Skip to main content

Functions in Python

 

Learning Sections     show

Functions in Python

Functions in Python are reusable blocks of code that perform a specific task. They help in organizing code into manageable sections and avoid repetition. Functions are defined using the def keyword followed by the function name and parentheses ().

Defining a Function

A function is defined using the def keyword. Here's a simple example of a function that takes a parameter and prints a greeting message:


# Defining a function
def greet(name):
    # Function body
    print("Hello, " + name + "!")

# Calling the function
greet("Alice")  # Output: Hello, Alice!

Return Statement

The return statement is used to return a value from a function. If no return statement is used, the function returns None by default.


# Function with return statement
def add(a, b):
    return a + b

# Calling the function
result = add(5, 3)
print(result)  # Output: 8

Default Arguments

Functions can have default argument values, which are used if no value is provided when the function is called.


# Function with default arguments
def greet(name, message = "Hello"):
    print(message + ", " + name + "!")

# Calling the function
greet("Alice")  # Output: Hello, Alice!
greet("Bob", "Good morning")  # Output: Good morning, Bob!

Variable-Length Arguments

Functions can accept a variable number of arguments using the *args and **kwargs syntax.


# Function with *args
def sum_all(*args):
    total = 0
    for num in args:
        total += num
    return total

# Calling the function
result = sum_all(1, 2, 3, 4)
print(result)  # Output: 10
    

# Function with **kwargs
def print_info(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

# Calling the function
print_info(name="Alice", age=25, city="Wonderland")

Lambda Functions

Lambda functions are small anonymous functions defined using the lambda keyword. They are useful for short, throwaway functions.


# Lambda function
square = lambda x: x ** 2

# Using the lambda function
print(square(5))  # Output: 25

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