps and best practices outlined in this guide, you can confidently use sets in your Python projects. If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

Python
27.09.2024
Reading time: 6 min

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Training The next step is training. optimizer = optim.Adam(model.parameters(), lr=0.001) EPOCHS = 3 for epoch in range(EPOCHS): for data in trainset: X, y = data model.zero_grad() output = model(X.view(-1, 28 * 28)) loss = F.nll_loss(output, y) loss.backward() optimizer.step() print(loss) The optimizer calculates the difference (loss) between the actual data and the prediction, adjusts the weights, recalculates the loss, and continues the cycle until the loss is minimized. Training Verification Here, we compare the actual values with the predictions made by the model. For this tutorial, the accuracy is high because the neural network effectively recognizes each digit. correct = 0 total = 0 with torch.no_grad(): for data in testset: data_input, target = data output = model(data_input.view(-1, 784)) for idx, i in enumerate(output): if torch.argmax(i) == target[idx]: correct += 1 total += 1 print('Accuracy: %d %%' % (100 * correct / total)) To verify that the neural network works, pass it an image of a digit from the test set: plt.imshow(X[1].view(28,28)) plt.show() print(torch.argmax(model(X[1].view(-1, 784))[0])) The output should display the digit shown in the provided image. Final Script Here’s the full script you can run to see how the neural network works: import torch import torchvision import torch.nn.functional as F import matplotlib.pyplot as plt import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets train = datasets.MNIST("", train=True, download=True, transform = transforms.Compose([transforms.ToTensor()])) test = datasets.MNIST("", train=False, download=True, transform = transforms.Compose([transforms.ToTensor()])) trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True) testset = torch.utils.data.DataLoader(test, batch_size=15, shuffle=True) class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 86) self.fc2 = nn.Linear(86, 86) self.fc3 = nn.Linear(86, 86) self.fc4 = nn.Linear(86, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return F.log_softmax(x, dim=1) model = NeuralNetwork() optimizer = optim.Adam(model.parameters(), lr=0.001) EPOCHS = 3 for epoch in range(EPOCHS): for data in trainset: X, y = data model.zero_grad() output = model(X.view(-1, 28 * 28)) loss = F.nll_loss(output, y) loss.backward() optimizer.step() print(loss) correct = 0 total = 0 with torch.no_grad(): for data in testset: data_input, target = data output = model(data_input.view(-1, 784)) for idx, i in enumerate(output): if torch.argmax(i) == target[idx]: correct += 1 total += 1 print('Accuracy: %d %%' % (100 * correct / total)) plt.imshow(X[1].view(28,28)) plt.show() print(torch.argmax(model(X[1].view(-1, 784))[0])) Each time we run the network, it will take a random image from the test set and analyze the digit depicted on it. After the process is completed, it will display the recognition accuracy in percentage, the image itself, and the digit recognized by the neural network. This is how it looks: Conclusion PyTorch is a powerful open-source machine learning platform that accelerates the transition from research prototypes to production deployments. With it, you can solve various tasks in the fields of artificial intelligence and neural networks. You don’t need deep knowledge of machine learning to begin working with PyTorch. It is enough to know the basic concepts to repeat and even modify popular procedures like image recognition to suit your needs. A big advantage of PyTorch is the large user community that writes tutorials and shares examples of using the library. Object recognition in images is one of the simplest and most popular tasks in PyTorch for beginners. However, the capabilities of the library are not limited to this. To create powerful neural networks, you need a lot of training data. These can be stored, for example, in an object-based S3 storage such as Hostman, with instant data access via API or web interface. This is an excellent solution for storing large volumes of information.
01 April 2025 · 10 min to read

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