One of R’s strengths is the ease at which a plot can be produced, including the mathematical notations and formulas. R comes with a wide range of statistical techniques such as linear modeling, non-linear modeling, statistical tests, clustering, etc. R is a programming language for statistical analysis or computing and graphics. Threading: Threading or flow of multiple functions at the same time is a downside in Python due to its Global Interpreter Lock(GIL). The process gets slower when more objects need to be accessed.ĭatabase Access Layers: Python’s database access layers are underdeveloped in comparison to Java Database Connectivity(JDBC), and Open Database Connectivity(ODBC), making it a less used database connectivity. Memory consumption: Python consumes a significant amount of RAM. However, it can be used with additional efforts. Developers claim it to be weak language in such an environment. Mobile environment: Python is not suitable for android and iOS environments. 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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