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Asked by Veerendra Last Modified
Answer Python is an excellent solution for data science. Being released in 1989, it has been around for a long time and contains OOP. There are excellent IDE editors, vast ecosystem, and superb libraries.
It would be wrong to consider one of R and Python as a better one. These both are excellent but should be used in different cases.
Python has definite advantages for data science:
For collecting data, there are Feather, Ibis, ParaText, and bcolz.
For data visualization: Altair, Bokeh, and Geoplotlib;
For cleaning and transforming Data: Blaze, xarray and Dask;
For modeling: Keras and PyMC3.
Python is a great general programming language, with many libraries dedicated to data science.
It is the transition language for many ETL and Machine Learning workflows.
Python is more comfortable to deploy, integrate, and scale.
But the reality is that both languages are valuable and irreplaceable.
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