Skip to main content

Break and Continue Statement in Python

 

Learning Sections     show

Break and Continue Statements in Python

In Python, the break and continue statements are used to control the flow of loops. They provide greater control over how and when the loop should terminate or skip an iteration.

The Break Statement

The break statement is used to exit a loop prematurely when a certain condition is met. It stops the execution of the loop and moves to the code that follows the loop.


# Using break in a loop
for i in range(10):
    if i == 5:
        break
    print(i)
    

In this example, the loop will print numbers from 0 to 4 and then terminate when i equals 5.

The Continue Statement

The continue statement is used to skip the current iteration of a loop and continue with the next iteration. It effectively skips the rest of the code inside the loop for the current iteration only.


# Using continue in a loop
for i in range(10):
    if i == 5:
        continue
    print(i)
    

In this example, the loop will print numbers from 0 to 9, except for 5, which is skipped.

Using Break and Continue in While Loops

The break and continue statements can also be used in while loops to provide additional control over loop execution.


# Using break in a while loop
count = 0

while count < 10:
    print(count)
    count += 1
    if count == 5:
        break
    

In this example, the loop will print numbers from 0 to 4 and then terminate when count equals 5.


# Using continue in a while loop
count = 0

while count < 10:
    count += 1
    if count == 5:
        continue
    print(count)
    

In this example, the loop will print numbers from 1 to 10, except for 5, which is skipped.

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...

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 (...

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)...