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. Read more about how to use them here.

The Jupyter extension in VS Code is another way to work with notebooks.


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.

Back to top