Well my team does that for one. We use Python packaging ecosystem, specify Python dependencies using standard tools like setup.py, requirements.txt and pip. All Python dependencies are baked into a fat Python package using PEX format[1]. Also tried Facebook's xar format[2], without success yet. What matters is to have a statically linked dependencies packaged in one executable file. Like a Windows exe file.
Then you proceed with bundling in higher level OS dependencies, because each app is not just a Python package but also a collection of shell scripts, configs, configuration of paths for caches, output directories, system variables, etc. For this we throw everything into one directory tree and run FPM [3] command on it which turns the directory tree into a RPM package. We use a fpm parameter to specify installation location of that tree to /opt or /usr elsewhere.
The way to bundle it properly is to actually use two rpm packages linked together by rpm dependency. One for the app and the other for the deployment configuration. The reason is you only have one executable, but many possible versions of deploying it. Ie. depending on environment (dev, staging, prod) or you just simply want to run the same app in parallel with different configuration.
eg. one rpm package for the app executable and statix files
my_app_exe-2.0-1.noarch.rpm
and many othe related config rpms
my_app_dev-1.2-1.noarch.rpm (depends on my_app_exe > 2.0)
my_app_prod-3.0-1.noarch.rpm (depends on my_app_exe == 2.0)
You then install only the deployment packages and rpm system fetches the dependencies for you automatically.
There are other mature places who use similar techniques for deployments, for example [4].
All of this then can be wrapped in even higher level of packaging, namely Docker.
[1] https://github.com/pantsbuild/pex
[2] https://code.fb.com/data-infrastructure/xars-a-more-efficien...
[3] https://github.com/jordansissel/fpm
[4] https://hynek.me/articles/python-app-deployment-with-native-...