Book Image

Functional Python Programming. - Second Edition

Book Image

Functional Python Programming. - Second Edition

Overview of this book

If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.
Table of Contents (22 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Using lists, dicts, and sets


A Python sequence object, such as a list, is iterable. However, it has some additional features. We'll think of it as a materialized iterable. We've used the tuple() function in several examples to collect the output of a generator expression or generator function into a single tuple object. We can also materialize a sequence to create a list object.

In Python, a list display, or list comprehension, offers simple syntax to materialize a generator: we just add the [] brackets. This is ubiquitous to the point where the distinction between generator expression and list comprehension is lost. We need to disentangle the idea of generator expression from a list display that uses a generator expression.

The following is an example to enumerate the cases:

>>> range(10)
range(0, 10)
>>> [range(10)]
[range(0, 10)]
>>> [x for x in range(10)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 

The first example...