Python Functions Explained

Python functions are the building blocks of Python Language. Functions provide a way to break down your code into smaller, more manageable pieces, making it easier to understand and maintain. In this article, we will explain working with functions in Python.

Functions allow you to reuse code, saving you time and effort when writing new programs. We will learn the fundamentals of Python functions, including how to create and call functions, how to use default parameter values and much more.

1. Introduction to Functions in Python

Functions in Python are reusable blocks of code that can perform a specific task. A function in Python is defined using the def keyword, followed by the function name and a set of parentheses. Inside the parentheses, you can specify any parameters the function needs to take.

The body of the function is defined using a colon and indentation. This defines the code that will be executed whenever the function is called.

Functions in Python can take zero or more parameters, depending on the specific needs of the function. The parameters are specified inside the parentheses when defining the function, and commas separate them.

Python functions can return a value using the return keyword. This allows the function to pass data back to the calling code. The returned value can be a single value, a tuple of values, or even a more complex data structure.

2. Types of Functions

In Python, there are two main types of functions built-in functions & user-defined functions.

2.1 Built-in Function

Built-in functions are functions that are already built into the Python language. You can use them directly without having to define them.

Some common built-in functions in Python include print(), len(), max(), min(), sum(), and range(). Here are some examples of these functions:


# Example using the print() function
print("Hello, world!")

# Example using the len() function
my_list = [1, 2, 3, 4, 5]
print(len(my_list))

2.2 User-Defined Functions

User-defined functions are functions that you define yourself in your code. These functions can be as simple or as complex as you need them to be.

To define a function in Python, you use the def keyword followed by the function name, a set of parentheses, and a colon.


# Syntax for Defining a Function
def func(a, b):
    pass

3. Creating a Function

To create a function in Python, you use the def keyword followed by the name of the function, a set of parentheses, and a colon. You can put the code underneath.


# A function that print hello world
def my_function():
    print("Hello, world!")

Or more specific functions like finding the factorial of a number. See the following code example, which returns the factorial of a number instead of printing something to the console.


# Function that finds factorial of a Number
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

When a return statement is executed in a function, the function stops executing and returns the value specified in the return statement.

4. Calling a Function

When a function is defined in Python, it does not execute until it is called. In order to call a function, simply write its name followed by parentheses. If the function takes arguments, then you should pass them within the parentheses.

In the above section, we have created the factorial function, to call it we can use the following syntax:


# call function
factorial(4)

# Or we can assign the value to a variable
factorial_value = factorial(4)

print(factorial_value)

# Output:
# 24

5. Default Parameters

In Python, you can specify default parameter values for functions. This means that if the caller of the function does not provide a value for a certain parameter, then it uses the default value instead.

To define a default parameter, simply assign a value to the parameter in the function definition. Here is an example:


# Example of the default Parameter
def greet(name, greeting="Hello"):
    print(greeting + ", " + name + "!")

greet("Ali")  

# Output: Hello, Ali!

greet("Ali", "Hi")  

# Output: Hi, Ali!

Make sure that default parameters must come after non-default parameters in the function definition. For example, this is not valid:


# This code will result an error
def greet(greeting="Hello", name):
    print(greeting + ", " + name + "!")

6. Anonymous Functions

Anonymous functions are functions that are defined without a name. These functions are also known as lambda functions.

Lambda functions are useful when you need to write a small function for a specific task that you don’t want to define as a separate function.

The syntax for creating a lambda function is as follows:


# Syntax of lambda function
lambda arguments: expression

See how we have used the Lambda function to generate a list of the squares of a given list:


# Using a lambda function
numbers = [1, 2, 3, 4, 5]
squares = map(lambda x: x**2, numbers)
print(list(squares))  

# Output: [1, 4, 9, 16, 25]

7. Importance of Function in Python

Below are a few reasons that make functions important in python or in any programming language:

  1. Functions help you break down complex tasks into smaller, more manageable pieces of code.
  2. Functions allow you to reuse code, which reduces redundancy and improves the overall quality of your codebase.
  3. It provides a high level of abstraction, which means you can focus on what a function does rather than how it does it.
  4. You can easily modify or expanded to accommodate changes in requirements or new features.
  5. Functions allow you to organize your code into logical groups, which makes it easier to navigate and understand.

7. Summary and Conclusion

Functions allow you to break down complex tasks into smaller, more manageable pieces of code, which you can then reuse and call whenever necessary. We have learned, how to create and use functions in Python. I hope this article was helpful. Please leave questions in the comment section below.

Happy Coding!

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AlixaProDev

I am an astute software engineer with extensive 3+ years of experience in developing full-stack web applications. My skillets include building backend services for different databases. Mainly I work with Python, Flask, Django, and FastAPIs. Being Python Specialist, I have worked with Numpy, Pandas, and other Python Libraries to build tools that make ETL normalization easier.

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