Write C Code Without Learning C: The Magic of PythoC

an interesting library the opposite day that I hadn’t heard of before. 

PythoC is a Domain-Specific Language (DSL) compiler that enables developers to jot down C programs using standard Python syntax. It takes a statically-typed subset of Python code and compiles it directly right down to native machine code via LLVM IR (Low Level Virtual Machine Intermediate Representation).

LLVM IR is a platform-independent code format used internally by the LLVM compiler framework. Compilers translate source code into LLVM IR first, after which LLVM turns that IR into optimised machine code for specific CPUs (x86, ARM, etc.).

A core design philosophy of PythoC is: C-equivalent runtime + Python-powered compile-time, and it has the next almost unique selling points.

1. Creates Standalone Native Executables

Unlike tools similar to Cython, that are primarily used to create C-extensions to hurry up existing Python scripts, PythoC can generate completely independent, standalone C-style executables. Once compiled, the resulting binary doesn’t require the Python interpreter or a garbage collector to run.

2. Has Low-Level Control with Python Syntax

PythoC mirrors C’s capabilities but wraps them in Python’s cleaner syntax. To realize this, it uses machine-native type hints as a substitute of Python’s standard dynamic types.

• Primitives: i32, i8, f64, etc.
• Memory structures: Pointers (ptr[T]), arrays (array[T, N]), and structs (created by decorating standard Python classes).
• Manual Memory Management: Since it doesn’t use a garbage collector by default, memory management is explicit, similar to in C. Nevertheless, it offers modern, optional safety checks, similar to (which be sure that every allocation is explicitly deallocated to forestall leaks) and (to implement compile-time validation checks).

Python as a Metaprogramming Engine

One among PythoC’s strongest features is its handling of the compilation step. Since the compile-time environment is just Python, you should use standard Python logic to generate, manipulate, and specialise your PythoC code it gets compiled right down to LLVM. This provides you highly flexible compile-time code-generation capabilities (just like C++ templates but driven by pure Python).

It sounds promising, but does the truth live as much as the hype? Okay, let’s see this library in motion. Installing it is straightforward, like most Python libraries its only a pip install like this:

pip install pythoc

However it’s probably higher to establish a correct development environment where you possibly can silo your different projects. In my example, I’m using the UV utility, but use whichever method you’re most comfortable with. Type in the next commands into your command line terminal.

C:Usersthomaprojects> cd projects
C:Usersthomaprojects> uv init pythoc_test
C:Usersthomaprojects> cd pythoc_test
C:Usersthomaprojectspythoc_test> uv venv --python 3.12
C:Usersthomaprojectspythoc_test> .venvScriptsactivate
(pythoc_test) C:Usersthomaprojectspythoc_test> uv pip install pythoc

A Easy Example

To make use of PythoC, you define functions using specific machine types and mark them with PythoC’s compile decorator. There are two fundamental ways to run your PythoC code. You’ll be able to call the compiled library directly from Python like this,

from pythoc import compile, i32

@compile
def add(x: i32, y: i32) -> i32:
    return x + y

# Can compile to native code
@compile
def fundamental() -> i32:
    return add(10, 20)

# Call the compiled dynamic library from Python directly
result = fundamental()
print(result)

Then run it like this.

(pythoc_test) C:Usersthomaprojectspythoc_test>python test1.py

30

Or you possibly can create a standalone executable which you can run independently from Python. To do this, use code like this.

from pythoc import compile, i32

@compile
def add(x: i32, y: i32) -> i32:
    print(x + y)
    return x + y

# Can compile to native code
@compile
def fundamental() -> i32:
    return add(10, 20)

if __name__ == "__main__":
    from pythoc import compile_to_executable
    compile_to_executable()

We run it the identical way. 

(pythoc_test) C:Usersthomaprojectspythoc_test>python test4.py

Successfully compiled to executable: buildtest4.exe
Linked 1 object file(s)

This time, we don’t see any output. As a substitute, PythoC creates a construct directory underneath your current directory, then creates an executable file there which you can run.

(pythoc_test) C:Usersthomaprojectspythoc_test>dir buildtest4*
 Volume in drive C is Windows
 Volume Serial Number is EEB4-E9CA

 Directory of C:Usersthomaprojectspythoc_testbuild

26/02/2026  14:32               297 test4.deps
26/02/2026  14:32           168,448 test4.exe
26/02/2026  14:32               633 test4.ll
26/02/2026  14:32               412 test4.o
26/02/2026  14:32                 0 test4.o.lock
26/02/2026  14:32         1,105,920 test4.pdb

We are able to run the test4.exe file just as we might every other executable.

(pythoc_test) C:Usersthomaprojectspythoc_test>buildtest4.exe

(pythoc_test) C:Usersthomaprojectspythoc_test>

But wait a second. In our Python code, we explicitly asked to print the addition result, but we don’t see any output. What’s happening?

The reply is that the built-in Python print() function relies on the Python interpreter running within the background to determine tips on how to display objects. Because PythoC strips all of that away to construct a tiny, blazing-fast native executable, the print statement gets stripped out.

To print to the screen in a native binary, you will have to make use of the usual C library function: printf.

How you can use printf in PythoC

In C (and subsequently in PythoC), printing variables requires format specifiers. You write a string with a placeholder (like %d for a decimal integer), after which pass the variable you wish to insert into that placeholder.

Here is the way you update our code to import the C printf function and use it appropriately:

from pythoc import compile, i32, ptr, i8, extern

# 1. Tell PythoC to link to the usual C printf function
@extern
def printf(fmt: ptr[i8], *args) -> i32:
    pass

@compile
def add(x: i32, y: i32) -> i32:
  
    printf("Adding 10 and 20 = %dn", x+y)
    return x + y

@compile
def fundamental() -> i32:
    result = add(10, 20)
    
    # 2. Use printf with a C-style format string. 
    # %d is the placeholder for our integer (result).
    # n adds a brand new line at the top.
   
    
    return 0

if __name__ == "__main__":
    from pythoc import compile_to_executable
    compile_to_executable()

Now, if we re-run the above code and run the resulting executable, our output becomes what we expected.

(pythoc_test) C:Usersthomaprojectspythoc_test>python test5.py
Successfully compiled to executable: buildtest5.exe
Linked 1 object file(s)

(pythoc_test) C:Usersthomaprojectspythoc_test>buildtest5.exe
Adding 10 and 20 = 30

Is it really well worth the trouble, though?

All of the things we’ve talked about will only be value it if we see real speed improvements in our code. So, for our final example, let’s see how briskly our compiled programs might be in comparison with the equivalent in Python, and that ought to answer our query definitively.

First, the regular Python code. We’ll use a recursive Fibonacci calculation to simulate a long-running process. Let’s calculate the fortieth Fibonacci number.

import time

def fib(n):
    # This calculates the sequence recursively
    if n <= 1:
        return n
    return fib(n - 1) + fib(n - 2)

if __name__ == "__main__":
    print("Starting Standard Python speed test...")
    
    start_time = time.time()
    
    # fib(38) normally takes around 10 seconds in Python, 
    # depending in your computer's CPU.
    result = fib(40) 
    
    end_time = time.time()
    
    print(f"Result: {result}")
    print(f"Time taken: {end_time - start_time:.4f} seconds")

I got this result when running the above code.

(pythoc_test) C:Usersthomaprojectspythoc_test>python test6.py
Starting Standard Python speed test...
Result: 102334155
Time taken: 15.1611 seconds

Now for the PythoC-based code. Again, as with the print statement in our earlier example, we are able to’t just use the regular import timing directive from Python for our timings. As a substitute, we've got to borrow the usual timing function directly from the C programming language: clock(). We define this in the identical way because the printf statement we used earlier.

Here is the updated PythoC script with the C timer in-built.

from pythoc import compile, i32, ptr, i8, extern

# 1. Import C's printf
@extern
def printf(fmt: ptr[i8], *args) -> i32:
    pass

# 2. Import C's clock function
@extern
def clock() -> i32:
    pass

@compile
def fib(n: i32) -> i32:
    if n <= 1:
        return n
    return fib(n - 1) + fib(n - 2)

@compile
def main() -> i32:
    printf("Starting PythoC speed test...n")
    
    # Get the beginning time (this counts in "ticks")
    start_time = clock()
    
    # Run the heavy calculation
    result = fib(40)
    
    # Get the top time
    end_time = clock()
    
    # Calculate the difference. 
    # Note: On Windows, 1 clock tick = 1 millisecond.
    elapsed_ms = end_time - start_time
    
    printf("Result: %dn", result)
    printf("Time taken: %d millisecondsn", elapsed_ms)
    
    return 0

if __name__ == "__main__":
    from pythoc import compile_to_executable
    compile_to_executable()

My output this time was,

(pythoc_test) C:Usersthomaprojectspythoc_test>python test7.py
Successfully compiled to executable: buildtest7.exe
Linked 1 object file(s)

(pythoc_test) C:Usersthomaprojectspythoc_test>buildtest7.exe
Starting PythoC speed test...
Result: 102334155
Time taken: 308 milliseconds

And on this small example, although the code is barely more complex, we see the true advantage of using compiled languages like C. Our executable was a whopping 40x faster than the equivalent Python code. Not too shabby.

Who's PythoC for?

I see three fundamental sorts of users for PythoC.

1/ As we saw in our Fibonacci speed test, standard Python might be slow when doing heavy mathematical lifting. PythoC might be useful for any Python developer constructing physics simulations, complex algorithms, or custom data-processing pipelines who has hit a performance wall.

2/ Programmers who work closely with computer hardware (like constructing game engines, writing drivers, or programming small IoT devices) normally write in C because they should manage computer memory manually.

PythoC could appeal to those developers since it offers the identical manual memory control (using pointers and native types), nevertheless it lets them use Python as a “metaprogramming” engine to jot down cleaner, more flexible code before it gets compiled right down to the hardware level.

3/ In the event you write a helpful Python script and wish to share it with a coworker, that coworker normally needs to put in Python, arrange a virtual environment, and download your dependencies. It might be a hassle, particularly if the goal user isn't very IT-literate. With PythoC, though, once you will have your compiled C executable, anyone can run it just by double-clicking on the file.

And who it’s not for

The flip side of the above is that PythoC might be not one of the best tool for an online developer, as performance bottlenecks there are frequently network or database speeds, not CPU calculation speeds.

Likewise, should you are already a user of optimised libraries similar to NumPy, you won’t see many advantages either.

Summary

This text introduced to you the relatively latest and unknown PythoC library. With it, you should use Python to create super-fast stand-alone C executable code.

I gave several examples of using Python and the PythoC library to supply C executable programs, including one which showed an incredible speedup when running the executable produced by the PythoC library in comparison with a typical Python program. 

One issue you’ll run into is that Python imports aren’t supported in PythoC programs, but I also showed tips on how to work around this by replacing them with equivalent C built-ins.

Finally, I discussed who I believed were the sorts of Python programmers who might see a profit in using PythonC of their workloads, and those that wouldn’t. 

I hope this has whetted your appetite for seeing what sorts of use cases you possibly can leverage PythoC for. You'll be able to learn far more about this handy library by testing the GitHub repo at the next link.

https://github.com/1flei/PythoC