Skip to main content

Set Methods in Python

 

Learning Sections          show

Set Methods in Python

A set is an unordered collection of unique elements. Python provides several built-in methods for manipulating sets:

1. add()

Adds an element to the set if it is not already present.


# Define a set
fruits = {'apple', 'banana', 'cherry'}

# Add an element to the set
fruits.add('orange')
print(fruits)  # Output: {'banana', 'cherry', 'orange', 'apple'}

2. remove()

Removes the specified element from the set. Raises a KeyError if the element is not present.


# Remove an element from the set
fruits.remove('banana')
print(fruits)  # Output: {'apple', 'cherry', 'orange'}

3. discard()

Removes the specified element from the set if it is present. Does not raise an error if the element is not present.


# Discard an element from the set
fruits.discard('grape')
print(fruits)  # Output: {'apple', 'cherry', 'orange'}

4. pop()

Removes and returns an arbitrary element from the set. Raises a KeyError if the set is empty.


# Remove an arbitrary element from the set
removed_item = fruits.pop()
print(removed_item)  # Output: apple
print(fruits)  # Output: {'cherry', 'orange'}

5. clear()

Removes all elements from the set.


# Clear the set
fruits.clear()
print(fruits)  # Output: set()

6. union()

Returns a new set containing all elements from the original set and the specified set(s).


# Define another set
more_fruits = {'banana', 'grape'}

# Perform union operation
combined_fruits = fruits.union(more_fruits)
print(combined_fruits)  # Output: {'banana', 'grape', 'cherry', 'orange'}

7. intersection()

Returns a new set containing only the elements that are present in both sets.


# Perform intersection operation
common_fruits = fruits.intersection(more_fruits)
print(common_fruits)  # Output: {'banana'}

8. difference()

Returns a new set containing the elements that are present in the original set but not in the specified set(s).


# Perform difference operation
unique_fruits = fruits.difference(more_fruits)
print(unique_fruits)  # Output: {'cherry', 'orange'}

9. symmetric_difference()

Returns a new set containing the elements that are present in either set, but not both.


# Perform symmetric difference operation
symmetric_diff = fruits.symmetric_difference(more_fruits)
print(symmetric_diff)  # Output: {'cherry', 'orange', 'grape'}

10. isdisjoint()

Returns True if two sets have no elements in common.


# Check if sets are disjoint
disjoint = fruits.isdisjoint(more_fruits)
print(disjoint)  # Output: False

11. issubset()

Returns True if all elements of one set are present in another set.


# Check if a set is a subset
subset = fruits.issubset(combined_fruits)
print(subset)  # Output: True

12. issuperset()

Returns True if all elements of one set are present in another set.


# Check if a set is a superset
superset = combined_fruits.issuperset(fruits)
print(superset)  # Output: True

Labels

Labels:

Popular posts from this blog

Learn Python

  Learning Sections Introduction to Python Comment, escape sequence and print statement in Python Variables and Data Types in Python Typecasting in Python User input in Python String slicing and operations on string in Python String methods in Python If else conditional statements in Python Match case statement in Python For loops in Python While loops in Python Break and continue statement in Python Functions in Python Function Arguments in Python introduction to lists in Python List methods in Python Tuples in Python Operations on tuple in Python f strings in Python Docstrings in Python Recursion in Python Sets in Python Set methods in Python Dictionaries in Python for Loop with else in Python Exception Handling in Python Finally keyword in Python Raising custom errors in Python Short hand if else statements Enumerate Function in Python Virtual Environment in Python How import works in Python if __nam...
Read more

MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...
Read more

Conclusion and where to go after this

  Conclusion and Where to Go After This Congratulations on completing your Python learning journey! You've covered a wide array of topics, from the basics of syntax and data types to advanced concepts like multithreading, multiprocessing, and decorators. But learning doesn't stop here. Python is a versatile language with many specialized fields where you can apply your skills. Here are some potential paths you can explore next: Machine Learning Machine Learning (ML) is one of the most exciting fields you can dive into. Python's libraries like TensorFlow, Keras, scikit-learn, and PyTorch make it an ideal language for building ML models. You'll learn about supervised and unsupervised learning, deep learning, neural networks, and more. Start with the basics of linear regression and classification, then move on to more complex models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Data Structures and Algorithms (DSA)...
Read more