Python and its use case in real-time Industry

The Master guide which gives you an idea about Python and its use in the real-time industry

How did it evolve? Python was invented in 1991 by Dutch programmer Guido Van Rossum. It is an interpreted language. This means that it has an interpreter to execute the program directly. As opposed to depending on more complicated machine languages. In fact, Van Rossum wants Python to eventually as understandable and clear as plain English. He has also made the language open source, which means that anyone can contribute to it, and he hopes that it will become as powerful as competing languages.


Where is it been used? Not surprisingly, given its accessible and versatile nature, Python is among the top five most popular languages in the world. Python is used by Wikipedia, Google (where Van Rossum used to work), Yahoo!, CERN and NASA, among many other organizations.


It can be used in a lot of places as described below:

  1. Web Applications. [ Django, Flask, Tornado ]

  2. Desktop Applications. [PyGTK, Cocoa ]

  3. Hardware Programming [ Raspberry Pi ]

  4. Data Science, Machine Learning and Deep Learning. Python is kind of a champion in this field [ Tensorflow, theano, Spark ]

  5. Automation of daily and industrial tasks.

  6. Network Programming such as SDN [ Ryu, OpenFlow (POX) ]

  7. Cloud and DevOps [ OpenStack APIs, Fabric]

Now let’s discuss the few reasons why one should learn python.

  1. Easy to learn: Python is simple and easy to learn as it does not have any complicated syntax or tough rules. It also resembles the English language quite closely and that is one of the main reasons that it is one of the most popular languages for beginners.

  2. Popularity and its Demand: Python is very popular in current times and also the fastest-growing language. Moreover, it was ranked one among the first in top programming languages by IEEE Spectrum 2018. Because of its popularity and multiple uses, Python developers are quite highly paid, especially in Machine learning, Web development and Data Science.

  3. Vast libraries and frameworks: Python has many libraries and frameworks for various different purposes. For example, Django is used for web development, PyBrain is used for data science, Tensorflow is used for machine learning, etc. This ensures that the process of application development is very easy and smooth as the libraries and frameworks can be used according to requirements.

  4. Most Popularly used in Data Science: Python is the most popular language in Data Science. One of the major reasons is that it provides many libraries and frameworks such as PyBrain, NumPy, SymPy, PyMySQL, etc. Also, the Python data analysis library, Pandas, is multi-faceted and one of the reasons for the success of Python.

  5. Machine learning & AI: Python is a popular language in Machine Learning as it can be used to build algorithms using statistics to allow computers to perform different actions. Some of the Python modules that are used to support machine learning are Theano, Scikit-learn, Tensorflow, etc. Also, Python is quite useful in Artificial Intelligence with libraries such as Keras that deals with neural network experimentation.

  6. Scripting and Automation: In addition to being a programming language, Python is also a scripting language. A Python script can contain functions that were imported as a library of functions in other scripts. Also, Python can be used to automate different tasks that significantly reduces the time and energy spent on them.

  7. Compatible with Major Platforms and Systems: At present, Python supports many operating systems. You can even use Python interpreters to run the code on specific platforms and tools. Also, Python is an interpreted programming language. It allows you to run the same code on multiple platforms without recompilation. Hence, you are not required to recompile the code after making any alteration. You can run the modified application code without recompiling and check the impact of changes made to the code immediately. The feature makes it easier for you to make changes to the code without increasing development time.

Conclusion: In a nutshell, I would like to conclude that an individual who puts sincere efforts in learning python would definitely excel in the industry due to its vast scope and helps to achieve a dream job of his.


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