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What makes Python a top choice in the Data Science community?

Python has become the most used programming language for data science practices. Developed by Guido van Rossum and launched in 1991, it is an interactive and object-oriented programming language similar to PERL or Ruby. Its inherent readability, simplicity, clean visual layout, less syntactic exceptions, greater string manipulation, ideal scripting, and rapid application, an apt fit for many platforms, make it so popular among data scientists. This programming language has a plethora of libraries (e.g., TensorFlow, Scipy, and Numpy); hence Python becomes easier to perform multiple additional tasks.

Python is an object-oriented, open-source, flexible, and easy to learn programming language.  According to a 2013 survey by industry analyst O’Reilly, 40% of data scientist respondents admitted using Python in their daily work. They join the many other programmers in all fields who have made Python one of the world’s top ten most popular programming languages ever since 2003. In fact, many surveys show it as the number one preferred language.

Why is it so Popular?

One of the main reasons why Python is widely used in the scientific and research communities is its ease of use and simple syntax that makes it easy to adapt for people without much programming or engineering background. It is also suitable for quick prototyping. Further, it allows the developer to run the code anywhere, like Windows, Mac OS X, UNIX, and Linux. And since it is a flexible programming language, it offers data scientists the facility to solve any given problem or carry projects concerning about developing machine learning models, web services, data mining, classification, etc., in less time frame than most of the programming languages. Python libraries Python Scrapy and BeautifulSoup can help to extract data from the internet, whereas Python Seaborn and Matplotlib help in data visualization or graphical representation. In data analytics helps with better insight, understanding patterns and correlates data from big datasets. Its libraries like Tensorflow, Keras, and Theano allow data scientists to develop deep learning models and Scikit-Learn helps to develop machine learning algorithms. It can also be leveraged in non-technical fields like business and advertising.

Besides, Python has a huge community base of engineers and data scientists like,,, etc., where Python developers can impart their issues and thoughts to the community at no cost. Also, Python has great compatibility with Hadoop, which is a renowned open-source big data platform.

Microsoft’s New Update

Microsoft has been a constant advocate of Python. It supports open-source Python in developer tools, including the Visual Studio integrated development environment (IDE), and hosts it in Azure Notebooks and uses it to build end-user experiences like the Azure command-line interface (CLI). Recently, Microsoft released a new update of its Visual Studio Code (VS Code) code editor for Windows, Windows on Arm, macOS, and Linux. In this, it launched a new version of the Python language extension for VS code editor that breaks out the Jupyter Notebooks functionality into a distinct VS Code extension. Jupyter is a free, open-source, interactive web tool, which researchers use to combine software code, computational output, explanatory text, and multimedia resources in a single document.  It draws its name from the programming languages Julia (Ju), Python (Py), and R. This means, it not only supports Python but also other popular data science languages like Julia and R.

Although Microsoft’s Python extension for VS Code has supported Jupyter Notebooks for a year now, the tech giant decided to break out Jupyter notebooks functionality to improve support for other data-science languages. Source