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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What this book covers

Chapter 1, Numbers, Strings, and Tuples, will look at the different kinds of numbers, work with strings, use tuples, and use the essential built-in types in Python. We will also exploit the full power of the unicode character set.

Chapter 2, Statements and Syntax, will cover some basics of creating script files first. Then we'll move on to looking at some of the complex statements, including if, while, for, try, with, and raise.

Chapter 3, Function Definitions, will look at a number of function definition techniques. We'll also look at the Python 3.5 typing module and see how we can create more formal annotations for our functions.

Chapter 4, Built-In Data Structures Part 1 – Lists and Sets, will look at an overview of the various structures that are available and what problems they solve. From there, we can look at lists and sets in detail.

Chapter 5, Built-In Data Structures Part 2 – Dictionaries, will continue examining the built-in data structures, looking at dictionaries in detail. This chapter will also look at some more advanced topics related to how Python handles references to objects.

Chapter 6, User Inputs and Outputs, will explain how to use the different features of the print() function. We'll also look at the different functions used to provide user input.

Chapter 7, Basics of Classes and Objects, will create classes that implement a number of statistical formulae.

Chapter 8, More Advanced Class Design, will dive a little more deeply into Python classes. We will combine some features we have previously learned about to create more sophisticated objects.

Chapter 9, Functional Programming Features, will examine ways Python can be used for functional programming. This will emphasize function definitions and stateless, immutable objects.

Chapter 10, Input/Output, Physical Format, and Logical Layout, will work with different file formats such as JSON, XML, and HTML.

Chapter 11, Testing, will give us a detailed description of the different testing frameworks used in Python.

Chapter 12, Web Services, will look at a number of recipes for creating RESTful web services and also serving static or dynamic content.

Chapter 13, Application Integration: Configuration, will start looking at ways that we can design applications that can be composed to create larger, more sophisticated composite applications.

Chapter 14, Application Integration: Combination, will look at ways that complications that can arise from composite applications and the need to centralize some features, such as command-line parsing.

Chapter 15, Statistical Programming and Linear Regression, will look at some basic statistical calculations that we can do with Python's built-in libraries and data structures. We'll look at the questions of correlation, randomness, and the null hypothesis.