10 reasons why is Python language of choice visit us @ www.a-leap.com
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While each language available today offers its own advantages to Data Scientists, Python does stand apart from the rest due to the sheer expanse of its libraries and the functionality that they provide.
The discussion on the choice of language can be a never-ending one. Nevertheless, I will list down some points which according to me, make Python a very suitable choice for Data Scientists. So here we go:
Multi-Purpose
Python is multi purpose programming language. It can do a lot of things like - Object oriented programming, scripting, functional programming, interface with C Libraries and so on. It is the Swiss Army Knife of coding!
Rapid Proto-typing
Python is easy to learn and the syntax can be grasped quickly. This is a huge advantage for someone (Read Mathematician / statistician) who wants to quickly design a solution and who isn’t really a software engineer.
Great Support
Python has great developer support, good community & constant upgradation / development. The libraries are well maintained and any query / error that you may have is addressed quickly.
Closely imitates Matlab
The python libraries Numpy and Matplotlib have been heavily influenced by Matlab style operations. This makes it easier for anyone having extensive experience with Matlab to quickly shift and adapt to Python for doing the same tasks.
Matplotlib / Seaborn Libraries
You can design some cool visualizations using Python. There are a growing number of APIs which can make interesting visualizations in Python and give R visualizations a run for their money.
Data Analysis Library - Pandas
Pandas Library is one of the most used data analysis weapon in the repository of a Python Data scientist. The library provides an extremely efficient data structure in the form of Data Frame which can be used to load data from a variety of sources - text, excel, json, csv etc. The huge number of analytical operations that can be performed on Pandas Data Frame make it a popular choice among data scientist for Data Analysis.
Scikit-Learn Library
The scikit-learn Machine learning library of Python provides an easy interface and a simple syntax to implement a wide variety of ML algorithms. The library is well documented and makes the process of creating a model fairly straight-forward.
Free and open source
Python has always been free to download, use and distribute. You never have to pay a license fee to use Python.
Web Services & API
A data scientist might want to make available their models on the web using REST API. Python provides for an easy and efficient way to create web services / API using Flask.
Ability to build production-ready systems
Python has been known for its stability and scalability. It is a good choice of language when you want to build a stable, secure, scalable & maintainable system.
All of the above reasons and a whole lot of other external factors has helped Python gain a good reputation among the Data Scientists. With more & more Data Scientists joining the Python Bandwagon, I think Python is poised to continue its spectacular growth in the coming years.
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