- Install it into an existing Python interpreter. This is the most common scenario, but it requires setting up a copy of the interpreter. At best, this means an entirely separate step, one fraught with complexity if Python versions already exist on the system. This is also the scenario people want to avoid in the first place, because they want to make their app as easy to redistibute as possible.
- Bundle the interpreter with the program and its dependencies. This is the approach taken by projects like PyInstaller and Nuitka. The downsides are that the deliverables tend to be quite large, and creating them requires learning the quirks of these projects. But they do work.
- Use a system like Docker to bundle the program. Docker containers introduce their own world of trade-offs. On the one hand, you get absolutely everything you need to run the program, including any system-level dependencies. On the other hand, the resulting container can be positively hefty. And, of course, using Docker means adopting an additional software ecosystem.
Some of the newer solutions to the problem try to solve one particular pain point or another, as a way to make the whole issue less unpalatable. For instance, PyApp uses Rust to build a self-extracting binary that installs the needed Python distribution, your app, and all its dependencies. It has two big drawbacks: you need the Rust compiler to build it for your project, and your project must be an installable package that uses the pyproject.toml standard. The first of these requirements is likely to be the larger hurdle; most Python projects need a pyproject.toml of some kind at this point.
Another solution is one I wrote myself: pydeploy. It also requires the project in question be installable via pip install. Otherwise, pydeploy needs nothing more than Python’s standard library to generate a self-contained deliverable with the Python runtime included. Its big drawback right now is that it only works for Microsoft Windows, but in theory it could work on any operating system.
Maybe someday
All the recent major changes being proposed for Python, such as the new native JIT and full concurrency or multithreading, are meant to enhance Python’s behavior as a dynamic language. Any proposals designed to change that dynamism essentially would mean creating a new language with different expectations about its behavior.
