maximlt

Steps to create a good project base for developing in a conda environment

Step-by-step (uncomplete) tutorial for setting up a base Python library given the following requirements:

• Use conda (instead of pipenv or others) because this is both a package manager and an environment manager, and installing the Python scientific stack (Numpy, Pandas, Scipy, Matplotlib, etc.) is straightforward
• Use Visual Studio Code (instead of PyCharm, Spyder or others) because it's free, runs on Windows and is one of the mostly used IDE
• Document and automate as many production steps as possible including linting (flake8), formatting (black), packaging (setup.py, setup.cfg), versionning (git), testing (pytest, pytest-cov, tox), documenting (sphinx, readthedocs), building (setuptools) and distributing (twine, keyring)
• Include IPython Notebooks and have them tested (pytest-nbval)

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