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Python-Based Tools#

Beyond its data science uses, Python can also be used to install development tools. This article covers installing those tools in a manner consistent with the Iron Law of Python Management.


pipx enables you to put Python tools with command-line interfaces in isolated environments.

First, install pipx:

For server installations, you will use one of the server versions of Python to install pipx. You must also provide the --user flag so that pipx is only installed for your account.


$ /opt/python/3.9.2/bin/python -m pip install pipx --user
$ /opt/python/3.9.2/bin/python -m pipx ensurepath

Using your pyenv global version of Python, install pipx and then rehash to make pyenv aware of it:


$ python -m pip install pipx
$ python -m pipx ensurepath
$ pyenv rehash

Black, a Python tool for formatting code, is a good example of a tool you might want to install this way--you may want to format Python code across several Python projects without installing it into each project.


WDAGUtilityAccount@mvp MINGW64 ~/Documents/python-examples (master)
$ pipx install black
  installed package black 20.8b1, Python 3.9.2
  These apps are now globally available
    - black-primer.exe
    - black.exe
    - blackd.exe
done! ✨ 🌟 ✨

Confirm that it worked:


WDAGUtilityAccount@mvp MINGW64 ~/Documents/python-examples (master)
$ black --version
black, version 20.8b1


Notebooks are a popular interface for editing Python data science code. Using them correctly with virtual environments can be a bit challenging because:

The easiest way to use notebooks with virtual environments on server and desktop is to use the Jupyter extension in VS Code.


Conda is a package and environment manager which you can use to follow many of the strategies outlined in this series. However, many data science packages can be installed easily without it.