This article was published as a part of the Data Science Blogathon.
Python is one of the most famous programming languages. Python is an interpreted programming language and has different execution environments. It has a wide range of compilers to execute the python programs eg. PyCharm, PyDev, Jupyter Notebook, Visual Studio Code, and many more. The compiler is a special program that is written in a specific programming language to convert the human-readable language i.e. high-level language to machine-readable language i.e. low-level language.
Image Source
So in this blog, I am going to cover my personal favorite top 6 python compilers that are useful for Python developers and data scientists. So let’s get started!
If you want to check more articles, click here
Image Source
Here is a wide range of compilers to execute the python programs.
It is created by Jet Brains and it is one of the best and broadly utilized Integrated Development Environment (IDE). Developers utilize this IDE for creating gainful Python and creates perfect and viable code. The PyCharm IDE assists engineers with making greater profitability and gives savvy help to the developers. It helps developers to write good quality code correctly. It saves the developers time by performing the fast compilation.
Image Source
Features of PyCharm
Pros
Cons
Check the official page here: PyCharm
It is another open-source IDE that can be utilized as a python compiler for python code advancement. The principle justification for building up this IDE is to give a huge scientific environment to python, which researchers and developers use. It includes features such as editing, debugging and has an API and plugin system. It is a blend of different Python stacks like NumPy, pandas, IPython, and so on, and is delivered under the MIT permit.
Image Source
Features
Pros
Cons
Check the official page here: Spyder
This IDE is developed by Microsoft in 2015. It is free and open-source. It is lightweight and very powerful. It provides features such as unit testing, debugging, fast code completion, and more. It has a large number of extensions for different uses, for example, if you want to use C++, then install C++ extension, similarly install the different extension for different programming languages.
Image Source
Features
Pros
Cons
Check the official page here: Visual Studio Code
PyDev is an IDE for Eclipse and is utilized in the advancement of Python, JPython, and IronPython. It is presently the best Python environment for coding. For as long as 8 years, the community is dealing with it to improve it for Python clients.
PyDev is free and open-source, people can introduce it from the web and begin utilizing it. It is perhaps the most usable IDE and liked by a large portion of developers.
Image Source
Features
Pros
Cons
Check the official page here: PyDev
It is one of the most widely used python IDE for data science and machine learning environments. It is an open-source and web-based interactive environment. It permits us to create and share documents that have mathematical equations, plots, visuals, live code, and readable text. It supports many languages such as Python, R, Julia, etc but it is mostly used for Python.
Image Source
Features
Pros
Cons
Check the official page here: Jupyter Notebook
Sublime Text is an IDE that comes in two renditions for example free and paid. The paid variant contains additional highlights features. It has different plugins and is kept up under free software licenses. It upholds numerous other programming languages, for instance, Java, C/C++, and so on not just Python.
Sublime Text is very quick when contrasted with other text compilers. One can likewise introduce different bundles like debugger, code linting, and code completion.
Image Source
Features
Pros
Cons
Check the official page here: Sublime Text
A. Python is an interpreted language, and it does not require compilation like traditional compiled languages. However, popular Python interpreters include CPython (the reference implementation), PyPy (a just-in-time compiler), and Anaconda (a distribution that includes the conda package manager and various scientific computing libraries).
A. In the context of Python, a compiler is a software tool that translates Python code written in high-level human-readable form into low-level machine code or bytecode that can be executed directly by a computer. The compiled code is typically more efficient and faster to execute than the original Python source code. Python compilers can optimize the code, perform static type checking, and generate standalone executable files or bytecode files that can be run on a specific platform or within a virtual machine. Examples of Python compilers include Cython, Nuitka, and Shed Skin.
So in this article, we have covered the top 6 Python Compilers For Data Scientists in 2021. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!
You can check my articles here: Articles
Email id: gakshay1210@gmail.com
Connect me on LinkedIn: LinkedIn
The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.
Disparity between title and content . Title says compiler but talks of IDEs. So please change the title . Misleading
the website is very good Great article.
This is laughable. IDEs are called compilers