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Hi, I am looking for a transpiler that converts Python into C, not something like in Cython where it gets transpiled to an extenion? More on discuss.python.org

Here's what I often write to devs moving over (some links might be helpful): Python is just another coding language, so existing skills in programming and domain knowledge will benefit potential employers/clients even if they require Python coding. Experienced programmers know that coding is a small part of programming, but proficiency in (and enjoyment of) any particular language, best suited to particular problem types, is, of course, highly beneficial. There are many areas that Python has become well established in. Machine learning and AI are very well served in Python. Alongside R for the more statistically inclined, data science & analytics is an incredibly common field for Python. The latest release of Excel includes Python in recognition of this. A review of Python Success Stories , on the Python Software Foundation website, will provide a good view of the wide range of areas Python has been used in. Python isn't the best answer for all problems (and may be highly unsuitable for some, of course), although it might be the most pragmatic in organisations that have a lot of experience in, and well established ecosystems around, it (such as sophisticated CI/CD pipelines). For example, Python isn't the best language for modern triple-A games, but it is heavily used by many games software houses to orchestrate, automate, optimise the work. Some of the largest consumer services in the world are heavily Python based, such as Instagram (leaning strongly on the Python Django web framework). Most experienced programmers shall be well versed in data structures, algorithms, design patterns, and so on. They are largely independent of the coding language. The same principles apply to Python, although the implementation patterns (and efficiencies) will vary. Similarly, successful programmers will likely be comfortable with CI/CD tooling and workflows, which are just as important for Python as for other languages. Programmers new to Python may want to spend some time looking at the most popular testing frameworks, though, such as PyTest (rather than the standard unittest) to work with those pipelines. Packaging for Python is perhaps another area to get some experience around as that will be different from other languages, especially given that as standard Python is not compiled to binary. (for those not aware, the standard CPython reference implementation compiles to byte code, much like happens with Java, for execution in a Python Virtual Machine, built into CPython.) I'd recommend looking at videos on YouTube by ArjanCodes , especially those doing some kind of code reviews (will help you spot a lot of potential problems). One book I would recommend is Fluent Python, 2nd Edition by Luciano Ramalho. Additional tips A quick look at the docs, should suffice for many programmers taking up Python There are a few underpinning differences to be aware of Python is not statically typed, some mistake this for with weak typing Python is in fact strongly typed, Applies at run time, not at compile time Python is compiled to a byte code, just like Java, but where the latter executes on a JVM, the Python virtual machine is built into the standard implementations of Python as part of the same programme (CPython for the reference implementation) type hinting, is option but will help your IDE greatly in spotting potential problems Ignored at run time, just there for your benefit There are also some external tools that can be run to check types using the type hints, which can be useful for testing in a CI/CD pipeline pydantic is a popular library for taking typing management further Essentially, everything in Python is an object all variables/names are effectively pointers (they reference the objects in memory) but without all the features you get in C (e.g. no pointer arithmetic) but there isn't a pointer type Python does its own garbage collection / memory management (and uses a reference counting system to know when objects are no longer required) functions are first class citizens for is more of a for each variables assigned to objects inside a loop, remain in-scope beyond the loop Python uses indenting to distinguish code blocks, rather than ; and {}, white space is important (recommended default indentation is 4 spaces (sic)) A couple of videos to watch which, despite being old, will lock in some key differences in approach to keep in mind: Loop like a native: while, for, iterators, generators presented by Ned Batchelder Python's Class Development Toolkit by Raymond Hettinger (a Python core developer) Given the referenced implementation of Python is written in C and Python, a quick look at the source code will resolve many queries for experienced programmers as well. Overall, there is much less boilerplate code required in Python than typical C/C++ projects. There are a huge number of libraries/packages to use, many of which are written in C (such as NumPy) for performance. It can be useful to use some of the existing C/C++ code from Python rather than completely recoding in Python. The Cython project, offering C extensions for Python, might be worth looking at if there is a lot of C/C++ code to exploit. More on reddit.com

I learned Python as my first language. I am by no means good at programming, but I had to learn it so I did. It is pretty straightforward in terms of syntax and it reads kind of like English. I guess it was good for me to focus on the logic rather than the syntax and other things. Now I have to learn C++ and for the most part i am happy to have the logic part down and now it's just a matter of time to get used to the syntax and specific things of C++.

I think if I would have learned c++ first I would have been very overwhelmed, but I had no experience at all and everything was foreign to me so I can't say if this is the case for you.

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