<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Pipx on Alexander Junge&#39;s website</title>
    <link>https://www.alexanderjunge.net/tags/pipx/</link>
    <description>Recent content in Pipx on Alexander Junge&#39;s website</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-US</language>
    <lastBuildDate>Sun, 20 Feb 2022 00:00:00 +0000</lastBuildDate>
    
	<atom:link href="https://www.alexanderjunge.net/tags/pipx/index.xml" rel="self" type="application/rss+xml" />
    
    
    <item>
      <title>One JupyterLab, many projects</title>
      <link>https://www.alexanderjunge.net/blog/pyenv-virtualenv-poetry-jupyter/</link>
      <pubDate>Sun, 20 Feb 2022 00:00:00 +0000</pubDate>
      
      <guid>https://www.alexanderjunge.net/blog/pyenv-virtualenv-poetry-jupyter/</guid>
      <description>Jupyter notebooks edited in JupyterLab are my tool of choice when working with and exploring data in Python. I frequently mature code stored in notebooks to importable .py files and further to stand-alone Python packages. I recently read the &amp;ldquo;Everything Gets a Package&amp;rdquo; post where Ethan Rosenthal describes his data science project setup. This led me to rethink how I manage virtual environments, dependencies and JupyterLab installations across projects.</description>
    </item>
    
  </channel>
</rss>