The 10 Best Python Compilers for Developers
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Looking for various Python compilers used by developers to do all that magic with Python code? You are at the right place; this article covers the best compilers you can use to compile Python code to the desired output format.
Created by Guido van Rossum back in 1991, Python is the fastest-growing programming language of recent times. Python has applications in multiple areas including web and desktop development, but it is the #1 language of choice when it comes to machine learning, artificial intelligence, data analysis, and data visualization.
The Best Python Compilers
While official and the most widely used one is CPython, there are many others including Jython, Brython, PyPy, Skulpt, IronPython, PyJs, Nuitka, WinPython, and few others.
When you download Python from the official website and start playing around with it, you are dealing with CPython default.
Since this is the official one, when you download Python from the official website, you essentially download CPython to execute Python code on your machine.
Also, given that this is the original Python compiler, it happens to be the first one to get all the latest and greatest features of the Python language. In essence, this is the reference implementation of Python language specifications.
It is written purely in C programming language, hence the name. It is worth noting that you can call functions of C source code from Python code. This lets you utilize tons of existing C libraries directly in Python.
Also note that since CPython interprets the bytecode at run time, it uses a global interpreter lock (GIL) on each process which ensures that only one thread is interpreting the bytecode.
For all the students and beginners, this is the one to start your Python programming journey. Other compiler implementations of Python are more for specific use cases and to mix other programming languages with Python.
CPython vs. Other Compilers
While we're on the topic of CPython, the source code written in .py files is first compiled automatically to .pyc file which contains the Python bytecode and is then interpreted by the PVM (Python Virtual Machine).
So, it is a combination of compiler and interpreter, unlike C language where source code is directly compiled to the binary machine code as CPU instructions.
Other implementations of Python deal with the source code differently. Jython for example is the Python implementation that converts code to the Java bytecode to run on JVM.
This also means that you can mix both Java and Python code and utilize existing libraries of both Java and Python. We will talk about this a bit more.
Let's look at other various Python compilers.
After including brython.js, you can add Python scripts using script type
While CPython is the most widely used compiler for Python, it is not the fastest one. An alternative to default Python implementation is PyPy, which supports core language specifications of both Python 2 and Python 3.
Why PyPy for Python?
PyPy works on the just-in-time (JIT) compilation concept where code is compiled directly to machine code prior to the execution, which means faster execution.
In the case of CPython, bytecode is interpreted at run time which means performance hit. Experts claim you get almost 4x speed with PyPy when compared to CPython.
It is worth noting that PyPy speed advantage is at its best when you are dealing with long-running processes where Python code execution takes most of the time. For shorter processes, the JIT compiler itself might take more time nullifying the overall speed advantage.
Jython or JPython
Jython was originally known as JPython and is the second most used implementation of Python. It is the Java Virtual Machine (JVM) implementation of the Python programming language and is designed to mix Python with Java.
You can import existing Java libraries and packages into your Python programs. Another way round, you can embed Python scripts in your Java programs.
Either way, the Jython compiler will compile the entire code mix (Java & Python) to the bytecode that can run on JVM.
Since it runs on JVM, you can create Jython projects on almost all platforms like Windows, Linux, macOS, FreeBSD, Solaris, and others.
Before we continue, it is worth mentioning that Cython is different from CPython. Cython is more like a superset that lets you combine both C and Python in your code and generates C code as an output, which can further be compiled using any C/C++ compiler.
This gives you the speed of C and flexibility of Python and can be used as a powerful tool to write C extensions for Python, with ease of Python programming.
Important notes about Cython:
- Allows you to use static type declarations in Python code
- Debug mixed source code written in Cython, Python, and C
- Build performant applications using existing libraries like NumPy, SciPy, and others from the CPython ecosystem
In other words, using CPython gives you the speed of Bytecode, which is interpreted, but using Cython gives you the speed of native code, which is pre-compiled to machine code.
Skulpt is an in-browser implementation of Python. While the main purpose of Skulpt is to provide a good online Python compiler, it can also be used in your own web pages — including the Skulpt engine in the web app that lets you write Python scripts for front-end development.
Skulpt was created by Graham Scott as an experimental project but became popular soon after.
Nuitka is another useful Python compiler, and it is written completely in Python itself. It is developed and maintained by Kay Hayen and has started to get a lot of attention in the last few months. While still under heavy development, it claims to give run-time improvements over the default CPython implementation.
Nuitka works by compiling the Python code to C code and utilizes libpython for final execution. This, however, is planned to be replaced with pure C where native C data types would be used without accessing libpython.
Note that Nuitka for Python is free to use under Apache License and can be set up on Linux, Windows, macOS, and few other platforms.
IronPython is a Python implementation for .NET framework and supports both .NET core and .NET Standard. Like Jython is to Java, IronPython is to .NET Framework.
With IronPython you can use all Python libraries, .NET Framework, and all other .NET supported languages like C#.
IronPython was first released back in 2006 and can run on Windows, macOS, and Linux platforms. It is written in C# and available under Apache licenses as free to use the software.
If you are from the .NET background, you can easily set up VS code for Python by using the Python tools extension.
PyJS also has a desktop version that runs the same web version (feature-wise) of the application on the desktop.
WinPython is a ready-to-use distribution of Python that runs on Windows PC without any installation. I wouldn’t specifically call it a different Python implementation.
WinPython compiler for Python not only brings home a Python execution environment but comes packed with many Python libraries like Scipy, Numpy, and Pandas.
Note that WinPython is a full-featured scientific environment for data scientists and a handy tool for students and beginners.
It is quite evident that various compilers empower developers to mix and match multiple languages in their projects. Developers use these compilers for many reasons, including those listed below:
- The need to utilize existing libraries from other programming languages
- To speed up the Python runtime execution.
- And much more
I know many developers who use scientific Python libraries in Java projects with help of Jython implementation of Python. Do share your experience using one or more of these Python compilers with our readers, via comments.
Published at DZone with permission of Sophia Rodriguez. See the original article here.
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