How Python became Language of Choice for Data Science

Python is a preferred platform for data science. Many companies had channelized into new paths using R and Python as prime software for data science. Visualizing, prototyping and implementing data analytics just became handy for the data scientist through Python and R languages. Implementing these functions on data sets irrespective of their size will be so eased.

Matlab is also good in prototyping that can accurately create a prototype of algebra and matrix with effective visualization. It also allows performing text mining and file parsing. The main backdrop is that Matlab is very expensive and it needs to be paid separately for the toolbox.

Octave, JRuby, and rhubarb are a few other platforms that share their time in the industry. Each of these is individually specialized on their own aspects. Rhabarber is also a language that enables to drag the syntax dynamically to have literal matrices.   

JRuby is also a language that is integrated with Java that enables to write high-performance code at the time necessary. Matlab is specialized in a few aspects in which Python lags. Shotgun also had much time in the field, even they turned themselves to Python.

Matlab is a dynamic platform that can automate to reload after they get modified. Matlab provides the opportunity to make a step by step analysis of the data and it also enables the modifications of each function. Python maintains a different type of interface when compared with Matlab. Python is a friendly platform that can initiate with the command line.

Elefant toolbox is an aspiring project, but could not able to finish the race. PETSc is a scientific application that contains distributed matrix and simple things like creating a matrix. Dealing with new language make the people more doubtful to work but, Python worked out and came with the desired result.

The industry has many effective tools to work with data and they are also freeware in data science. Python supports object-oriented and structured programming. It also supports functional programming patterns. It can handle any sort of data mining activity to construct a website and can also run the embedded system.  

Scilkit-learn and PyBrain are used to establish neutral networks and data processing. These are machine learning libraries. Stasmodels tool is used for statistical analysis. NumPy contains SciPy that provides techniques for scientific analysis.

There are few specialized libraries used in data science. SymPy library is used in statistical applications and other libraries like Shogun, PyLearn2 are machine learning libraries. Bokeh, d3py, and Plotly are few libraries used for plotting visualization of data. csvkit, PyTables, and SQlite3 for storage and data formatting.

Python experts are always within reach to help beginners in both online and offline mediums. Python is widespread in the data science platform. Pandas is a library used in data science for importing data from the excel sheets.

Conclusion 

Python has created a wide base in the data science industry. It is a multi-paradigm programming language. Python can display the data visualization accurately and also it can handle the huge data sets maintained by the company.

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