Designed for beginners, this presentation demystifies Python project management using Hatch and delves into pyproject.toml for efficient configuration. We'll guide you through organizing directories, implementing unit testing for code reliability, and using mypy for type checking to enhance code quality. The session concludes with insights into ruff, a modern linter for maintaining Python standards, which is replacing black, isort, flake8. This talk is a comprehensive toolkit for anyone eager to learn and apply the latest practices in Python development. The talk was given at PyConDE / PyData Berlin 2024. More details here: https://pretalx.com/pyconde-pydata-2024/talk/CBVTEG/

1. © 2024
A Guide to a Modern
Project Setup
PyConDE / PyData 2024, April 22nd
Florian Wilhelm
Streamlining
Python
Development

2. Mathematical Modelling
Modern Data Warehousing & Analytics
Personalisation & RecSys
Uncertainty Quantification & Causality
Python Data Stack
OSS Contributor & Creator of PyScaffold
Dr. Florian Wilhelm
• HEAD OF DATA SCIENCE
FlorianWilhelm.info
florian.wilhelm@inovex.de
FlorianWilhelm

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design,Backend Services)
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Intelligence, Big Data, Searches, Data Science
and Deep Learning, Machine Perception and
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‣ Scalable IT-Infrastructures (IT Engineering, Cloud
Services, DevOps, Replatforming, Security)
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4. 1. Introduction:
a. What makes a good project setup?
b. How do we achieve it?
2. Streamlined Project Setup:
a. configuration with pyproject.toml
b. tooling with hatch, ruff, mypy, pytest, …
3. Conclusion
Agenda

5. Introduction

6. 1. efficient development
2. easy collaboration
3. seamless build & deployment
What makes a streamlined Python Project Setup?

7. 1. Conventions
a. project structure
b. code formatting, e.g., pep8, black, ruff
c. documentation, e.g., Sphinx, mkdocs
2. Automation
a. dependency & environment management
b. building & publishing
c. versioning, e.g., semantic versioning
d. testing, linting/formatting, type checking
3. Easy to Use!
Concrete Requirements for those Goals

8. Semantic Versioning
‣ tells developers what to
expect
‣ avoids dependency hell for
developers using your
software
‣ necessary for requirement
specifiers like ~= 2.21 or
^2.2.21 (Poetry only)
More Details: https://www.geeksforgeeks.org/introduction-semantic-versioning/ and https://semver.org/

9. This is not a talk about the best Package Management Tool
Source: An unbiased evaluation of environment management and packaging tools (https://www.inovex.de/de/blog/)

10. Streamlined Project Setup

11. ‣ reproducibly building & publishing packages
‣ robust environment management with support for
custom scripts
‣ easy Python management, replacing pyenv
‣ easy semantic versioning based on Git tags
‣ sophisticated testing within various environments,
replacing tox
🐣 Hatch, the extensible Python project manager
Ofek Lev

12. ‣ folders for
∙ source files
∙ documentation
∙ tests
‣ human-readable information
∙ README.md
∙ …
‣ configuration files
∙ pyproject.toml
∙ …
Project Directory Structure

13. ‣ defines the build system
‣ metadata about your project
for PyPI
‣ configuration for (almost) all
tools
∙ pytest
∙ mypy
∙ ruff
∙ coverage
All-in-One Configuration with pyproject.toml

14. Scripts in pyproject.toml for automation of tasks, e.g.
∙ running unit-tests with our without coverage, debugging,
∙ building the documentation,
∙ running the linters, code checks, mypy,
∙ …
Automation with Scripts!
> hatch run test:cov

15. ‣ replaces tons of tools
‣ easy configuration via
pyproject.toml
‣ extremely fast
‣ over 700 plugins
Code Quality: Linting & Formatting
Ruff
flake8
autoflake
pydocstyle
…

16. Why mypy?
Type Checking: Are you my type?
compile-time type checking finds many errors in
advance, often edge cases.
type declaration act as machine-checked
documentation, thus enhancing the dev
experience.

17. Mypy Example
> hatch run lint:typing

18. pytest
‣ defacto standard for unit testing
‣ powerful features like fixtures, etc.
‣ tons of useful plugins, e.g.:
∙ pytest-cov for coverage
∙ pytest-recording for mocking calls to external services
∙ pytest-sugar to make it easier on the eyes
Testing with pytest & hatch
hatch & tox
‣ isolated environments for testing different Python versions and
dependency combinations

19. Avoiding human-errors by automated checks on every git commit
Automated QA with pre-commit

20. ‣ Automatic and reproducible testing
‣ Publishing packages based on git tags
‣ Established branching strategy, e.g. GithubFlow
for efficient collaboration
‣ Scalability and Adaptability when needed
‣ Automated deployments, building of
documentation etc.
Automation with CI/CD
More Details: Data Science in Production: Packaging, Versioning and Continuous Integration (https://www.inovex.de/de/blog/)

21. Conclusion
‣ unified configuration in pyproject.toml
‣ standardized folder structure with
src-layout and useful README.md
‣ easy package management and
automation with hatch
‣ automated QA with ruff, pytest,
pre-commit, mypy, CI/CD
‣ proper documentation with mkdocs
‣ automation & conventions are key!

22. https://github.com/FlorianWilhelm/the-hatchlor
Check out the Hatchlor!
⭐

23. CHEERS TO THE COMMUNITY
Credits & Resources
‣ Ofek Lev, the creator of hatch, for is
awesome work in his spare time ❤
‣ Michael Hofmann from inovex who
made these awesome slides

24. © 2023
Thank you!
Dr. Florian Wilhelm
Head of Data Science
PyConDE / PyData 2024
inovex.de
florian.wilhelm@inovex.de
@inovexlife
@inovexgmbh