Professional Python Project Template

This is an opinionated fork based on ds-template. Useful template to bootstrap new professional data science and python projects, with mind of production quality allowing to adapt to needs.

Documentation

Table of Contents

• What is it?
• What are the benefits?
• Getting started

What is it?

Its intended use is to generate basic, most common configuration - however each team and developer is encouraged to modify it for its special needs.

It is a result of our experiences with building data science projects and is a part of our internal best practices, however it is not a silver bullet and should be treated as a starting point for your project. Especially some settings might be less/more restrictive than you needs but we believe it is better to start with a good baseline and modify it later than to start from scratch.

What are the benefits?

Generated project consists of:

1. Basic python package structure:
   • setup.py - compatibility for pip install -e ..
   • setup.cfg - package metadata and dependencies.
   • pyproject.toml - all tools configuration (if support is present)
   • a very minimal python code + example test
2. pre-commit hooks:
   • black, flake8 - enforce code style
   • jupytext - (optional) sync jupyter notebooks to plain python files
   • pycln - cleanups unused imports
   • mypy - checks type errors
   • isort - sorts imports
   • pylint - provides static code analysis and enforces coding standard
   • pyupgrade - modernizes code for given python version
   • bandit - checks for security issues - required for SOC 2 Certification
3. Sphinx documentation:
   • basic preconfigured documentation template
   • recommended extensions
   • page with list of autogenerated thirdparty python packages list with licenses
4. Basic script to create venv
5. Minimal README.md file
6. Preconfigured semantic versioning with bump2version
7. Dockerfile for pre-commit image
8. Gitlab integration (default, optional):
   • linter stage (pre-commit run --all)
   • tests (pytest) + code coverage
   • license checks of installed packages
   • building and hosting documentation on GitLab Pages
   • building package and uploading to private GitLab Package registry
   • security: trivy - required for SOC 2 Certification
   • steps to rebuild linter docker image
9. Github integration (optional):
   • linter stage (pre-commit run --all)
   • tests (pytest) + code coverage
   • license checks of installed packages
   • building and hosting documentation on GitHub Pages (if enabled)
   • testing if package can be build
   • security: trivy - required for SOC 2 Certification, but is reporting only
10. Other less important files (more configurations, .gitignore etc)

Documentation for parent project with rather matching descriptions, tips and explanations are in the documentation.

Getting started

Generate project template locally:

Install cookiecutter (at least >=2.1.1 version) first and then point it to this repository.

    $ pip install cookiecutter>=2.1.1

Cookiecutter will ask you set of questions so it can generate customized project.

    $ cookiecutter py-template/
    author []: Author Name
    project_name [default]: Sunglass
    repo_name [client-name-sunglass]:
    ...

Author can be your company name. It will be used in the documentation and in the package metadata.

How to initialize new repository with the template:

Firstly, you need to create a new project. The name should be of the following convention:

<project_name>

Execute the following steps then:

Approach 1 (clone empty):

# clone empty repository to repo_name
$ git clone <GIT-SSH>
# install cookiecutter if not yet installed
$ pip install cookiecutter
# generate cookiecutter with --force and ensure the repo_name is set to the same name as directory you cloned git repository to.
$ cookiecutter -f [email protected]:rAum/py-template.git
# finally, add all files, commit and push.
$ git add .
$ git commit -m "Initialize repository with default project template"
$ git push origin

Approach 2 (initialize git locally and push to remote):

# install cookiecutter if not yet installed
$ pip install cookiecutter
# generate project
$ cookiecutter [email protected]:rAum/py-template.git
# enter created directory
$ cd <project-name>
# now we need to connect it to repository (assuming empty repository)
$ git init
$ git remote add origin <GIT-SSH>
$ git fetch
$ git checkout -t origin/main
# finally, add all files, commit and push.
$ git add .
$ git commit -m "Initialize repository with default project template"
$ git push --set-upstream origin main
$ git push origin