Python Extensions!

9 min readMar 1, 2023

Introduction

Python is a high-level programming language that is known for its simplicity, readability, and ease of use. However, it is an interpreted language, which means that it can be slower than compiled languages like C and C++. This is where Python extensions come in. Python extensions allow programmers to write high-performance code in languages like C and C++, and then use it in Python programs.

There are several types of Python extensions, including C extensions, C++ extensions, and Cython extensions.

Python C extensions

Python C extensions are modules that are written in C and can be loaded and used in Python programs. They are typically used for tasks that require high-performance, such as numerical computations, image processing, and scientific simulations. Python C extensions can also be used for low-level tasks like operating system programming and network programming.

There are several advantages of using Python C extensions. First, they can significantly improve the performance of Python programs. This is because C is a compiled language, which means that it can be much faster than interpreted languages like Python. Second, Python C extensions can access low-level system resources that are not available in Python. This makes them ideal for tasks like device driver development and network programming. Finally, Python C extensions are easy to use. Once a C extension is written, it can be imported into Python just like any other module.

To write a Python C extension, you will need to have a good understanding of C programming. You will also need to be familiar with the Python C API, which provides a set of functions and macros for interacting with Python objects and the Python interpreter.

Writing a Python C extension typically involves the following steps:

Writing the C code: This involves writing the C code that performs the desired task. This code will typically use the Python C API to interact with Python objects and the Python interpreter.

Building the extension: Once the C code has been written, it must be compiled into a dynamic library that can be loaded into Python. This is typically done using a build system like setuptools or distutils.

Testing the extension: Once the extension has been built, it should be tested to ensure that it works as expected. This can be done using automated tests or by manually testing the extension in a Python program.

Examples:

Let’s consider an example use-case where we need to write a Python C extension to compute the sum of two large integers represented as Python strings.

To do this, we can write a C function that takes two Python string objects as input, converts them to C strings, performs the addition, and returns the result as a new Python string object. Here is an example implementation:

#include <Python.h>

static PyObject *sum(PyObject *self, PyObject *args) {
    const char *str1;
    const char *str2;

    // Parse the input arguments as Python strings
    if (!PyArg_ParseTuple(args, "ss", &str1, &str2)) {
        return NULL;
    }

    // Convert the input strings to integers
    long long a = atoll(str1);
    long long b = atoll(str2);

    // Perform the addition
    long long c = a + b;

    // Convert the result to a Python string
    return Py_BuildValue("s", c);
}

static PyMethodDef module_methods[] = {
    {"sum", (PyCFunction) sum, METH_VARARGS, "Compute the sum of two integers represented as strings"},
    {NULL, NULL, 0, NULL}
};

static struct PyModuleDef module_def = {
    PyModuleDef_HEAD_INIT,
    "sum_module",
    "A Python C extension module that computes the sum of two integers represented as strings",
    -1,
    module_methods
};

PyMODINIT_FUNC PyInit_sum_module(void) {
    PyObject *module = PyModule_Create(&module_def);
    if (module == NULL) {
        return NULL;
    }
    return module;
}

In this example code, the sum() function takes two input arguments as Python strings, which are parsed using the PyArg_ParseTuple() function. The atoll() function is used to convert the input strings to integers, which are then added together. Finally, the result is converted back to a Python string using the Py_BuildValue() function and returned as the result of the function.

To build and install this extension, we can use the following commands:

$ gcc -fPIC -I/usr/include/python3.8 -c sum.c -o sum.o
$ gcc -shared sum.o -o sum.so
$ python3 -c "import sum_module; print(sum_module.sum('12345678901234567890', '9876543210987654321'))"

This will build the extension as a shared library, which can be loaded into Python using the import statement. The sum() function can then be called with two input strings, and the result will be printed to the console.

Note that this is just a simple example of how to write a Python C extension. Real-world extensions will typically be much more complex, and will often need to interact with low-level system resources or other external libraries. Nevertheless, this example should give you a good starting point for writing your own Python C extensions.

Let’s consider another example use-case where we need to write a Python C extension to compute the nth Fibonacci number.

To do this, we can write a C function that takes an integer n as input, and returns the nth Fibonacci number as a Python integer object. Here is an example implementation:

#include <Python.h>

static PyObject *fib(PyObject *self, PyObject *args) {
    int n;

    // Parse the input arguments as a single integer
    if (!PyArg_ParseTuple(args, "i", &n)) {
        return NULL;
    }

    // Compute the nth Fibonacci number
    long long a = 0, b = 1, c;
    for (int i = 0; i < n; i++) {
        c = a + b;
        a = b;
        b = c;
    }

    // Convert the result to a Python integer object
    return PyLong_FromLongLong(a);
}

static PyMethodDef module_methods[] = {
    {"fib", (PyCFunction) fib, METH_VARARGS, "Compute the nth Fibonacci number"},
    {NULL, NULL, 0, NULL}
};

static struct PyModuleDef module_def = {
    PyModuleDef_HEAD_INIT,
    "fib_module",
    "A Python C extension module that computes the nth Fibonacci number",
    -1,
    module_methods
};

PyMODINIT_FUNC PyInit_fib_module(void) {
    PyObject *module = PyModule_Create(&module_def);
    if (module == NULL) {
        return NULL;
    }
    return module;
}

In this example code, the fib() function takes a single input argument as an integer, which is parsed using the PyArg_ParseTuple() function. The nth Fibonacci number is computed using a simple iterative algorithm, which computes each Fibonacci number as the sum of the previous two. The result is then converted to a Python integer object using the PyLong_FromLongLong() function and returned as the result of the function.

To build and install this extension, we can use the following commands:

$ gcc -fPIC -I/usr/include/python3.8 -c fib.c -o fib.o
$ gcc -shared fib.o -o fib.so
$ python3 -c "import fib_module; print(fib_module.fib(50))"

This will build the extension as a shared library, which can be loaded into Python using the import statement. The fib() function can then be called with a single integer argument, and the result will be printed to the console.

Note that this is just a simple example of how to write a Python C extension for computing Fibonacci numbers. Real-world extensions will typically be much more complex, and may need to use more sophisticated algorithms for computing Fibonacci numbers, such as matrix exponentiation. Nevertheless, this example should give you a good starting point for writing your own Python C extensions.

Other Extensions

C++ extensions, also known as C++ Python bindings, are similar to C extensions in that they are written in C++ and can be loaded and used in Python programs. However, they are typically used for tasks that require advanced C++ features like templates, exception handling, and operator overloading. C++ extensions can be created using tools like Boost.Python, PyBind11, and SWIG.

Boost Python: Boost Python is a library that provides a set of C++ tools for creating Python extensions. It is part of the Boost C++ libraries, which are a collection of libraries that extend the functionality of the C++ programming language. Boost.Python provides a rich set of C++ classes and functions for interacting with Python objects and the Python interpreter. It also provides support for advanced C++ features like templates, exception handling, and operator overloading. Boost.Python is a powerful and flexible tool for creating C++ extensions for Python, but it has a steep learning curve and can be challenging for beginners.

PyBind11: PyBind11 is a lightweight header-only library that provides a simple and intuitive way to create Python bindings for C++ code. It is designed to be easy to use and has a minimalistic syntax that is similar to Python. PyBind11 provides automatic conversion between C++ and Python data types, which makes it easy to interface with Python code. It also provides support for advanced C++ features like lambdas, templates, and STL containers. PyBind11 is a great choice for creating simple and easy-to-use C++ extensions for Python.

SWIG: SWIG stands for Simplified Wrapper and Interface Generator. It is a powerful and flexible tool for creating interfaces between C++ code and other programming languages, including Python. SWIG provides a language-agnostic interface definition language that can be used to generate wrappers for C++ code in multiple programming languages. SWIG can generate wrappers for both C and C++ code and provides support for advanced features like multiple inheritance and templates. SWIG has a steep learning curve and can be complex to use, but it is a powerful tool for creating complex C++ extensions for Python.

Cython extensions, on the other hand, are a hybrid between Python and C. Cython is a superset of Python that allows you to write Python code with C-like syntax. Cython code can be compiled into C code, which can then be compiled into a Python extension module. Cython extensions can be used to write high-performance Python code, and can also be used to interface with C and C++ libraries.

Python extensions are all powerful tools for Python programmers who need to write high-performance code or access low-level system resources. The choice of which type of extension to use will depend on the specific requirements of your project and your level of expertise in C, C++, and Cython.

Useful Resources:

Here are some useful resources on Python extensions:

Python documentation on extending and embedding Python: https://docs.python.org/3/extending/index.html
Python C API reference: https://docs.python.org/3/c-api/index.html
Python C extension tutorial: https://realpython.com/python-c-extension-module/
Boost.Python documentation: https://www.boost.org/doc/libs/1_75_0/libs/python/doc/html/index.html
PyBind11 documentation: https://pybind11.readthedocs.io/en/stable/
SWIG documentation: http://www.swig.org/Doc3.0/Python.html
Cython documentation: https://cython.readthedocs.io/en/latest/src/tutorial/cython_tutorial.html#extending-python-with-cython
ctypes documentation: https://docs.python.org/3/library/ctypes.html

These resources cover a wide range of topics related to Python extensions, including the Python C API, writing and building C extensions, using third-party libraries like Boost.Python and PyBind11, and using tools like SWIG, Cython, and ctypes to generate Python extensions from other languages.

Conclusion

Python extensions are an important tool for Python programmers who need to write high-performance code or access low-level system resources. They are easy to use and can significantly improve the performance of Python programs. If you are a seasoned Python programmer looking to take your skills to the next level, learning how to write Python C extensions is definitely worth considering.

Thank you for reading! I would love to hear from you and will do my best to respond promptly. Thank you again for your time, and have a great day! If you have any questions or feedback, please let us know in the comments below or email me.

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