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

An overview of function varieties


We need to distinguish between two broad species of functions, as follows:

  • Scalar functions: They apply to individual values and compute an individual result. Functions such as abs(), pow(), and the entire math module are examples of scalar functions.
  • Collection functions: They work with iterable collections.

We can further subdivide the collection functions into three subspecies:

  • Reduction: This uses a function to fold values in the collection together, resulting in a single final value. For example, if we fold (+) operations into a sequence of integers, this will compute the sum. This can be also be called an aggregate function, as it produces a single aggregate value for an input collection.
  • Mapping: This applies a scalar function to each individual item of a collection; the result is a collection of the same size.
  • Filter: This applies a scalar function to all items of a collection to reject some items and pass others. The result is a subset of the input. 

We...