Name: Modern Python Cookbook
Author: Steven F. Lott
ISBN: 9781800205802

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Modern Python Cookbook

Steven F. Lott

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About the author
About the reviewers
Preface
1 Numbers, Strings, and Tuples
Working with large and small integers
Choosing between float, decimal, and fraction
Choosing between true pision and floor pision
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
2 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
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 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
Other Books You May Enjoy
Index

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Modern Python Cookbook