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This article mainly introduces the reasons for choosing python for data analysis. It has certain reference value. Interested friends can refer to it. I hope you will gain a lot after reading this article. Let Xiaobian take you to understand it together.

A large and vibrant scientific computing community

Among the many interpretive languages, Python's biggest feature is that it has a large and active scientific computing community, and since the beginning of the 21st century, the adoption of Python for scientific computing in industrial applications and academic research has become more and more fierce.

constantly improving library

If you want to use someone else's well-built mature wheel, python is the best choice. Python has many wheels, such as numpy, scipy, scikit learn, gensim and so on. These libraries make Python a great alternative to data processing tasks, and are unique to other open source and commercial domain-specific programming language tools such as R MATLAB SAS Stata. Combined with its general purpose programming prowess, Python is a language we can use to build data-centric applications.

Python as a binder

Python's success as a scientific computing platform stems in part from its ability to easily integrate C++ and Fortran code. Most modern computing environments utilize some Fortran and C libraries to implement linear algebra, optimization, integration, fast Fourier, etc. algorithms. Numpy libraries are also, and collaboration between them is quick and easy.

Of course, it has some shortcomings, because Python is an interpreted language, most Python code must be forced to Java C++, which is much slower.

Thank you for reading this article carefully. I hope that Xiaobian's "What are the reasons for choosing python for data analysis" will be helpful to everyone. At the same time, I hope that everyone will support you a lot and pay attention to the industry information channel. More relevant knowledge is waiting for you to learn!

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