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)
Other Books You May Enjoy

Picking a subset – three ways to filter

Choosing a subset of relevant rows can be termed filtering a collection of data. We can view a filter as rejecting bad rows or including the desirable rows. There are several ways to apply a filtering function to a collection of data items.

In the Using stacked generator expressions recipe, we wrote the skip_header_date() generator function to exclude some rows from a set of data. The skip_header_date() function combined two elements: a rule to pass or reject items and a source of data. This generator function had a general pattern that looks like this:

def data_filter_iter(source: Iterable[T]) -> Iterator[T]:
    for item in source: 
        if item should be passed: 
            yield item 

The data_filter_iter() function's type hints emphasize that it consumes items from an iterable source collection. Because a generator function is a kind of iterator, it will produce items of the same type for another consumer...