In the data mining industry, who is used more, R or Python?

Both tools are very convenient and do not require very advanced programming skills. They are both suitable for algorithm development and have a large number of packages for you to use.

Python is easy to get started, while R is relatively difficult (purely a personal feeling, depending on everyone's previous experience, the experience may be different).

R is still a bit weak for text mining. Of course, its advantage is that the functions are written for you. You only need to know the form of the parameters. Sometimes even if the parameter form is wrong, R can " Intelligently” helps you adapt. This simple software is suitable for people who want to focus on their business.

Python can do almost everything. It has more functions than R and is faster than R. It is a language, and R is more like a software, so python is better able to develop flexible algorithms.

Python is suitable for processing large amounts of data, while R has a lot of limitations in this regard. Of course, the premise of this is that for children with relatively average programming basics, for adults, if you can use vector programming more flexibly, , the speed of R is not too bad either.

In terms of performance, Python is between high-level languages ????such as C/C++/Java and R language. Although the performance is not as good as those high-level languages, general daily data can be basically realized with Python, and there are no performance requirements. For picky people, it's enough

python

You need to install a series of packages such as numpy, pandas, scipy, cython, statsmodels, matplotlib

, you also need to install the ipython interactive environment. Using python alone to directly perform quantitative analysis and statistical functions does not have function support; R is based on statistical analysis, and its performance and efficiency are slightly inferior to python. The advantage of R is that it is superior to Python in statistics and data calculation and analysis.

The code programmed in the Python language is highly readable, beautiful overall, and is simple and crude in nature. A small amount of code can achieve complex functions in a short time; the syntax of R is very strange, and various packages do not comply with the syntax specifications. , causing it to often feel painful to use; R programs end up not looking as simple and beautiful as Python.

In terms of comprehensiveness

I think Python is indeed better than R. Whether it is calling other languages, connecting and reading data sources, operating the system, or regular expressions and word processing, Python has obvious advantages

. After all, Python itself emerged as a computer programming language, while R itself only originated from statistical computing. Therefore, in terms of language comprehensiveness, the difference between the two is significant.

Python is a machine

Most people in the learning field use it. As far as I know, few people who do marketing research, econometrics, and statistics use python

Reference from: blog.sina.com.cn/s/blog_8813a3ae0101e631