As someone with nearly two decades of experience with Python and who has deployed it in production in high impact scenarios, I’d like to nuance the above for p

As someone with nearly two decades of experience with Python and who has deployed it in production in high impact scenarios, I’d like to nuance the above for people who think Python is only good for prototyping and that you have to rewrite in a “proper” programming language. This is the kind of blanket thinking to avoid. I think the rewriting part is only necessary if there’s some characteristic in your use case that you need that Python doesn’t provide — like raw computational speed, low latencies etc. If you’re a senior engineer familiar with Python, these trade offs are always front of mind. For the majority of software I’ve developed, these characteristics were not essential. Python was a good choice for 80-90% of all of the important software products I’ve ever written. For many data science projects, maintaining large code bases in Python is often the optimal decision (rather than reinventing the wheel and writing your own data frame and machine learning libraries, or using immature poorly maintained ones in other languages — for data manipulation and scientific algorithms, these libraries are highly optimized in Python anyway since the underlying code is in C or Fortran). Python is also a good choice for data engineering pipelines. Where I might hesitate to recommend Python is when you have to write web services or desktop apps or any kind of application which requires speed or scale that cannot be handed down to a lower level library in Python. Also for very large code bases, static typing truly helps to keep things sane, especially when you have different teams working on different parts of the codebase — with statically typed languages, no type checks in unit tests are needed, and refactoring is much more solid and error free. Python’s type annotations are an attempt to move in this direction, but static languages truly excel at type integrity (which are sometimes the cause of subtle errors in Python). Otherwise my experience has often been that Python is a good first or second choice, depending on what you’re doing. Making that choice correctly is what sets senior/principal engineers apart from junior folks.