Introduction
In Python, we have learned all the basic concepts in my previous articles. These programming concepts are very common and useful for the Python Applications for beginners as well as for an expert. In this article, we will see what makes Python popular and will discuss all the future aspects of this cutting edge language. Also, we will check out the benefits and drawbacks of Python.
Easy to read and easy to code
Compared to other programming languages Python is very easier to understand. The programming basics are as simple as mathematics where 3+5=8. So it might be your first programming language if you are a beginner.
Free and Open Source
We can easily download Python from its official link, where we can found all the previous releases. We can go with latest version, whether you know the previous versions or not.
Interpreted
There is no need to compile the code, likewise C++ and Java. So debugging becomes very easier than other programming languages.
High Level
The programmer does not have to learn the System Architecture for Python. There is not to worry about the memory management concept, it is the key feature of Python.
Dynamically Typed
For any program, we have different variables with different values. In that case, the Python language does not allow the user to define the type of variables. It will automatically understand the variable type and returns the values.
Portable
Python code is written on the Windows operating system. If you want to run this same code on macOS, in that case, it will run without any interruption because the system-dependent feature doesn't affect Python.
Extensible
It is possible to write Python code in other programming languages.
Large Standard Library
Python language has large library where we can find unit-testing, CGI, image manipulation, databases, regular expressions, and many other in-built functions and methods. So we don’t need to code for apply functionalities to the program.
GUI programming
wxPython or JPython these toolkits are used to perform GUI programming. These are very easy to implement and fast development methods.
Python Applications
Web & Internet Development
There are major frameworks used for web applications, such as Django, Pyramid, and Flask.
Database Access
For data science, if we have to perform Data Analysis then use the Pandas framework. To implement Data Visualization Metplotlib, seaborn libraries are used. For any Numeric and Scientific Calculations, the Numpy framework is used. For Machine Learning, the Scikit-learn is used. As a library, machine, and deep learning-TensorFlow, Keras frameworks are used. These frameworks and libraries are used for Data Science in Python.
Artificial Intelligence
For Deep learning, the Keras library is used. For any Deep learning framework, the Microsoft Cognitive Toolkit is implemented. OpenCV-Open source computer vision is used as the library; NLTK- Natural Language Toolkit. These Python Libraries are used for AI.
Automation
Robot framework, PyTest, Selenium Python, PyUnit are different frameworks or libraries used for automation.



Ankur MistryPosted Dec 21, 2019, 9:33 AM
Very nicely explained