Packaging a Python project

Initial setup for package metadata

• git init

• .gitignore, see examples:

  • pyx-sys and mvdef
    • packaged projects with vim swap files and Python build/dist artifacts (build, dist, .eggs etc.)
  • dx
    • a packaged project as above, also with store/ and tmp/ data directories
  • emoji-liif with Jupyter notebooks

• README.md

  • title e.g. # myproject (add a brief description, copy this to the GitHub repo description)
  • ## Usage (brief note on what usage will look like if you've not written it yet)
  • ## Requirements list - Python 3 and if there are many packages, put them on one line usually
    • These should be the proper human-readable names (the pip names may differ, they go in requirements.txt)
    • If there are complex conda environment requirements or different options, write a little backticked block to reproduce it (as here for emoji-liif)

• LICENSE

  • If you haven't pushed to GitHub yet, copy a LICENSE (the text will be detected)
    • E.g. from dx (click 'Raw' to get plain text)

• pyproject.toml

  • Handles the requirements for building binaries/wheels to ship (not required for package users), namely setuptools, wheel, setuptools_scm for auto-versioning
  • Copy the one for mvdef
  • Versioning is incremented by git tags (see the tags for mvdef for examples). These allow you to do 'releases' (again see mvdef)

• echo > requirements.txt (if you haven't written any code yet, otherwise list requirements for pip)

• setup.py

  • Handles the metadata for pip installation
  • Copy an example like dx
    • Change the classifiers for 'intended audience', license, and so on, to match your project
    • find_packages will auto-detect the package inside src
    • If you want command-line entrypoints these can be added (e.g. mvdef has 4 such console_scripts)
    • The version_scheme interprets the git tag to auto-version upon each new git tagging
      • I use helper functions retag_republish_major/minor/micro to automate the simultaneous git tag and PyPi republish steps, see Rebuild Python package for PyPi and republish for code/further details

Package source code

Paths in the following sections are under src/pkgname (replacing 'pkgname' with your package)

• share indicates /src/pkgname/share

Path management

• Create modules for each section (e.g. share, dataset, model) as well as a top-level __init__.py and any computation for things to be exported into the main namespace there in utils.py (but do not crowd this directory with anything else!)

Data management

• Keep data separate from computation on that data

  • src contains both but not in the same subdirectories

• Keep fixed data files used for computation in share/data

  • This will be included in the distributed package via setup.py by setting include_package_data=True in setuptools.setup()

• Keep artifacts [outputs computed from running your package code] in store/

  • This will be excluded from the distributed package via .gitignore by the line **/store/*

• Don't put data at the top-level (above src) outside the package, your code won't be able to interact with it properly (by exposing directory paths from __init__.py files)

  • as done for dx in share/data/__init__.py

    from . import __path__ as _dir_nspath
    from pathlib import Path as _Path
    
    __all__ = ["dir_path"]
    
    dir_path = _Path(list(_dir_nspath)[0])

    which is then provided to the share.scraper.parse_topics module:

    from ..data import dir_path as data_dir