When I started to learn Python a few years ago, I often wondered about what’s the “correct” or “best” way to prepare your system’s Python environment for the requirements your software project or some Python-based application you’d like to start using may have: Should I install modules using the package manager of my OS? Or by using Python tools for it like pip? What are “virtual environments” and how do I utilize these for my projects? What’s all this pyenv, pip, pipenv, easy_install, setuptools, anaconda, conda, miniconda …
In this article series, I’d like to introduce the most common tools and techniques on how to do this in the Python world.
At the end of the series, I will share some of my thoughts, doubts, and questions I had back then, tell about some experiences I gathered in the meantime and generally share the outcome of this journey and what my Python-Workflow looks like, nowadays.
Introduction to pyenv 🐍
This first article is about pyenv, a lightweight, yet powerful, Python version management tool that works in user – scope and does stay out of the way of systems global Python interpreters.
Born in 1982, Marc Richter is an IT enthusiastic since 1994. He became addicted when he first put hands on their family’s pc and never stopped investigating and exploring new things since then.
He is married to Jennifer Richter and proud father of two wonderful children, Lotta and Linus.
His current professional focus is DevOps and Python development.
An exhaustive bio can be found at this blog post.
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