I would say Python is used pretty much everywhere in the industry, even outside of the data science niche it seems to be popular in right now. Its ease of use a

I would say Python is used pretty much everywhere in the industry, even outside of the data science niche it seems to be popular in right now. Its ease of use and extensive standard library makes it a great choice for one-off CLI tools as well as quick prototyping, and it is not uncommon to e.g. have Python scripts as part of the build pipeline for a C++ or Rust project, or to develop the first iteration of a web app using Flask or even Bottle. In my opinion Python's strength does not lie in the language itself but rather in its third party package ecosystem. Libraries such as NumPy, Requests and PIL/Pillow provide optimized (and often native) black-box implementations of common tasks and wrap them into simple APIs, turning Python into basically the modern equivalent of shell scripting. You can usually "pipe" data from one library to the other and create complex pipelines in just a few lines of code, without sacrificing too much performance since most of the magic happens within the packages' DLLs. I guess this is what made it popular for data analysis in the first place. I would strongly recommend OP to familiarize themselves with NumPy and other popular packages, including the data-science-oriented ones, as they might come in handy when writing nontrivial scripts. Writing simple command line tools is also useful for getting used to the standard library.