Functions in Python

Functions in Python

What Are Functions?

• Functions are reusable blocks of code that perform specific tasks.
• They help organize code into manageable parts.
• Essential for writing modular and maintainable code, especially in NLP.

def function_name():
    # Code to be executed
    pass

Why Use Functions in NLP?

• Simplify complex processes like data cleaning, preprocessing, and feature extraction.
• Promote code reusability and clarity, making it easier to manage large projects.

def preprocess_text(text):
    return text.lower()  # Example function to convert text to lowercase

Defining Functions

Basic Function Definition

• Use the def keyword to define a function.
• You can pass arguments for customization.

def greet():
    print("Hello, welcome to Python!")

Example: Calling a Function

• Call a function by using its name followed by parentheses.

greet()  # Output: Hello, welcome to Python!

Hello, welcome to Python!

Functions with Arguments

Functions with Arguments

• Accept data inputs to customize behavior.

def greet(name):
    print(f"Hello, {name}!")

Example: Custom Greetings

• Call the function with different arguments.

greet("Alice")  # Output: Hello, Alice!
greet("Bob")    # Output: Hello, Bob!

Hello, Alice!
Hello, Bob!

Returning Values from Functions

Returning Values

• Use return to send back a value from the function.

def add_numbers(a, b):
    return a + b

Example: Storing Returned Values

• Store the returned value for further use.

sum_result = add_numbers(10, 20)
print(sum_result)  # Output: 30

30

Default Arguments

Default Arguments

• Assign default values to function arguments.

def greet(name="there"):
    print(f"Hello, {name}!")

Example: Calling with Default Argument

• If no argument is provided, the default value is used.

greet()         # Output: Hello, there!
greet("Alice")  # Output: Hello, Alice!

Hello, there!
Hello, Alice!

Lambda Functions

What Are Lambda Functions?

• Small anonymous functions written in a single line.
• Useful for simple operations.

square_lambda = lambda x: x ** 2

Example: Using Lambda Functions

• Call the lambda function directly.

print(square_lambda(5))  # Output: 25

25

Functions and Lists

Combining Functions with Lists

• Use functions to process items in a list efficiently.

def square(x):
    return x ** 2

numbers = [1, 2, 3, 4]
squares = [square(num) for num in numbers]

Output the Results

• Print the resulting list after processing.

print(squares)  # Output: [1, 4, 9, 16]

[1, 4, 9, 16]

Best Practices with Functions

Best Practices

• Use descriptive names for clarity.
• Keep functions short and focused on a single task.

def calculate_area(radius):
    """Calculate the area of a circle."""
    return 3.14 * radius ** 2

Importance of Documentation

• Write docstrings and comments to explain complex functions.

def process_data(data):
    """Process the input data."""
    # Implementation goes here

Summary

• Functions are essential for modular programming in Python.
• They improve code readability and maintainability, especially in NLP applications.
• Combine functions with lists to process collections of data efficiently.