Book Image

Functional Python Programming - Second Edition

By : Steven F. Lott
Book Image

Functional Python Programming - Second Edition

By: Steven F. Lott

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

Applying partial arguments with partial()

The partial() function leads to something called a partial application. A partially applied function is a new function built from an old function and a subset of the required arguments. It is closely related to the concept of currying. Much of the theoretical background is not relevant here, since currying doesn't apply to the way Python functions are implemented. The concept, however, can lead us to some handy simplifications.

We can look at trivial examples as follows:

>>> exp2 = partial(pow, 2)
>>> exp2(12)
>>> exp2(17)-1

We've created the function exp2(y), which is the pow(2, y) function. The partial() function bounds the first positional parameter to the pow() function. When we evaluate the newly created exp2() function, we get values computed from the argument bound by the partial() function, plus the additional argument provided to the exp2() function.

The bindings of positional parameters are handled in...