Setting up Python Environment in Data Science

by Jones David

Python: a general-purpose programming language that is becoming very popular these days among companies. Employers from all over the world are using Python to bring out better results from their upcoming projects linked with data science. Here, we provide information and related knowledge that one needs to possess to learn python for data services.

Python is an interpreted object-oriented language that enables easy and simple programming for both small and large applications. It mainly focuses on reusability and readability of code while using white space.

Python has been called among The popular programming languages. Python can be used for developing desktop GUI application, various websites and web application. Python in Data sciences can be used for developing basic concepts and data structure.

Also, python is a High-level Language that helps its users to know about the core functionality of the application by taking care of the very common tasks related to programming. The ease of the syntax and the rules of the language make it easier for the people to understand the code and the syntaxes made readable and the applications are made more maintainable.

Why Python for Writing Software Application

  • Easily Readable and Maintainable Code

It is important to keep in mind the simplicity of the codes for its easy maintenance and updates. The syntax rules for Python make it easier to understand the concept without making additional and unnecessary coding. Also, unlike other programming languages, python allows its users to use easy English language with proper punctuations so that it becomes easily readable and accessible. This readable code helps in easy maintenance and updating of the software without putting extra efforts and wasting much time.

  • Multiple Programming Paradigm

Like other modern languages for programming, Python also supports programming paradigms. It completely supports object-oriented and structured programming. Other than this, python also features dynamic type of system. These programming paradigms of Python help to develop large and much complex software applications.

  • Compatible Across Major Platforms and Systems

At present, Python supports many operating Systems. Python interprets can also be used to run the codes on various different platforms. Moreover, it helps you to run the same code on different platforms without recompilation. So, there is no special need to restructure the code once it is written. These interesting features help to run the program at different platforms without increasing the development time.

  • Prosperous Standard Library

It’s large and prosperous standardized library helps it to stand above all the other programming languages. This library helps the users to choose the modules according to the desired needs. One need not write the additional code and can easily add functionality to the existing module. For example: while you make a web application with Python, specific modules can be used for web services or working with internet protocols. Information about various modules can also be gathe red from standard library documentation.

  • Many Open Source Frameworks and Tools

Being an efficient programming language, Python ensures to cut short the software development cost. Various frameworks, library, and tools can be used to curtail the cost and to save time and resources. For example, you can easily simplify and speed up the web application process by using Python frameworks like Django, Flask, Pyramid, Bottle and Cherrypy.

  • Simplify Complex Software Development

As it is a general-purpose language, so it can be used both for desktop as well as developing web Application. Python is specially designed to facilitate data analysis and visualization. This feature can be used as an advantage to put big data solution without putting extra efforts.

  • Adopt Test Driven Development

Python can be used to create a prototype of the software application. Also, by simply refactoring the python code, software application be directly built. By using a test-driven development approach, coding and testing can be done simultaneously. You can write the required test prior to making the code and the structure and test the code continuously. These tests can also be used to check if they meet the prerequisite requirement of the structured code.

Uses of Python

  • Web Development and Internet Development

It helps you to develop a web application easily and on time. It has various libraries for protocols like HTML and XML, email processing, etc.

  • Desktop and GUI Applications

Python helps you to draft a graphical user interface with the application. In addition to this, there are certain tool-kits available to help you make your task easier.

  • Numerical and Scientific Applications

Python has found its way in scientific research as well; there are different zones that are available for this:

  • Pandas
  • SciPy
  • IPython
  • Software Carpentry Cost
  • Application in Business

It is a great choice in developing various e-commerce systems. Odoo is management software with various business applications. It is an all-rounder and helps in efficient management of applications.

  • Application in Education

Python is simple, economic and has the capability to deal with the larger community. It can be made to learn an introductory programming language.

  • Games and Graphics

When people say that they are learning Python, the first thing that comes to mind is 3D gaming and graphics. PyGame and PyKyra are two frameworks in Python for game development.

  • Others

Other than the cases discussed above python serve multiple uses like:

  • Learning Robotics
  • Computer vision
  • 3D CAD application
  • Scripting etc.

Like other programming languages, python does have its own shortcomings, but the ease of learning and implementing makes it the most popular language among the learners. With available modules, frameworks, and libraries, python provides the ease to process the code in the most Basic English language that is readable and reproducible. Data analysis and visualization can be easily done with Python to make your data more presentable and colorful. This could be easily learned under wise supervision and undertaking practical projects at real-time bases, and testing once code before final activation.

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