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

Cloning iterators with tee()


The tee() function gives us a way to circumvent one of the important Python rules for working with iterables. The rule is so important, we'll repeat it here:

Note

Iterators can be used only once.

The tee() function allows us to clone an iterator. This seems to free us from having to materialize a sequence so that we can make multiple passes over the data. For example, a simple average for an immense dataset could be written in the following way:

def mean(iterator: Iterator[float]) -> float:
    it0, it1 = tee(iterator,2)
    N = sum(1 for x in it0)
    s1 = sum(x for x in it1)
    return s1/N

This would compute an average without appearing to materialize the entire dataset in memory in any form. Note that the type hint of float doesn't preclude integers. The mypy program is aware of the type coercion rules, and this definition provides a flexible way to specify that either int or float will work.

While interesting in principle, the tee() function's implementation...