Compiler for Python
May 25, 2021
Information Technology
By: Prabhjot

Compiler for Python – Which is the best in 2021

Introduction to a Compiler

A compiler is a computer program that translates code written in one programming language into another programming language. Since the computer only understands machine language, the compiler plays a major role in using a programming language.

There is no scarcity to Python compilers that can cater to varying project needs. In spite of the fact that CPython compiler (- cum-interpreter) is the true Python compiler as it has a place with the reference execution of Python, i.e., CPython, there are a few other Python compilers that developers like to use.

 

The best Compilers for Python are mentioned here in this blog; let’s have a look –

 

1. Brython

  • A short form for Browser Python, it supports Python 3 to 3.7 versions.
  • It is labeled as a ‘Python 3 implementation for client-side web programming.
  • It converts Python code into JavaScript code.
  • It is adapted to the HTML5 environment and comes with an interface to the DOM objects and events.
  • It is used in creating simple document elements and dragging and dropping to 3D navigation.
  • It has more compatibility with Firefox than Google Chrome.
  • The Python compiler comes with a JavaScript console that can be used for evaluating the execution time of some JS program compared to its equivalent Python in the editor.
  • Sometimes Brython is even speedier than the Python reference implementation, i.e., CPython.
  • Brython upholds the greater part of the syntax of Python 3, like comprehensions, generators, and imports.
  • It likewise offers help for a few modules having a place with the CPython distribution.
  • Support for the most recent specifications of HTML5/CSS3 is additionally accessible in Brython.
  • More importantly, it can use popular CSS frameworks like BootStrap3 and LESS.

 

2. Nuitka

  • Nuitka supports Python Version 2.6, 2.7, 3.3 to 3.7.
  • It is a source-to-source Python compiler.
  • Nuitka takes Python code and compiles it to C/C++ source code or executables.
  • It is used for developing standalone programs even when you are not running Python on your machine.
  • Written completely in Python, it supports several Python libraries and extension modules.
  • It is available for FreeBSD, Linux, macOS X, NetBSD, and Windows platforms.
  • It is also available with Anaconda for developing projects involving data science and machine learning.

 

3. PyJS

  • It supports all the Python versions upto 2.7.
  • PyJS is one of the go-to options for those who want to write and run Python code in a web browser.
  • The PyJS compiler translates Python code into equivalent JavaScript code to execute inside a web browser.
  • It accompanies an AJAX system that fills the gaps left between JS and DOM support accessible for various internet browsers.
  • To generate equivalent JS code, PyJS leverages Python’s abstract syntax tree.
  • It is possible to run a Python web application source code as a standalone desktop application using the PyJS Desktop module.
  • PyJS converts some of the Python data types to custom objects, such as lists.
  • PyJS is a lightweight application.
  • The PyJS compiler also offers runtime support for runtime errors.
  • JS engineers can plan and create applications in a pure object-oriented view utilizing PyJS.

 

4. Shed Skin

  • Shed Skin supports – Python 2.4 to 2.6 versions.
  • It converts a statically typed (in which the variables in use should only infer to a single data type) Python program into an equivalent, pure C++ program.
  • It doesn’t support using nested functions and defining functions that accept a varying number of arguments.
  • Shed Skin can’t scale beyond two or three thousand lines of code.
  • It can generate standalone programs or extension modules, which can be imported and used in large Python programs.
  • The biggest advantage of using Shed Skin is that it allows for a significant performance boost because the Python compiler has reimplemented the built-in Python data types into its own set of classes, implemented in efficient C++ code.

 

5. Skulpt

  • Skulpt supports up to Python 3.3 versions.
  • Written in JavaScript, Skulpt offers a certified climate where the compiled code is executed in JS structure.
  • Since it is an in-browser implementation of Python, there is no need for additional processing, plugins, or server-side support required to run Python in a web browser.
  • Skulpt is a good option to make a web application that allows users to run Python programs inside a web browser.
  • It can be easily embedded into an existing blog or webpage too.
  • For custom integration, Skulpt code can be added to the HTML.

 

6. Transcript

  • Supports Python 3 to 3.7
  • It permits assembling a genuinely broad subset of Python into a minimized, decipherable, and simple to troubleshoot JavaScript code.
  • It follows a simplistic and powerful syntax without requiring any additional extensions.
  • The lightweight Python compiler supports slicing with [i:j:k] matrix and vector operations with +, -, *, and / operators.
  • It helps in better team cooperation working on full-scale projects.
  • In addition to offering seamless access to any JS library, Transcrypt also runs on top of Node.js.
  • Thankfully it supports hierarchical modules, local classes, and multiple inheritances.

 

7. WinPython

  • It supports up to Python 3.7 versions.
  • WinPython is a Python distribution that is specifically created for the Windows operating system.
  • Since WinPython is an independent distribution for Python, you just need to download and unload it to begin.
  • WinPython likewise comes prepackaged with the absolute most mainstream Data Science and Machine Learning Python libraries, like NumPy, Pandas, and SciPy.
  • It is accessible in a zero bundle choice that accompanies just the Python compiler and that’s it.

 

Conclusion

That completes our list of the best 7 Python compilers. As each one of them is designed with explicit prerequisites in the center, you can utilize them for taking into account an alternate arrangement of necessities. In programming, the more a developer knows, the better it is. So, get started today!

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