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Functional Python Programming

You're reading from   Functional Python Programming Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788627061
Length 408 pages
Edition 2nd Edition
Languages
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Toc

Table of Contents (18) Chapters Close

Preface 1. Understanding Functional Programming FREE CHAPTER 2. Introducing Essential Functional Concepts 3. Functions, Iterators, and Generators 4. Working with Collections 5. Higher-Order Functions 6. Recursions and Reductions 7. Additional Tuple Techniques 8. The Itertools Module 9. More Itertools Techniques 10. The Functools Module 11. Decorator Design Techniques 12. The Multiprocessing and Threading Modules 13. Conditional Expressions and the Operator Module 14. The PyMonad Library 15. A Functional Approach to Web Services 16. Optimizations and Improvements 17. Other Books You May Enjoy

Using named tuples to collect data

The third technique for collecting data into a complex structure is the named tuple. The idea is to create an object that is a tuple as well as a structure with named attributes. There are two variations available:

  • The namedtuple function in the collections module.
  • The NamedTuple base class in the typing module. We'll use this almost exclusively because it allows explicit type hinting.

In the example from the previous section, we have nested namedtuple classes such as the following:

from typing import NamedTuple

class Point(NamedTuple):
latitude: float
longitude: float

class Leg(NamedTuple):
start: Point
end: Point
distance: float

This changes the data structure from simple anonymous tuples to named tuples with type hints provided for each attribute. Here's an example:

>>> first_leg = Leg(
... Point(29.050501...
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