Introduction to Python Compilers
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 among the various fields like data analytics and visualization, artificial intelligence and machine learning, automation. Python is also used in the development of Web apps, Mobile applications, and desktop apps, Python can be formally defined as an interpreted high-level programming language.
Thus being an interpreted language python improves compilation rate as the time for compiling the source code to machine code is not required, also the code syntax is generally very short and to the point thus helping reduce the code volume without compromising on the features in offer. Python was created in 1991 by Guido Van Rossum.
Python gained popularity owing to the fact that it offered an opportunity to implement multiple paradigms like object-oriented programming, procedural programming, and functional programming, etc. Python being a high-level language provides the user with the advantage of not having to bother about complex tasks like memory management, also it is cross-platform and has a large ecosystem of libraries. The simple, beginner-friendly syntax also is a reason for the popularity. The primary applications in the present-day world for python are found in web development, Data science, and machine learning, data analysis, and visualization.
What is Python Compiler?
- A compiler is a program used to convert high-level programming language into the lower-level language which can be understood by the assembly and interpreted into logical inputs. Python is miscategorized as an interpreted language as it is having an array of different implementation versions like CPython, Pypy, Iron Python.
- Among these CPython being a standard version. This CPython is an interpreted language and compiler independent but follows conversion to bytecode thus leading to the misconception that python has interpreted. These interpreted codes are not understandable for the CPU unit and thus requires an interpreter which is generally Python virtual machine. Python Virtual machine converts bytecode into machine code.
Types of Python Compilers
Let us look at different types of Python Compilers:
2) Jython or Jpython.
5) PyJS (it also is formerly known as Pyjamas).
7) Stackless Python.
Let’s see the significance of the individual Compilers of Python in details-
- 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 its processes faster in a C based code. Also, python is a dynamic programming language as it allocates the resources on the go not considering future consequences.
- However, when the same code is defined for CPython based compiler systems the type definition is taken into consideration. The steps of compilation are as follows:- Decoding, Tokenizing, Parsing, AST(Abstract Syntax Tree), Compiling.
2) Jython or Jpython
- Jython is an implementation that has been designed for the seamless integration of Python code over Java virtual machine, the advantage of this integration is that it provides an opportunity for amalgamation of a popular scripting language like python to 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 any 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.
- 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 in Github. To be used for scripting this has a requisite of 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 which is available in the dot net framework as an application for writing the dynamic language. The iron python interprets Python code to in-memory bytecode before execution. The primary aim behind the design of IronPython as an implementation of the dot net framework was to utilize the full potential of the vast User interface libraries available for the dot net framework.
- ActivePython is a commercial version of Python scripting platform designed and developed by the Open source organization called ActiveState. It provides Python bundles along with some additional packages.
- The biggest advantage associated with ActivePython is that it supports installation on many platforms even some of which are not supported by Python source code such as the AIX platform. Although it technically similar to python in terms of all its operation yet it is distinguished based upon its compatibility.
5) PyJS also is formerly known as Pyjamas
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 in it.
- It is an advanced version of programming language interpreter used by the developers in multithreaded programming without compromising on performance complexity and performance issues faced by the conventional multithreaded programming languages.
- The interpreter gets its name from the fact that it does not engage C call stacks but rather frees the same during function calls. Resulting in something called a micro thread approach.
- This is an extremely popular implementation as an alternative for traditional python owing to its fast and compliant nature. PyPy uses Just in time compiler which is a runtime compiler proficient for the dynamic compilation of the source code. 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 scope for micro thread optimization and concurrency.
Thus from the points above, we have come to the understanding of the use of python compilers and interpreters and their different types. We have realized their advantages and uses. Python being a developing language gives huge scope for many such implementations.
This has been a guide to Python Compilers. Here we discuss the basic concept, with different types of compilers in a concise manner. You can also go through our other suggested articles to learn more –