book

Publishing Python Packages

Name: Publishing Python Packages
Author: Dane Hillard
ISBN: 9781617299919

by Dane Hillard

February 2023

Intermediate to advanced

248 pages

6h 42m

English

Manning Publications

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inside front cover
Publishing Python Packages
Copyright
brief contents
contents
front matter
forewordprefaceacknowledgmentsabout this bookWho should read this book?How this book is organized: a roadmapAbout the codeliveBook discussion forumabout the authorabout the cover illustration
Part 1. Foundations
1 The what and why of Python packages
1.1 What is a package, anyway?1.1.1 Standardizing packaging for automation1.1.2 The contents of a distribution package1.1.3 The challenges of sharing software1.2 How packaging helps you1.2.1 Enforcing cohesion and encapsulation through packaging1.2.2 Promoting clear ownership of code1.2.3 Decoupling implementation from usage1.2.4 Filling roles by composing small packagesSummary
2 Preparing for package development
2.1 Managing Python virtual environments2.1.1 Creating virtual environments with venvSummary
3 The anatomy of a minimal Python package
3.1 The Python build workflow3.1.1 Parts of the Python build system3.2 Authoring package metadata3.2.1 Required core metadata3.2.2 Optional core metadata3.2.3 Specifying a license3.3 Controlling source code and file discovery3.4 Including non-Python files in a packageSummary

Part 2. Creating a viable package
4 Handling package dependencies, entry points, and extensions
4.1 A package for calculating vehicle drift4.2 Creating a C extension for Python4.2.1 Creating C extension source code4.2.2 Integrating Cython into a Python package build4.2.3 Installing and profiling your C extension4.2.4 Build targets for binary wheel distributions4.2.5 Specifying required Python versions4.3 Offering command-line tools from a Python package4.3.1 Creating commands with Setuptools entry points4.4 Specifying dependencies for Python packagesAnswer to exercisesSummary
5 Building and maintaining a test suite
5.1 Integrating a testing setup5.1.1 The pytest testing framework5.1.2 Adding test coverage measurement5.1.3 Increasing test coverage5.2 Addressing testing tedium5.2.1 Addressing repetitive, data-driven tests5.2.2 Addressing frequent package installation5.2.3 Configuring test environments5.2.4 Tips for quicker and safer testingAnswers to exercisesSummary
6 Automating code quality tooling
6.1 The true power of tox environments6.1.1 Creating nondefault tox environments6.1.2 Managing dependencies across tox environments6.2 Analyzing type safety6.2.1 Creating a tox environment for type checking6.2.2 Configuring mypy6.3 Creating a tox environment for code formatting6.3.1 Configuring black6.4 Creating a tox environment for linting6.4.1 Configuring flake8Answers to exercisesSummary
Part 3. Going public
7 Automating work through continuous integration
7.1 The continuous integration workflow7.2 Continuous integration with GitHub Actions7.2.1 A high-level GitHub Actions workflow7.2.2 Understanding GitHub Actions terminology7.2.3 Starting a GitHub Actions workflow configuration7.3 Converting manual tasks to GitHub Actions7.3.1 Running a job multiple times with a build matrix7.3.2 Building Python package distributions for a variety of platforms7.4 Publishing a packageSummary
8 Authoring and maintaining documentation
8.1 Some quick philosophy on documentation8.2 Starting your documentation with Sphinx8.2.1 Automating documentation refresh during development8.2.2 Automating extraction of code documentation8.3 Publishing documentation to Read the Docs8.3.1 Configuring Read the Docs8.4 Documentation best practices8.4.1 What to document8.4.2 Prefer linking over repetition8.4.3 Use consistent, empathetic language8.4.4 Avoid assumptions and create context8.4.5 Create visual interest and coherent structure8.4.6 Powering up your documentationSummary
9 Making a package evergreen
9.1 Choosing a package-versioning strategy9.1.1 Direct and indirect dependencies9.1.2 Python dependency specifiers and dependency hell9.1.3 Semantic versioning and calendar versioning9.2 Getting the most out of GitHub9.2.1 The GitHub dependency graph9.2.2 Mitigating security vulnerabilities with Dependabot9.3 Thresholding test coverage9.4 Updating Python syntax using pyupgrade9.5 Reducing rework using pre-commit hooksAnswers to exercisesSummary
Part 4. The long haul
10 Scaling and solidifying your practices
10.1 Creating a project template for future packages10.1.1 Creating a cookiecutter configuration10.1.2 Extracting a cookiecutter template from an existing project10.2 Using namespace packages10.2.1 Converting an existing package to a namespace package10.3 Scaling packaging in your organization10.3.1 Private package repository serversSummary
11 Building a community
11.1 Your README needs to make a value proposition11.2 Provide supporting documentation for different user types11.3 Establish, provide, and enforce a code of conduct11.4 Conveying the project’s road map, status, and changes11.4.1 Using GitHub projects for kanban management11.4.2 Use GitHub labels to track status for individual tasks11.4.3 Track high-level changes in a log11.5 Gather consistent information with issue templates11.6 Go forthSummary
Appendix A. Installing asdf and python-launcher
A.1 Installing asdfA.2 Installing python-launcherAnswer to exercise A.1
Appendix B. Installing pipx, build, tox, pre-commit, and cookiecutter
B.1 Installing pipxB.2 Installing buildB.3 Installing toxB.4 Installing pre-commitB.5 Installing cookiecutter
index
inside back cover

Content preview from Publishing Python Packages

9 Making a package evergreen

This chapter covers

Choosing a versioning strategy for your package releases
Automating dependency updates with GitHub’s Dependabot
Setting test coverage thresholds
Upgrading your Python syntax using pyupgrade
Reducing rework using pre-commit hooks

In previous chapters, you successfully built a package locally and then published it so developers at all your client companies could benefit from all your hard work. You might imagine at this point that you’ve done most of the work, but releasing a project is often just the beginning for many developers. After people start using your package, new and broken use cases start to surface. A popular open source project might turn into a years-long endeavor.

Even when the ...

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Publishing Python Packages

by Dane Hillard

9 Making a package evergreen

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