Struggling to choose between Shed Skin and Cython? Both products offer unique advantages, making it a tough decision.
Shed Skin is a Development solution with tags like compiler, python, c, optimization, static-analysis.
It boasts features such as Compiles Python code to C++, Performs advanced static analysis and type inference, Generates optimized C++ code, Makes it easy to create Python extensions in C++, Can significantly improve execution time of CPU-bound Python programs and pros including Faster execution than pure Python, Easier than hand-writing C++ extensions, Seamless integration with existing Python code.
On the other hand, Cython is a Development product tagged with python, c, compiled, performance.
Its standout features include Allows writing C extensions for Python, Can call C functions and declare C types from Python code, Can compile Python code to C/C++ for improved performance, Supports calling Python from C code, Static typing for performance and efficiency, Can access C libraries directly from Python code, and it shines with pros like Great performance gains compared to pure Python, Easier and faster than writing extensions in C, Seamless interoperability between Python and C/C++ code, Can selectively optimize hotspots instead of entire codebase, Retains Python language features and compatibility.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
Shed Skin is an experimental compiler, Python to C++, that makes creating Python extensions easy. It uses advanced static analysis and type inference techniques to generate optimized C++ code. Shed Skin can significantly improve the execution time of CPU-bound Python programs.
Cython is a programming language that aims to be a superset of the Python language, while also being compilable to C/C++ code. It allows Python code to be compiled for speed and efficiency while retaining compatibility and interoperability with Python code.