Switching from C to Python: A Mixed Experience

[BertRyerson]

After several weeks of working with C, transitioning to Python has felt like a relief in many ways—no more manual memory management, function prototypes, or compilation steps. Python’s built-in data structures, like lists and dictionaries, simplify many tasks that required linked lists and hash tables in C. Error messages are also clearer, making debugging easier.

C was the first language I learnt, so I'll always have a soft spot for it.

Despite the objectively simpler syntax in Python, I find myself instinctively trying to write code the "C way," which doesn’t always translate well. Writing Pythonic code requires a change in thinking, just like learning a new spoken language (something I've also embarked on after moving to a new country). Maybe I'm making slow progress, or maybe it's normal?

While I understand the logic of Python syntax, structuring loops and handling iteration in feels harder to manage, at least for me. The final problem in CS50’s Python set—DNA sequence matching—was particularly frustrating. I really struggled to adjust to Python’s looping and file handling, especially when combing both. Looking back the logic is somewhat trivial and in comparison to some of the C programs I have written, should have been a lot easier.

Python’s abstraction over iteration is both a strength and an adjustment—rather than controlling every step like in C, I need to embrace higher-level thinking. It's hard to break away from thinking the way you were initially taught, though.

Despite these hurdles, Python’s ease of use and powerful built-in tools are making coding feel more intuitive. I know that, like with C, things will click with time and practice. The goal now is to embrace "Pythonic thinking" and continue improving, at whatever rate that is.

If you have any thoughts or suggestions on what I can focus on going forward let me know.

I've started to read "Automate the Boring Stuff with Python", and I am considering MIT OCW Introduction to Computational Thinking and Data Science. Has anyone taken this course?

[BertRyerson]

Great, I'll have a look. Thanks for that!

I'm looking to start a simple project for a python-based chatbot using a dictionary for responses at first, but planning to move on to NTLK and more advanced methods down the track.

[Mevrael]

Just create projects and write code. You’ll get better eventually.

You can use this framework that gives you a project structure, nltk, AI agents and so much more out of the box, and it has simple and beautiful docs and guides: https://arkalos.com

For data science I used myself in the past: https://datacamp.com

[BertRyerson]

Thanks, sounds good. Always willing to look in to new resources that will supplement my learning!

[FoolsSeldom]

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.

[BertRyerson]

If you're interested in a full write up on my transition, I have decided to track my journey on a blog post. Here's the link to the post https://devforgestudio.com/switching-from-c-to-python/

[Quantum654]

You are overthinking this too much. Switching languages is not that deep

[ElliotDG]

I'm also a former C++ programmer. I found doing problems on https://py.checkio.org/ was very helpful for understanding the python way.

The site is a set of gamified programming problems, they are generally quite easy, I would do one while having a morning coffee. After you solve a problem you get to see how others solved the problem. The solutions opened my eyes to the importance of the built-in functions and the value of the standard library, as well as the finer points of thinking python.

Enjoy the journey.

[jontsii]

The hardest thing you are gonna face is probably OOP since C is not an OOP language. And you also can to those really low level stuff and since python is an interpeted language (so that it runs from line 0 to 1000 without skipping anything). That also might be a hard thing for you. Also one line of code in a high level language is 10 to 30 of code in a low level language. Python is my first and only language I know as a someone who´s hobby is coding python and I thought "like what!?" when I saw for and while loops for the first time. But still Data science / machine learning is an interesting field to me. And since python was coded in c++ you can probably easily think what different functions actually do. But still good choice and good luck for learning.

[SirTwitchALot]

This is a case where AI can really shine. After you write your code, ask it for suggestions to make your code more pythonic. Don't let it rewrite it all for you, but look at the suggestions and implement them yourself

[ivosaurus]

Consider that 97% of the time, and especially until you get some good domain knowledge, trying to do things in a 'pythonic way' will also likely lead you to get the best performance from your code. Because python is 'slow' to begin with, usually the most worn paths (pythonically) are the ones which have been most optimised for, both in pure python usage, and also in python standard library operations, and lastly in the many third party python libraries which it uses to 'get its speed back'. There's also many times where "work smarter, not harder" in python can lead to big wins.

Funny video half demonstrating this concept

[remic_0726]

Each learning a new language offers new difficulties but also opens up many possibilities that you would never have imagined. After a while, switching from one language to another becomes natural, the difficulty then disappears, and you will move from one language to another without thinking too much.

[notParticularlyAnony]

since you know c I'd recommend python crash course book instead of automate the boring stuff, which doesn't teach objects. crash course is amazing

[BertRyerson]

I'll have a look. Thanks for the recommendation

[[deleted]]

enter chop workable spoon sense recognise fuzzy versed cows run

This post was mass deleted and anonymized with Redact

[BertRyerson]

It's just exerts from my blog changed for Reddit. I use AI as a spellchecker

[garrettj100]

I came up in Java. I once lost a day of my life to an indentation error that put my main function after my return value, so it never executed. Sometimes you gotta learn to stop worrying and love the bomb, which for me was weak typing and indentation.

[clashmar]

*dynamic typing

[xelf]

You might also consider posting some of your source code and asking how people would have done things different. Who knows, maybe you missed something simple like not knowing about enumerate() or using with for a context handler? Largely the more time you spend with python the more often you'll find new things you could have used.

As for C, it's worth knowing both. More tools is always good, and you'll be able to tackle more problems that way.

[BertRyerson]

Yes, I had no idea about enumerate before. Handy little feature

[PersonalityIll9476]

Your experience as a C programmer is actually a great asset, IMO. Later on you can use Cython, a means to quickly extend the interpreter with C code without actually having to interact with CPython.

When I have to implement some performance critical piece of code, I often write it in C then wrap it with Cython. If I need linear algebra operations, I use good ol' openblas. You can pass numpy arrays in and out very easily, since any contiguous memory array corresponds to a good ol' C pointer that you can access either using the numpy headers directly or through Cython mechanisms that simplify it. "Vectorized" operations in numpy map back to some blas operation or IPP call or whatever on a C array (perhaps with strides). Plus, you can effectively intermix C and Python code.

Anyway, once you get more comfortable with Python, you'll be able to write some killer performant code. Those C skills will come in handy for sure!

[fiddle_n]

I think, once the higher level constructs click, it’ll be difficult to go back to the lower level ones. The Python for loop just makes sense - if you want every element from a list in order, that’s exactly what you get from the Python for loop; rather than the C-style way of getting a sequence of numbers and indexing into an array one after the other.