cython-3-2-4

name

cython-3-2-4

description

A skill for using Cython 3.2.4, an optimizing Python compiler that makes writing C extensions as easy as Python itself by translating Python code to C/C++ with support for calling C functions and declaring C types on variables and class attributes. Use when building high-performance Python extensions, wrapping C/C++ libraries, creating optimized numerical code with NumPy integration, implementing parallel algorithms with OpenMP, or needing fine-grained manual tuning from broad to low-level C optimizations.

Cython 3.2.4

Overview

Cython is a compiler that translates Python-like code into optimized C or C++ extensions. It supports the full Python language plus optional static typing, C-level function calls, and direct access to C library APIs. Code runs within the Python runtime environment but compiles to native machine code instead of interpreted bytecode.

Cython's two syntax variants — Cython syntax (.pyx files with cdef, cimport) and Pure Python mode (.py files with cython.* annotations and decorators) — let you start with plain Python and incrementally add performance optimizations where needed.

Key capabilities:

• Static type declarations for variables, function parameters, and return values
• Direct C/C++ function calls with zero Python overhead
• Typed memoryviews for efficient NumPy array access
• Fused types for generic programming (like C++ templates)
• OpenMP-based parallelism via prange
• GIL release for true multi-threaded execution
• Wrapping existing C and C++ libraries

When to Use

• Building high-performance Python extensions where loops and numerical operations dominate runtime
• Wrapping C or C++ libraries for use from Python
• Optimizing hot paths in existing Python code with incremental typing
• Creating typed NumPy operations that outperform pure NumPy vectorization
• Implementing parallel algorithms using OpenMP (prange)
• Needing fine-grained control from broad Python-level optimization down to manual C tuning

Core Concepts

Two compilation stages: A .pyx or .py file is first compiled by Cython into a .c or .cpp file, then compiled by a C/C++ compiler into a platform-specific shared library (.so on Linux, .pyd on Windows).

Two syntax variants:

• Cython syntax (.pyx): Uses cdef, cimport, cpdef keywords. Not valid Python.
• Pure Python mode (.py): Uses PEP-484 type annotations with cython.* types and @cython.* decorators. Valid Python that also compiles with Cython.

Three function types:

• def — Python-callable function, always goes through Python calling convention
• cdef / @cython.cfunc — C-only function, fastest calls within Cython code, not visible from Python
• cpdef / @cython.ccall — Hybrid: callable from both Python and C, uses fast C calling when called from Cython

Extension types: Declared with cdef class or @cython.cclass, these are like Python classes but with faster attribute access for cdef members. They compile to C structs.

Installation / Setup

Install Cython via pip:

pip install "Cython==3.2.4"

A C compiler is required (gcc, clang, or MSVC). For NumPy integration, also install numpy.

Basic setup.py for compiling a .pyx file:

# setup.py
from setuptools import setup, Extension
from Cython.Build import cythonize

setup(
    ext_modules=cythonize("example.pyx")
)

Build with:

python setup.py build_ext --inplace

For pyproject.toml builds (PEP 518):

# pyproject.toml
[build-system]
requires = ["setuptools", "Cython"]
build-backend = "setuptools.build_meta"

Command-line compilation:

cythonize -i example.pyx    # compile to C and build extension in-place
cython -a example.pyx       # generate annotated HTML showing Python/C boundaries

Usage Examples

Basic typed function (Cython syntax):

# math_ops.pyx
def fast_sum(int[:] arr):
    cdef double total = 0.0
    cdef int i
    for i in range(arr.shape[0]):
        total += arr[i]
    return total

Same function in Pure Python mode:

# math_ops.py
import cython

@cython.boundscheck(False)
@cython.wraparound(False)
def fast_sum(arr: cython.double[:]) -> cython.double:
    total: cython.double = 0.0
    i: cython.Py_ssize_t
    for i in range(arr.shape[0]):
        total += arr[i]
    return total

Wrapping a C library:

# lib_wrapper.pyx
cdef extern from "math.h":
    double sin(double x)
    double cos(double x)

def compute_angle(double x, double y):
    return atan2(y, x)

Advanced Topics

Language Basics: Data types, cdef/cpdef, structs, unions, enums, pointers, ctuples → Language Basics

Typed Memoryviews: Efficient NumPy array access without GIL, slicing, strides, C/Fortran contiguity → Typed Memoryviews

External C/C++ Code: Wrapping C libraries, cdef extern from, header files, verbatim C, public/API declarations → External C/C++ Code

C++ Support: cppclass, templates, STL containers, operator overloading, exception handling → Using C++ in Cython

Fused Types: Generic programming with type specializations, indexing, memoryview fused types → Fused Types

Parallelism and GIL: prange, OpenMP schedules, releasing the GIL, nogil functions, thread-local buffers → Parallelism and GIL

Compilation and Directives: setup.py patterns, distutils comments, compiler directives, annotations, shared utility modules → Compilation and Directives