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 tuples to collect data


In Chapter 3, Functions, Iterators, and Generators, we showed two common techniques to work with tuples. We've also hinted at a third way to handle complex structures. We can do any of the following techniques, depending on the circumstances:

  • Use lambdas (or functions) to select a named item using the index
  • Use lambdas (or functions) with the argument to assign a tuple items to parameter names
  • Use named tuples to select an item by attribute name or index

Our trip data, introduced in Chapter 4, Working with Collections, has a rather complex structure. The data started as an ordinary time series of position reports. To compute the distances covered, we transposed the data into a sequence of legs with a start position, end position, and distance as a nested three-tuple.

Each item in the sequence of legs looks as follows as a three-tuple:

first_leg = (
    (37.549016, -76.330295), 
    (37.840832, -76.273834), 
    17.7246)

The first two items are the starting and ending...