Set Up Python Environment for Data Sciences

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An emerging high-level programming language, Python is becoming popular among data scientists for being fast, powerful, easy to learn, and open language. Other than this, Python is very compatible with all the platforms and helps in bringing out better results from the upcoming projects. Not only limited to small projects, but this object-oriented language also enables simple and easy programming for large applications as well.

 

Emerging among the top ten most popular languages among programmers, most of the data scientists are using Data Science with Python in their day-to-day work.

Python for Data Sciences

This is a multi-paradigm programming language that supports structured programming, functional programming, and object-oriented programming. The readability and simplicity offered to the programmers by Python make it easy to pick up the language. It has free to download conveniently available analytical libraries that could cater to the need of all sectors.

This language perfectly fits the demands of the data scientists when they need to integrate with web apps and even when there is a need for incorporating statistical codes with production bases. The well-organized programming nature of Python makes it a perfect fit for implementing all kind of algorithms.

Why Python?

Other than the ease of reproducing the code and use of simple English language for the data scientists, Python has many features:

● Ease Of Learning

The main reason for Python gaining popularity is that it is easy to grab and learn. Anyone who wishes to learn this language can give it a try and gain expertise very easily and quickly. The ease of understandability of the syntax of Python gives it an upper hand over the other languages used by the data scientists like R. It also promotes a shorter learning curve.

● Scalability

When you compare Python with other languages on the basis of its scalability, it emerges as a lead. This language is even faster than Matlab and Stata. You can even guess the ease and the flexibility of the language from the fact that YouTube has also migrated to Python. Many industries have started using python due to the various benefits it offers and is used in the rapid development of various kinds of applications.

● Choice of Libraries

There are various frameworks or libraries available for data scientists that have given it a push in this area. The libraries well known for data science community include SciPy, Scikit-Learn, NumPy, Pandas, and the like. And the list for the libraries does not end here. Due to the advancement in the language, Python has been able to find solutions for various problems that could earlier be considered as constraints.

● Python Community

The reason for an incomparable rise in the Python is due to its ever-expanding ecosystem. As Python has made its move towards the data sciences, people have volunteered for creating libraries, which has helped them to create more interactive and modern tools in Python.

Due to the increase in the community, it has become easy for the aspirants to have access to the solutions for their problems. All the queries are just a Google search away.

● Visualization and Graphics

The visualization options available with python are many. Due to various visualization packages available with Python, one can create charts, graphical plots, and web-ready interactive plots.

Data Analysis Libraries

The compatibility of data sciences and Python is very high. If one has decided to take Python for data sciences, then the next thing one must do is to check for the various libraries that do the analysis tasks. Here we have compiled the most famous libraries for data scientists:

● NumPy

This is nothing but a linear algebra library in data science. This can also be termed as the foundation base for other tools. It can be used to perform logical and mathematical operations on arrays. Also, this helps the programmers to understand the Pandas library effectively.

● SciPy

This library is basically used for scientific and technical computing with varied modules for image processing, special functions, linear algebra, optimization, and similar tasks. Also, this library finds its use in working with NumPy during various numerical routines.

● Pandas

This is the most preferred library among data scientists and can be termed as a game changer. The amazing thing about Pandas is that it creates objects with columns and rows that are called as data frames. Also, working with this framework makes it easier to handle missing data, aligning data automatically and it also makes it easier to work with data taken from different sources.

● SciKit

This library is specifically designed to interoperate with SciPy and NumPy. Also, this library has various classifications, regressions, and clustering algorithms. Other than this, algorithms like random forests, k-neighbors, and support machine vector are supported by SciKit.

● Matplotlib

With this library, easy things can be made simple and also it makes tackling the information for tricky objects easier. Special modules available with Matplotlib have features like font properties, formatting axes, and control line styles which makes the tasks easier.

Python programming is used by various employers all over the world so that better and promising results can be obtained from the existing projects of the organization. Other than this, Python has gained popularity because of its easily readable code that is in English and using proper punctuations. This platform independent language can work on any platform without causing any loss in the performance.

Also, Python can be used to create a prototype of software application. Advanced software applications can be successfully built by making minor changes in the Python code. Moreover, because of its test-driven approach testing and coding can be done simultaneously. And, these tests can also be used to check if the code built means the requirements set from it.

 

With available modules and the frameworks, Python has turned out to be the easily readable and producible language in spite of its shortcoming. Using data analysis and visualization can be done with Python to make the language more readable and reproducible. Python has made learning of Programming language an easy experience that can be taken by anyone having an interest in coding.

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