Write C Code Without Learning C: The Magic of PythoC

an attention-grabbing library the opposite day that I hadn’t heard of earlier than. 

PythoC is a Area-Particular Language (DSL) compiler that enables builders to jot down C packages utilizing normal Python syntax. It takes a statically-typed subset of Python code and compiles it immediately all the way down to native machine code through LLVM IR (Low Stage Digital Machine Intermediate Illustration).

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

A core design philosophy of PythoC is: C-equivalent runtime + Python-powered compile-time, and it has the next virtually distinctive promoting factors.

1. Creates Standalone Native Executables

In contrast to instruments equivalent to Cython, that are primarily used to create C-extensions to hurry up present Python scripts, PythoC can generate utterly unbiased, standalone C-style executables. As soon as compiled, the ensuing binary doesn’t require the Python interpreter or a rubbish collector to run.

2. Has Low-Stage Management with Python Syntax

PythoC mirrors C’s capabilities however wraps them in Python’s cleaner syntax. To realize this, it makes use of machine-native kind hints as a substitute of Python’s normal dynamic varieties.

• Primitives: i32, i8, f64, and so forth.
• Reminiscence buildings: Pointers (ptr[T]), arrays (array[T, N]), and structs (created by adorning normal Python lessons).
• Guide Reminiscence Administration: As a result of it doesn’t use a rubbish collector by default, reminiscence administration is express, similar to in C. Nonetheless, it provides trendy, optionally available security checks, equivalent to linear varieties (which be certain that each allocation is explicitly deallocated to stop leaks) and refinement varieties (to implement compile-time validation checks).

Python as a Metaprogramming Engine

Considered one of PythoC’s strongest options is its dealing with of the compilation step. As a result of the compile-time atmosphere is simply Python, you should use normal Python logic to generate, manipulate, and specialise your PythoC code earlier than it will get compiled all the way down to LLVM. This provides you extremely versatile compile-time code-generation capabilities (just like C++ templates however pushed by pure Python).

It sounds promising, however does the fact dwell as much as the hype? Okay, let’s see this library in motion. Putting in it’s straightforward, like most Python libraries its only a pip set up like this:

pip set up pythoc

Nevertheless it’s in all probability higher to arrange a correct improvement atmosphere the place you may silo your totally different tasks. In my instance, I’m utilizing the UV utility, however use whichever methodology you’re most comfy with. Kind within the following instructions into your command line terminal.

C:Usersthomaprojects> cd tasks
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 set up pythoc

A Easy Instance

To make use of PythoC, you outline features utilizing particular machine varieties and mark them with PythoC’s compile decorator. There are two predominant methods to run your PythoC code. You may name the compiled library immediately 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 predominant() -> i32:
    return add(10, 20)

# Name the compiled dynamic library from Python immediately
consequence = predominant()
print(consequence)

Then run it like this.

(pythoc_test) C:Usersthomaprojectspythoc_test>python test1.py

30

Or you may create a standalone executable that you would be able to run independently from Python. To try 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 predominant() -> i32:
    return add(10, 20)

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

We run it the identical method. 

(pythoc_test) C:Usersthomaprojectspythoc_test>python test4.py

Efficiently 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 listing beneath your present listing, then creates an executable file there that you would be able to run.

(pythoc_test) C:Usersthomaprojectspythoc_test>dir buildtest4*
 Quantity in drive C is Home windows
 Quantity Serial Quantity is EEB4-E9CA

 Listing 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 simply as we’d another executable.

(pythoc_test) C:Usersthomaprojectspythoc_test>buildtest4.exe

(pythoc_test) C:Usersthomaprojectspythoc_test>

However wait a second. In our Python code, we explicitly requested to print the addition consequence, however we don’t see any output. What’s occurring?

The reply is that the built-in Python print() operate depends on the Python interpreter working within the background to determine the best way to show objects. As a result of PythoC strips all of that away to construct a tiny, blazing-fast native executable, the print assertion will get stripped out.

To print to the display screen in a local binary, it’s important to use the usual C library operate: 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 move the variable you wish to insert into that placeholder.

Right here is the way you replace our code to import the C printf operate and use it accurately:

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

# 1. Inform PythoC to hyperlink to the usual C printf operate
@extern
def printf(fmt: ptr[i8], *args) -> i32:
    move

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

@compile
def predominant() -> i32:
    consequence = add(10, 20)
    
    # 2. Use printf with a C-style format string. 
    # %d is the placeholder for our integer (consequence).
    # n provides a brand new line on the finish.
   
    
    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 ensuing executable, our output turns into what we anticipated.

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

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

Is it actually well worth the hassle, although?

All of the issues we’ve talked about will solely be price it if we see actual pace enhancements in our code. So, for our remaining instance, let’s see how briskly our compiled packages might be in comparison with the equal in Python, and that ought to reply our query definitively.

First, the common Python code. We’ll use a recursive Fibonacci calculation to simulate a long-running course of. Let’s calculate the fortieth Fibonacci quantity.

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("Beginning Normal Python pace check...")
    
    start_time = time.time()
    
    # fib(38) often takes round 10 seconds in Python, 
    # relying in your pc's CPU.
    consequence = fib(40) 
    
    end_time = time.time()
    
    print(f"Consequence: {consequence}")
    print(f"Time taken: {end_time - start_time:.4f} seconds")

I acquired this consequence when working the above code.

(pythoc_test) C:Usersthomaprojectspythoc_test>python test6.py
Beginning Normal Python pace check...
Consequence: 102334155
Time taken: 15.1611 seconds

Now for the PythoC-based code. Once more, as with the print assertion in our earlier instance, we are able to’t simply use the common import timing directive from Python for our timings. As a substitute, we now have to borrow the usual timing operate immediately from the C programming language: clock(). We outline this in the identical method because the printf assertion we used earlier.

Right here is the up to date 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:
    move

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

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

@compile
def predominant() -> i32:
    printf("Beginning PythoC pace check...n")
    
    # Get the beginning time (this counts in "ticks")
    start_time = clock()
    
    # Run the heavy calculation
    consequence = fib(40)
    
    # Get the top time
    end_time = clock()
    
    # Calculate the distinction. 
    # Notice: On Home windows, 1 clock tick = 1 millisecond.
    elapsed_ms = end_time - start_time
    
    printf("Consequence: %dn", consequence)
    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
Efficiently compiled to executable: buildtest7.exe
Linked 1 object file(s)

(pythoc_test) C:Usersthomaprojectspythoc_test>buildtest7.exe
Beginning PythoC pace check...
Consequence: 102334155
Time taken: 308 milliseconds

And on this small instance, though the code is barely extra complicated, we see the actual benefit of utilizing compiled languages like C. Our executable was a whopping 40x sooner than the equal Python code. Not too shabby.

Who’s PythoC for?

I see three predominant varieties of customers for PythoC.

1/ As we noticed in our Fibonacci pace check, normal Python might be gradual when doing heavy mathematical lifting. PythoC might be helpful for any Python developer constructing physics simulations, complicated algorithms, or customized data-processing pipelines who has hit a efficiency wall.

2/ Programmers who work intently with pc {hardware} (like constructing sport engines, writing drivers, or programming small IoT units) often write in C as a result of they should handle pc reminiscence manually.

PythoC may enchantment to those builders as a result of it provides the identical guide reminiscence management (utilizing pointers and native varieties), however it lets them use Python as a “metaprogramming” engine to jot down cleaner, extra versatile code earlier than it will get compiled all the way down to the {hardware} stage.

3/ When you write a useful Python script and wish to share it with a coworker, that coworker often wants to put in Python, arrange a digital atmosphere, and obtain your dependencies. It may be a problem, notably if the goal person just isn’t very IT-literate. With PythoC, although, upon getting your compiled C executable, anybody can run it simply by double-clicking on the file.

And who it’s not for

The flip aspect of the above is that PythoC might be not the perfect software for an online developer, as efficiency bottlenecks there are often community or database speeds, not CPU calculation speeds.

Likewise, if you’re already a person of optimised libraries equivalent to NumPy, you gained’t see many advantages both.

Abstract

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

I gave a number of examples of utilizing Python and the PythoC library to supply C executable packages, together with one which confirmed an unimaginable speedup when working the executable produced by the PythoC library in comparison with a normal Python program. 

One challenge you’ll run into is that Python imports aren’t supported in PythoC packages, however I additionally confirmed the best way to work round this by changing them with equal C built-ins.

Lastly, I mentioned who I assumed had been the sorts of Python programmers who would possibly see a profit in utilizing PythonC of their workloads, and people who wouldn’t. 

I hope this has whetted your urge for food for seeing what sorts of use circumstances you may leverage PythoC for. You may be taught way more about this handy library by trying out the GitHub repo on the following hyperlink.

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