Functions, Lambda, and Comprehensions

Functions are the building blocks of clean, reusable code. In data science, you will write functions to transform data, create features, and build pipelines. This lesson covers everything from basic definitions to advanced patterns.

Defining Functions

A function maps inputs to outputs: . In Python, define with def:

Parameters and Arguments

Positional and Keyword Arguments

*args and **kwargs

*args collects extra positional arguments as a tuple. **kwargs collects extra keyword arguments as a dictionary.

Unpacking Arguments

Return Values

Scope and Closures

Lambda Functions

Lambdas are anonymous functions: . Equivalent to .

When to Use Lambdas

map, filter, and reduce

map

map() applies a function to every element of an iterable.

filter

filter() keeps elements where the function returns True.

reduce

reduce() accumulates elements into a single value. It is in the functools module.

Comprehension Transformation Flow

List Comprehensions

List comprehensions create new lists: . Faster and more readable than loops.

Basic Syntax

Filtering

Nested Comprehensions

Practical Data Science Examples

Dictionary Comprehensions

Set Comprehensions

Generator Expressions

Generator expressions look like list comprehensions but use parentheses. They produce values lazily, which saves memory.

Function Best Practices

Key Takeaways

• Functions encapsulate logic and make code reusable.
• Use *args and **kwargs for flexible function signatures.
• Lambdas are best for short, one-off operations (sorting, mapping).
• List comprehensions are preferred over map/filter for readability.
• Generator expressions save memory for large datasets.
• Always write descriptive function names and document with docstrings.