Modern Python Cookbook - Second Edition

By : Steven F. Lott

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.

Preface

What you need for this book

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Numbers, Strings, and Tuples

Working with large and small integers

Choosing between float, decimal, and fraction

Choosing between true division and floor division

Rewriting an immutable string

String parsing with regular expressions

Building complex strings with f-strings

Building complicated strings from lists of characters

Using the Unicode characters that aren't on our keyboards

Encoding strings – creating ASCII and UTF-8 bytes

Decoding bytes – how to get proper characters from some bytes

Using tuples of items

Using NamedTuples to simplify item access in tuples

Free Chapter

Statements and Syntax

Writing Python script and module files – syntax basics

Writing long lines of code

Including descriptions and documentation

Writing better RST markup in docstrings

Designing complex if...elif chains

Saving intermediate results with the := "walrus"

Avoiding a potential problem with break statements

Leveraging exception matching rules

Avoiding a potential problem with an except: clause

Concealing an exception root cause

Managing a context using the with statement

Function Definitions

Function parameters and type hints

Designing functions with optional parameters

Designing type hints for optional parameters

Using super flexible keyword parameters

Forcing keyword-only arguments with the * separator

Defining position-only parameters with the / separator

Writing hints for more complex types

Picking an order for parameters based on partial functions

Writing clear documentation strings with RST markup

Designing recursive functions around Python's stack limits

Writing testable scripts with the script-library switch

Built-In Data Structures Part 1: Lists and Sets

Choosing a data structure

Building lists – literals, appending, and comprehensions

Slicing and dicing a list

Deleting from a list – deleting, removing, popping, and filtering

Writing list-related type hints

Reversing a copy of a list

Building sets – literals, adding, comprehensions, and operators

Removing items from a set – remove(), pop(), and difference

Writing set-related type hints

Built-In Data Structures Part 2: Dictionaries

Creating dictionaries – inserting and updating

Removing from dictionaries – the pop() method and the del statement

Controlling the order of dictionary keys

Writing dictionary-related type hints

Understanding variables, references, and assignment

Making shallow and deep copies of objects

Avoiding mutable default values for function parameters

User Inputs and Outputs

Using the features of the print() function

Using input() and getpass() for user input

Debugging with f"{value=}" strings

Using argparse to get command-line input

Using cmd to create command-line applications

Using the OS environment settings

Basics of Classes and Objects

Using a class to encapsulate data and processing

Essential type hints for class definitions

Designing classes with lots of processing

Using typing.NamedTuple for immutable objects

Using dataclasses for mutable objects

Using frozen dataclasses for immutable objects

Optimizing small objects with __slots__

Using more sophisticated collections

Extending a built-in collection – a list that does statistics

Using properties for lazy attributes

Creating contexts and context managers

Managing multiple contexts with multiple resources

More Advanced Class Design

Choosing between inheritance and composition – the "is-a" question

Separating concerns via multiple inheritance

Leveraging Python's duck typing

Managing global and singleton objects

Using more complex structures – maps of lists

Creating a class that has orderable objects

Improving performance with an ordered collection

Deleting from a list of complicated objects

Functional Programming Features

Introduction

Writing generator functions with the yield statement

Applying transformations to a collection

Using stacked generator expressions

Picking a subset – three ways to filter

Summarizing a collection – how to reduce

Combining the map and reduce transformations

Implementing "there exists" processing

Creating a partial function

Simplifying complex algorithms with immutable data structures

Writing recursive generator functions with the yield from statement

Input/Output, Physical Format, and Logical Layout

Using pathlib to work with filenames

Replacing a file while preserving the previous version

Reading delimited files with the CSV module

Using dataclasses to simplify working with CSV files

Reading complex formats using regular expressions

Reading JSON and YAML documents

Reading XML documents

Reading HTML documents

Refactoring a .csv DictReader as a dataclass reader

Testing

Test tool setup

Using docstrings for testing

Testing functions that raise exceptions

Handling common doctest issues

Unit testing with the unittest module

Combining unittest and doctest tests

Unit testing with the pytest module

Combining pytest and doctest tests

Testing things that involve dates or times

Testing things that involve randomness

Mocking external resources

Web Services

Defining the card model

Using the Flask framework for RESTful APIs

Parsing the query string in a request

Making REST requests with urllib

Parsing the URL path

Parsing a JSON request

Implementing authentication for web services

Application Integration: Configuration

Finding configuration files

Using YAML for configuration files

Using Python for configuration files

Using class-as-namespace for configuration

Designing scripts for composition

Using logging for control and audit output

Application Integration: Combination

Combining two applications into one

Combining many applications using the Command Design Pattern

Managing arguments and configuration in composite applications

Wrapping and combining CLI applications

Wrapping a program and checking the output

Controlling complex sequences of steps

Statistical Programming and Linear Regression

Using the built-in statistics library

Average of values in a counter

Computing the coefficient of a correlation

Computing regression parameters

Computing an autocorrelation

Confirming that the data is random – the null hypothesis

Locating outliers

Analyzing many variables in one pass

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Index

Customer Reviews

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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.

Modern Python Cookbook - Second Edition

By : Steven F. Lott

Modern Python Cookbook - Second Edition

By: Steven F. Lott

Overview of this book

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What this book covers