Python is popular among students and beginners and favored for its easy syntax. You can use Python in many areas including machine learning, web development, artificial intelligence, data science, GUI development, scientific computing, and much more.
Before we get into details of Cython vs Python vs CPython, understand that Python is a programming language, there are standard specifications to write python code. CPython, on the other hand, is an implementation of Python specifications in C, it is the Python interpreter that understands Python code and translates it to the CPU.
Coming to Cython, it is a superset of Python and C, and brings in extended language specifications that enable developers to use Python and C syntax together in a single program.
Not only that, Cython compiles the combined code to very efficient C code which in turn can be compiled to the machine code using standard C/C++ compiler. We will get to more details in the following sections.
Also note that apart from Cython and CPython, there are many other implementations of Python including IronPython, Jython, Brython, Nuitka, and many more. You can check out our article on the complete list of best Python compilers.
This article covers key differences between Cython and Python and provide guidance and tips around when to uses what. You will also get to why Cython brings in all the performance boost over Python.
Cython vs Python vs CPython : Overview
CPython is the original implementation of Python, when you download and install python, you get CPython by default. CPython is what makes us call Python an interpreted language because it interprets the Python code for the CPU at run time.
It is worth noting that while Python is an interpreted programming language, it is first compiled to the bytecode by CPython before interpreting the bytecode to machine instructions.
Since compiling happens behind the scenes and is more like a temp phenomenon, you call Python an interpreted language. You always need to keep the source code for execution.
Cython on the other hand operates in a totally different manner. Its final intended outcome is the compiled machine code in the form of CPU instructions. You do not really need to keep the source code for run time execution.
Let us look at some of the key differences between Cython and Python –
Cython vs Python: Subtle Differences
Speed of Execution
Interpreters read the code line by line and convert the code to machine instructions, on the fly, to produce the program outcome. Compilers, on the other hand, compile the programs to very efficient machine code beforehand. This makes compiled code much faster than the interpreted code.
For the above reason, Python with its default implementation CPython is slow. On the other hand, Python with Cython can become as fast as C programs. You get the ease and simplicity of Python programming and execution speed of C by using Cython.
You can do a simple experiment to test it for yourself –
Write a simple Python program and save it as both .py and .pyx files separately. “.py” is the file type for Python programs and “.pyx” is the file type for Cython programs.
Execute the “.py” file with CPython which is default Python, and the .pyx file with Cython after compiling with C/C++ compiler. You will notice a good boost in the execution speed of Cython code. This, however, is the minimum you get with Cython.
To make the speed even better, you can make modifications in the code of .pyx file, and change its variables and assign data types, the way you typically do in C programs. If you execute the programs now, you will see a further speed boost.
In Python val = 9.2 In Cython (to take advantage of data types) cdef float val = 9.2
Above is a small modification you could do to your Python code to take speed advantage, there is a lot you can do to speed up your code. Check out official Cython documentation for more details.
When to use Cython instead of Python?
Python is a general-purpose, high-level, object-oriented programming language.
It is used by many large organizations in projects scaling from simple GUI apps to large scale web applications.
It is also a language of choice for beginners to learn programming, and for that reason, is widely used in universities, schools, and academies.
Large enterprises like Google, Facebook, Spotify, Quora, and Dropbox use Python in one or more of their projects.
On the other hand, Cython is still a growing project. Most of the developers are not using it heavily even though it brings in many benefits.
Having said that, Cython comes in very handy to write C extensions of Python, and core developers have a good grip on Cython. Many existing scientific and computing libraries like Pandas and SciPy are also written in Cython.
Conceptually speaking, Cython being a superset, you can do everything with Cython that you do in Python, but the opposite of this is not true.
All of this makes Cython the best option to develop Python modules using existing C libraries, existing python packages, as well as existing Cython libraries like Scipy.
Installation, Compatibility, and Interoperability
To use Cython, you need to have both Python as well as a standard C/C++ compiler installed on your machine. Standalone Python does not need anything more than the default installation. At the time of writing, Cython supports both Python 2 as well as Python 3 specifications.
From a reusability perspective, you can use Cython to create Python modules and use those modules in Python programs using import statements. Example: SciPy is a Cython module, that you use without even knowing it was written in Cython.
The major differences come during program writing, if you are using only Python, you have to stick to the standard Python language specifications, whereas in the case of CPython you can mix and match C, Python, and Cython, as required. So, all three are fully interoperable. You just need to be careful about the syntax requirements.
Syntax Differences between Cython and Python
There are some subtle differences you need to understand when developing in Cython. Since is the superset of Python, you can write regular Python code and compile using Cython compiler.
However, to take full advantage of Cython over Python, it is important to make few changes like adding type to the variable declarations.
Code Profiling for Performance
Performance is the key to Cython, else there probably was no need of having Cython vs Python debate. To make it easy for the developers, Cython provides hooks to use the existing cProfile module of Python to identify the performance bottlenecks in the code.
The best way to go about writing an absolute performant code with ease is to start of with standard Python, do the profiling, identify opportunities of performance boost and make spot modifications.
Cython brings in all the benefits of Python and provides the performance of compiled languages like C. It takes away most of the shortcomings of Python, which is labeled as being slow by many.
However, it is still not that widely used but is limited only to the professional and hard code programmers. 80% of the community of Python developers does not even tend to investigate what is Cython exactly. My best judgment is, most of them do not want to get into it for the simple reason that they do not care about performance, for the few others, they simply avoid getting into anything new.
For the handful few, they are doing their best to use Cython as well as promoting Cython too, I am one of them. Do share your views via comments. Happy coding!