Book Image

Modern Python Cookbook - Second Edition

By : Steven F. Lott
Book Image

Modern Python Cookbook - Second Edition

By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
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Combining the map and reduce transformations

In the other recipes in this chapter, we've been looking at map, filter, and reduce operations. We've looked at each of these in isolation:

  • The Applying transformations to a collection recipe shows the map() function.
  • The Picking a subset – three ways to filter recipe shows the filter() function.
  • The Summarizing a collection – how to reduce recipe shows the reduce() function.

Many algorithms will involve combinations of functions. We'll often use mapping, filtering, and reduction to produce a summary of available data. Additionally, we'll need to look at a profound limitation of working with iterators and generator functions; namely, this limitation:

An iterator can only produce values once.

If we create an iterator from a generator function and a collection of data, the iterator will only produce the data one time. After that, it will appear to be...