Why Standalone Python Apps Are Hard To Build

You spend weeks building a useful Python tool. It works perfectly on your machine. You try to share it with a teammate. It fails because they do not have Python installed.

This is a common struggle. Packaging Python into a single file that works everywhere is difficult. It is not just about a missing requirements file.

The problem starts with how Python works.

Python is a dynamic language. It makes many decisions while the program runs. It can import libraries on the fly. It can change how functions work while they execute.

Compiled languages like Go or Rust decide everything before the program starts. They strip out unused code to make small, fast files.

Python cannot do this. You cannot easily predict which parts of the runtime your code will need. To ensure it works, you must include the entire Python runtime.

This leads to the library problem.

In other languages, compilers use tree-shaking to keep files small. Python cannot use this. You must include every dependency and every sub-dependency. You also must include compiled binary files. This makes your small script turn into a 300MB package.

You have three main options:

  • Require Python on the target machine. This works for developers but fails for general users.
  • Bundle the interpreter. Tools like PyInstaller or Nuitka pack the runtime into your app. PyInstaller is popular. Nuitka converts your code to C to improve speed.
  • Use Docker. This provides total reliability by including the entire operating system layer. It works well for servers but creates large files.

New tools like PyApp offer a middle ground. They use a small launcher to download the right Python version for you.

How to choose:

• For technical teams: Use a virtual environment and a requirements file. • For non-developers: Use PyInstaller. It is the most tested method. • For servers and data pipelines: Use Docker. Reliability matters more than file size here. • For desktop apps where speed matters: Use Nuitka.

Python is not changing its core nature to fix this. The language stays dynamic. We must rely on better tools to bridge the gap.

Source: https://dev.to/azadarjoe/why-standalone-python-apps-are-so-hard-to-build-3g31