Book Image

Modern Python Standard Library Cookbook

By : Alessandro Molina
Book Image

Modern Python Standard Library Cookbook

By: Alessandro Molina

Overview of this book

The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python. The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data. By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Chapter 7. Algorithms

In this chapter, we will cover the following recipes:

  • Searching, sorting, filtering—high-performance searching in sorted containers
  • Getting the nth element of any iterable—grabbing the nth element of any iterable, generators too
  • Grouping similar items—splitting an iterable into groups of similar items
  • Zipping—merging together data from multiple iterables into a single iterable
  • Flattening a list of lists—converting a list of lists into a flat list
  • Producing permutations and—computing all possible permutations of a set of elements
  • Accumulating and reducing—applying binary functions to iterables
  • Memoizing—speeding up computation by caching functions
  • Operators to functions—how to keep references to callables for a Python operator
  • Partials—reducing the number of arguments of a function by preapplying some
  • Generic functions—functions that are able to change behavior according to the provided argument type
  • Proper decoration—properly decorating a function to avoid missing its signature...