Modern Python Cookbook

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Analyzing many variables in one pass

Locating outliers

Confirming that the data is random – the null hypothesis

Computing an autocorrelation

Computing regression parameters

Computing the coefficient of a correlation

Average of values in a counter

Using the built-in statistics library

15 Statistical Programming and Linear Regression

Controlling complex sequences of steps

Wrapping a program and checking the output

Wrapping and combining CLI applications

Managing arguments and configuration in composite applications

Combining many applications using the Command Design Pattern

Combining two applications into one

14 Application Integration: Combination

Using logging for control and audit output

Designing scripts for composition

Using class-as-namespace for configuration

Using Python for configuration files

Using YAML for configuration files

Finding configuration files

13 Application Integration: Configuration

Implementing authentication for web services

Parsing a JSON request

Parsing the URL path

Making REST requests with urllib

Parsing the query string in a request

Using the Flask framework for RESTful APIs

Defining the card model

12 Web Services

Mocking external resources

Testing things that involve randomness

Testing things that involve dates or times

Combining pytest and doctest tests

Unit testing with the pytest module

Combining unittest and doctest tests

Unit testing with the unittest module

Handling common doctest issues

Testing functions that raise exceptions

Using docstrings for testing

Test tool setup

11 Testing

Refactoring a .csv DictReader as a dataclass reader

Reading HTML documents

Reading XML documents

Reading JSON and YAML documents

Reading complex formats using regular expressions

Using dataclasses to simplify working with CSV files

Reading delimited files with the CSV module

Replacing a file while preserving the previous version

Using pathlib to work with filenames

10 Input/Output Physical Format and Logical Layout

Writing recursive generator functions with the yield from statement

Simplifying complex algorithms with immutable data structures

Creating a partial function

Implementing "there exists" processing

Combining the map and reduce transformations

Summarizing a collection – how to reduce

Picking a subset – three ways to filter

Using stacked generator expressions

Applying transformations to a collection

Writing generator functions with the yield statement

Introduction

9 Functional Programming Features

Deleting from a list of complicated objects

Improving performance with an ordered collection

Creating a class that has orderable objects

Using more complex structures – maps of lists

Managing global and singleton objects

Leveraging Python's duck typing

Separating concerns via multiple inheritance

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

8 More Advanced Class Design

Managing multiple contexts with multiple resources

Creating contexts and context managers

Using properties for lazy attributes

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

Using more sophisticated collections

Optimizing small objects with __slots__

Using frozen dataclasses for immutable objects

Using dataclasses for mutable objects

Using typing.NamedTuple for immutable objects

Designing classes with lots of processing

Essential type hints for class definitions

Using a class to encapsulate data and processing

7 Basics of Classes and Objects

Using the OS environment settings

Using cmd to create command-line applications

Using argparse to get command-line input

Debugging with f"{value=}" strings

Using input() and getpass() for user input

Using the features of the print() function

6 User Inputs and Outputs

Avoiding mutable default values for function parameters

Making shallow and deep copies of objects

Understanding variables references and assignment

Writing dictionary-related type hints

Controlling the order of dictionary keys

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

Creating dictionaries – inserting and updating

5 Built-In Data Structures Part 2: Dictionaries

Writing set-related type hints

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

Building sets – literals adding comprehensions and operators

Reversing a copy of a list

Writing list-related type hints

Deleting from a list – deleting removing popping and filtering

Slicing and dicing a list

Building lists – literals appending and comprehensions

Choosing a data structure

4 Built-In Data Structures Part 1: Lists and Sets

Writing testable scripts with the script-library switch

Designing recursive functions around Python's stack limits

Writing clear documentation strings with RST markup

Picking an order for parameters based on partial functions

Writing hints for more complex types

Defining position-only parameters with the / separator

Forcing keyword-only arguments with the * separator

Using super flexible keyword parameters

Designing type hints for optional parameters

Designing functions with optional parameters

Function parameters and type hints

3 Function Definitions

Managing a context using the with statement

Concealing an exception root cause

Avoiding a potential problem with an except: clause

Leveraging exception matching rules

Avoiding a potential problem with break statements

Saving intermediate results with the := "walrus"

Designing complex if...elif chains

Writing better RST markup in docstrings

Including descriptions and documentation

Writing long lines of code

Writing Python script and module files – syntax basics

2 Statements and Syntax

Using NamedTuples to simplify item access in tuples

Using tuples of items

Decoding bytes – how to get proper characters from some bytes

Encoding strings – creating ASCII and UTF-8 bytes

Using the Unicode characters that aren't on our keyboards

Building complicated strings from lists of characters

Building complex strings with f-strings

String parsing with regular expressions

Rewriting an immutable string

Choosing between true pision and floor pision

Choosing between float decimal and fraction

Working with large and small integers

1 Numbers Strings and Tuples

Preface

About the reviewers

About the author

Why subscribe?

版权信息

封面

封面

版权信息

Why subscribe?

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