Gain insights into functional programming in Python, learn about functions as objects, recursion, closures, and generators, and discover how to confidently apply these concepts to your projects.

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The functional programming paradigm can be a powerful tool, especially as it can be integrated seamlessly with procedural and object-oriented code in Python. 

In this course, you’ll learn what functional programming is, how it’s used, and the features of Python that support it. To start, you’ll learn how functions act as objects, the role of mutability, and how to perform recursion. 

In the latter half of the course, you’ll focus on closures, iterables & iterators, generators, and more. Throughout the course will be three exams which will test your understanding and really drive home what you’ve learned.  

By the end, you’ll have a new programming paradigm to add under your belt and have the confidence to start using functional programming in your own projects.

Learn Functional Programming in Python

# Lazy evaluation

The only way to get values from an iterator is to request the next item. This means that if you want to get the $100^{th}$ element, you will have to ask for the next item 100 times! There is no other option.
 
This means that an iterator doesn’t have to create all its values in one go. In fact, many iterators calculate their values one at a time, as they are needed. Each time you request the next item, the iterator will calculate it there and then. This is called **lazy evaluation** – the iterator doesn’t do any work until it absolutely has to. There are several advantages to this: 

- When you request the first value, the iterator can return it straight away. Without lazy evaluation, the iterator would need to calculate all the values before it could even return the first value. This can make your program more responsive if the series is very long. 
- You do not need to store the calculated values. A long series might use a lot of memory if you need to store it. 
- You don’t waste time calculating values that you might not use. 

To give an example, suppose you had an iterator, `myiter`, that created 1,000 values, and you wanted to find the first zero value. You could do it like this: 

Learn how and when the use of lazy evaluation by iterators is beneficial.

Introduction

Functions as Objects

Mutability

Recursion

Functional Programming in Python - Exam 1

Closures

Iterators

Transforming Iterables

Reducing Iterables

Functional Programming in Python - Exam 2

Comprehensions

Generators

Partial Application and Currying

Functors and Monads

Functional Programming in Python - Exam 3

Useful Libraries

Conclusion

Iterators Use Lazy Evaluation

Lazy evaluation