Install and Execute Python Applications Using pipx

Install and Execute Python Applications Using pipx
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A straightforward way to distribute desktop and command-line applications written in Python is to publish them on the Python Package Index (PyPI), which hosts hundreds of thousands of third-party packages. Many of these packages include runnable scripts, but using them requires decent familiarity with the Python ecosystem. With pipx, you can safely install and execute such applications without affecting your global Python interpreter.

In this tutorial, you’ll learn how to:

  • Turn the Python Package Index (PyPI) into an app marketplace
  • Run installed applications without explicitly calling Python
  • Avoid dependency conflicts between different applications
  • Try throw-away applications in temporary locations
  • Manage the installed applications and their environments

To fully benefit from this tutorial, you should feel comfortable around the terminal. In particular, knowing how to manage Python versions, create virtual environments, and install third-party modules in your projects will go a long way.

To help you get to grips with pipx, you can download the supplemental materials, which include a handy command cheat sheet. Additionally, you can test your understanding by taking a short quiz.

Get Started With pipx

On the surface, pipx resembles pip because it also lets you install Python packages from PyPI or another package index. However, unlike pip, it doesn’t install packages into your system-wide Python interpreter or even an activated virtual environment. Instead, it automatically creates and manages virtual environments for you to isolate the dependencies of every package that you install.

Additionally, pipx adds symbolic links to your PATH variable for every command-line script exposed by the installed packages. As a result, you can invoke those scripts directly from the command line without explicitly running them through the Python interpreter.

Think of pipx as Python’s equivalent of npx in the JavaScript ecosystem. Both tools let you install and execute third-party modules in the command line just as if they were standalone applications. However, not all modules are created equal.

Broadly speaking, you can classify the code distributed through PyPI into three categories:

  1. Importable: It’s either pure-Python source code or Python bindings of compiled shared objects that you want to import in your Python projects. Typically, they’re libraries like Requests or Polars, providing reusable pieces of code to help you solve a common problem. Alternatively, they might be frameworks like FastAPI or PyGame that you build your applications around.
  2. Runnable: These are usually command-line utility tools like black, isort, or flake8 that assist you during the development phase. They could also be full-fledged applications like bpython or the JupyterLab environment, which is primarily implemented in a foreign TypeScript programming language.
  3. Hybrid: They combine both worlds by providing importable code and runnable scripts at the same time. Flask and Django are good examples, as they offer utility scripts while remaining web frameworks for the most part.

Making a distribution package runnable or hybrid involves defining one or more entry points in the corresponding configuration file. Historically, these would be or setup.cfg, but modern build systems in Python should generally rely on the pyproject.toml file and define their entry points in the [project.scripts] TOML table.

Each entry point represents an independent script that you can run by typing its name at the command prompt. For example, if you’ve ever used the django-admin command, then you’ve called out an entry point to the Django framework.

Once you identify a Python package with entry points that you’d like to use, you should first create and activate a dedicated virtual environment as a best practice. By keeping the package isolated from the rest of your system, you’ll eliminate the risk of dependency conflicts across various projects that might require the same Python library in different versions. Furthermore, you won’t need the superuser permissions to install the package.

Deciding where and how to create a virtual environment and then remembering to activate it every time before running the corresponding script can become a burden. Fortunately, pipx automates these steps and provides even more features that you’ll explore in this tutorial. But first, you need to get pipx running itself.

Test Drive pipx Without Installation

If you’re unsure whether pipx will address your needs and would prefer not to commit to it until you’ve properly tested the tool, then there’s good news! Thanks to a self-contained executable available for download, you can give pipx a spin without having to install it.

To get that executable, visit the project’s release page on the official GitHub repository in your web browser and grab the latest version of a file named pipx.pyz. Files with the .pyz extension represent runnable Python ZIP applications, which are essentially ZIP archives containing Python source code and some metadata, akin to JAR files in Java. They can optionally vendor third-party dependencies that you’d otherwise have to install by hand.

Afterward, you can run pipx.pyz by passing the path to your downloaded copy of the file to your Python interpreter—just as you would with a regular Python script:

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April 03, 2024 at 07:30PM
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