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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Creating a partial function

When we look at functions such as reduce(), sorted(), min(), and max(), we see that we'll often have some argument values that change very rarely, if at all. In a particular context, they're essentially fixed. For example, we might find a need to write something like this in several places:

    reduce(operator.mul, ..., 1) 

Of the three parameters for reduce(), only one – the iterable to process – actually changes. The operator and the base value arguments are essentially fixed at operator.mul and 1.

Clearly, we can define a whole new function for this:

    def prod(iterable): 
        return reduce(operator.mul, iterable, 1) 

However, Python has a few ways to simplify this pattern so that we don't have to repeat the boilerplate def and return statements.

The goal of this recipe is different from providing general default values. A partial function doesn't provide a way for us to override the defaults...