Chapter 8: Functional Programming

Functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids changing state and mutable data. Python supports functional programming concepts that make your code more predictable, testable, and maintainable.

Understanding Functional Programming

Functional programming emphasizes:

• Pure functions: Functions that always return the same output for the same input
• Immutability: Data that doesn't change after creation
• No side effects: Functions that don't modify external state

Depending on Global State (Avoid This)

This function depends on external state, making it harder to test and reason about.

Functional Approach (Better)

The function is now pure - it always returns the same output for the same input, with no side effects.

Avoiding Mutable State

Functional Approach with Explicit State

Modifying Mutable Data (Avoid This)

Functional Approach with Immutability

Applying Functions to Data Science Workflows

Functional programming shines in data science when transforming and analyzing data.

Using apply() with DataFrames

Creating Feature Engineering Functions

Map, Reduce, and Filter

Python provides three powerful functional programming tools: map(), reduce(), and filter().

Map: Apply a Function to Every Element

Map with Multiple Inputs

Map with Lambda

Reduce: Combine Elements into a Single Value

Reduce with operator Module

Filter: Select Elements That Match a Condition

Filter with Lambda

Combining Map and Filter

List Comprehensions

List comprehensions provide a concise, readable way to create lists. They're often more Pythonic than map/filter.

From For Loop to List Comprehension

Mapping with List Comprehensions

Filtering with List Comprehensions

Multiple Variables with zip

Conditional Expressions in Comprehensions

Flattening Lists

Nested List Comprehensions

Creating 2D Arrays

Dictionary Comprehensions

Similar to list comprehensions, but create dictionaries.

Basic Dictionary Comprehension

Transforming Dictionaries

Dictionary from Rows

Filtering Dictionary Comprehensions

Set Comprehensions

Python also supports set comprehensions:

Practical Functional Programming Examples

Data Transformation Pipeline

Text Processing

Data Analysis with Functional Approach

Key Takeaways

1. Pure Functions: Functions with no side effects that always return the same output for the same input
2. Immutability: Prefer creating new data over modifying existing data
3. map(): Apply a function to every element in a sequence
4. filter(): Select elements that match a condition
5. reduce(): Combine elements into a single value
6. List Comprehensions: Concise, readable syntax for creating lists
7. Dictionary Comprehensions: Create dictionaries with comprehension syntax
8. Composition: Combine simple functions to build complex transformations

Functional programming makes code more predictable, testable, and often more concise. While Python isn't a purely functional language, it provides excellent tools for functional-style programming when appropriate.

Quiz

Further Reading

• Functional Programming HOWTO
• Functional Programming in Python
• functools module
• itertools module
• List comprehensions
• Wikipedia: Functional Programming