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#
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.
Terminal
$ /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:
Terminal
$ 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.
Terminal
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:
Terminal
WDAGUtilityAccount@mvp MINGW64 ~/Documents/python-examples (master)
$ black --version
black, version 20.8b1
Notebooks#
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#
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.