![]() Speed: Python is an interpreted language and thus is relatively slower than other programming languages. Python codes can be integrated with other programming languages like C++. Productivity: Its integration and control capabilities enhance and save a lot of time.Įmbeddable: Python codes are embeddable. Libraries: Python has many libraries that are necessary to carry out major data science-related functions. ![]() It has one of the most active supporting forums, and anyone can contribute to improving the libraries and their functionalities. Open Source: Python can be downloaded easily. Python is object-oriented, but it makes a transition to functional features allowing itself into different paradigms of programming. Python’s flexibility makes exploratory data analysis hassle-free. It is neat, uncomplicated to use, and well structured. Versatility: The language is one of the most versatile ones. The addition of Jupyter Notebook, a web application to share the code live, makes the data science explanations smooth. It includes libraries like Scikit, Keras, Tensorflow, Matplotlib, NumPy, Pandas, etc., that provide sophisticated functionalities. As it is an interpreted language, the debugging of the program becomes very easy. Programmers love Python as it helps them increase their code efficiency. The Python interpreter and libraries are free for distribution. In 3 simple steps you can find your personalised career roadmap in Software development for FREE
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