Python Compilers

Introduction to Python Compilers

In the following article, python compilers provide an overview of the top 7 Compiler of Python. Python is the world’s fastest-growing programming Language and is highly popular in various fields like data analytics and visualization, artificial intelligence and machine learning, and automation. Python is also used to develop Web apps, Mobile applications, and desktop apps.

Python was created in 1991 by Guido Van Rossum.

Python gained popularity because it offered an opportunity to implement multiple paradigms like object-oriented programming, procedural programming, functional programming, etc. Python is a high-level language providing the user with the advantage of not having to bother about complex tasks like memory management; it is cross-platform and has a large ecosystem of libraries. The simple syntax is a reason for its popularity. The primary applications are web development, Data science, machine learning, data analysis, and visualization.

What is Python Compiler?

A compiler is a program that converts high-level programming language into lower-level language, which can be understood by the assembly and interpreted into logical inputs. Python is miscategorized as an interpreted language as it has various implementation versions like CPython, Pypy, and Iron Python.
CPython is a standard version. It follows conversion to bytecode, leading to the misconception that python has interpreted. These interpreted codes are not understandable for the CPU unit and require an interpreter. Python Virtual machine converts bytecode into machine code.

Types of Python Compilers

Let us look at different types of Python Compilers:

1) CPython.

2) Jython or Jpython.

3) IronPython.

4) ActivePython.

5) PyJS (also formerly known as Pyjamas).

6) Nuitka.

7) Stackless Python.

Let’s see the significance of the individual Compilers of Python in detail –

1) CPython

It is the most widely used interpreter in Python, developed in C and python; the bindings for the interpreter must be written in a foreign language other than Python. CPython uses a Global Interpreter Lock (GIL) on each process; thus, python bytecode for a single process is executed on a single thread.
This interpreter is not suitable for CPU-intensive algorithms. CPython finds its use as many libraries are C optimized, i.e., many libraries will run their processes faster in a C-based code. Also, Python is a dynamic programming language as it allocates resources on the go, not considering future consequences.
However, when the same code is defined for CPython-based compiler systems, the type definition is considered. The compilation steps are:- Decoding, Tokenizing, Parsing, AST(Abstract Syntax Tree), and Compiling.

2) Jython or Jpython

Jython is an implementation designed to integrate Python code over a Java virtual machine seamlessly; this integration provides an opportunity to amalgamate a popular scripting language, python, into a vast library of the Java virtual machine. Jython compiles files to .class extensions.
The Jython programs can inherit and run any Java class and compile the code to bytecode. Along with this, Jython can be used to implement Java-based packages, especially desirable for creating solutions using Servlets, Swing, SWT, and AWT packages. Jython was created in 1997 by Jim Hugunin. Jython uses the Global interpreter lock (GIL) like CPython.

3) IronPython

Iron Python is a python implementation designed with the target of the dot net framework. This project is maintained presently by a small community of users on Github. To be used for scripting requires installing the Python tools for visual studio, which is available as an extension for visual studio IDE. The full implementation is written in C#.
Iron Python uses the Dynamic language runtime framework, available in the dot net framework, to write the dynamic language. The iron python interprets Python code to in-memory bytecode before execution. The primary aim behind the design of IronPython is to implement the dot net framework to utilize the full potential of the vast User interface libraries available for the dot net framework.

4) ActivePython

ActivePython is a commercial version of the Python scripting platform designed and developed by the Open source organization ActiveState. It provides Python bundles along with some additional packages.
The most significant advantage associated with ActivePython is that it supports installation on many platforms, even those not supported by Python source code, such as the AIX platform. Although it is technically similar to python in all its operations, it is distinguished based on its compatibility.

5) PyJS also is formerly known as Pyjamas

PyJs is a rich internet application mainly used to develop client-side web and desktop applications using Python scripting. The PyJs has a compiler that translates Python to JavaScript and is primarily designed over the Ajax framework.

6) Nuitka

Nuitka is an ideal example of a source-to-source compiler. The compiler allows the user to feed python codes and produce C/C++ extensions even if the computer has no python version installed.

7) Stackless

It is an advanced version of the programming language interpreter used by the developers in multithreaded programming without compromising performance complexity and performance issues faced by the conventional multithreaded programming languages.
The interpreter gets its name because it does not engage C call stacks but rather frees the same during function calls. It results in something called a micro thread approach.

8) PyPy

This is a prevalent implementation as an alternative to traditional python owing to its fast and compliant nature. PyPy uses the Just in time compiler, a runtime compiler proficient for the source code’s dynamic compilation. The space occupied by PyPy codes is smaller in terms of memory requirements.
PyPy is highly compatible with python source code and even allows implementations of the web development frameworks like Django and twisted. It also has additional product advantages from Stackless and provides micro thread optimization and concurrency scope.

Conclusion

Thus, from the points above, we have understood the use of python compilers and interpreters and their different types. We have realized their advantages and benefits. Python being a developing language gives vast scope for many such implementations.

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