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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

Reducing a product


In relational database theory, a join between tables can be thought of as a filtered product. A SQL SELECT statement that joins tables without a WHERE clause will produce a Cartesian product of rows in the tables. This can be thought of as the worst-case algorithm—a product without any filtering to pick the proper results. We can implement this using the itertools product() function to enumerate all possible combinations and filter those to keep the few that match properly.

We can define a join() function to join two iterable collections or generators, as shown in the following commands:

JT_ = TypeVar("JT_")
def join(
        t1: Iterable[JT_],
        t2: Iterable[JT_],
        where: Callable[[Tuple[JT_, JT_]], bool]
    ) -> Iterable[Tuple[JT_, JT_]]:
    return filter(where, product(t1, t2))

All combinations of the two iterables, t1 and t2, are computed. The filter() function will apply the given where() function to pass or reject two-tuples, hinted as Tuple[JT_, JT_...

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