Bash vs Python: My Rules for Choosing the Right Tool

As someone who usually moves between physics data analysis, GPU-programming (for our beloved LHCb Trigger), and my humble self-hosted infrastructure. I often find myself in dire need to automate, prototype, or orchestrate tasks quickly and optimize for how lazy I am in many situations. That usually means reaching for either a Bash script or a Python script.

But usually I have to think which one to pick. I live my life on computers, so I don’t pick one arbitrarily. Over time, I’ve developed a clear mental model. It is a set of practical rules for when to stay in the shell and when to switch to Python. It’s not about one language being better than the other, it’s about matching the right level of complexity, portability, and maintainability to the task at hand.

Here’s how I decide.

I use Bash scripts as my lightweight automation layer. They’re great when I need to add together commands, handle files, or orchestrate build and deployment steps especially when dealing with environments like Proxmox, LXC containers, remote SSH hops, or CERN’s lxplus. Or where installing extra Python packages is either painful or just not possible (looking at you LHCb GPU trigger development test servers).

Here’s the rule of thumb I follow for sticking with Bash:

• The task is simple and mechanical
• I don’t need data structures beyond maybe an array
• I’m not doing anything more complex than looping or a bit of string parsing
• I’m not doing serious math (basic expr arithmetic is enough)
• Performance doesn’t matter, and I don’t need threading or multiprocessing (Or not hadding big .root files)
• I don’t need functional programming constructs or clean abstractions

On the other hand, Python is my go-to when the task gets even slightly complex: if I’m parsing structured logs, managing long-running monitoring tools with alerting logic (HTCondor and Dirac logs are painful to deal with). Python brings structure, testing, and readability especially when I want clean error handling, proper typing, or reusable functions and classes. In addition, NumPy, PyTorch, or even just argparse make it feel more like a programming language and less like a duct-tape of shell pipes.