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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Applying transformations to a collection

Once we've defined a generator function, we'll need to apply the function to a collection of data items. There are a number of ways that generators can be used with collections.

In the Writing generator functions with the yield statement recipe earlier in this chapter, we created a generator function to transform data from a string into a more complex object. Generator functions have a common structure, and generally look like this:

def new_item_iter(source: Iterable) -> Iterator:
    for item in source: 
        new_item = some transformation of item 
        yield new_item 

The function's type hints emphasize that it consumes items from the source collection. Because a generator function is a kind of Iterator, it will produce items for another consumer. This template for writing a generator function exposes a common design pattern.

Mathematically, we can summarize this as follows: