What is the quickest way to call C++ from Python by constructing a Python binding to a C or C++ library? (I am using Windows, if that matters.)
One of the easiest ways to call C++ from Python is by using Boost.Python. Boost.Python is a library that provides seamless interoperability between Python and C++. It allows you to expose C++ classes, functions, and objects to Python with minimal changes to your existing C++ code. This makes it especially useful when you want to wrap third-party C++ libraries.
The library employs advanced metaprogramming techniques to simplify the process, essentially making the wrapping code look like a declarative interface definition. While it might feel a bit heavy for simpler use cases, its robustness makes it an excellent choice for complex projects where precision and functionality matter. If you’re new to call C++ from Python techniques, Boost.Python offers extensive documentation and community support to help you get started.
Another popular option to call C++ from Python is pybind11. It’s a lightweight, header-only library that’s considered a modern alternative to Boost.Python. Pybind11 focuses on simplicity and offers seamless integration with modern C++ (C++11/14/17), making it ideal for developers already working in that ecosystem.
One key benefit of pybind11 is its concise syntax, allowing you to write clean and manageable binding code without needing an extensive understanding of the library itself. It also supports advanced features like handling exceptions, managing smart pointers, and integrating NumPy arrays directly. If you’re working with a straightforward C++ library or need a lightweight solution to quickly call C++ from Python, pybind11 is a fantastic choice.
Cython is another excellent way to call C++ from Python. Unlike Boost.Python and pybind11, Cython is a programming language that extends Python with C-like performance capabilities. It allows you to write Python code that directly interfaces with C or C++ functions, enabling you to wrap C++ libraries and expose them to Python effectively.
What sets Cython apart is its ability to optimize Python code alongside binding C++ libraries, making it a double-edged tool for both performance tuning and interoperability. By compiling the Cython code into a shared library, you can efficiently expose your C++ functionality to Python with minimal runtime overhead. While Cython requires slightly more effort upfront, it’s an excellent option if you need fine-grained control and performance improvements in addition to bindings.