R and Python for statistical analysis, which is better

R is a must, python is strong, but not required. If it's just statistical analysis, there is no doubt that r is a must-learn.

If you're going to a large company, you're going to learn both.

No better. Python has been used for more than 10 years, and I don't feel that it is really good, just a passable scripting language. It's not too good, but it's not much better.

r has been studying for 2 years and is very strong. I've been working on big data. It feels very convenient. I can do all the big data stuff without r, and I don't have a python anymore.

But for a person with a statistical background, there is no way without R. Even if you know python, it is difficult to help yourself directly quickly without a development level that is not in the stream. After all, Python is a general-purpose language that requires a lot of accumulation to really work.

The k-means algorithm is the most famous clustering algorithm, which is simple in idea but has good results. The steps of the clustering algorithm are as follows: 1:

Initialize k samples as initial clustering centers; 2: Calculate the distance from each sample point to k centers, and select the nearest center as its classification until all sample points are classified; 3: Calculate the K classes separately.

r is more professional, because after all, he is specifically for statistical use.

Python is recommended, it is easy to learn, and the relevant libraries are very rich

Life is short, I use python

r is the language of statistical analysis. Python is a general-purpose language with a wider range of applications. If you focus on data analysis, there is little difference between mining and selection, and if you involve other development in addition to data processing, you can only choose Python.

R is a more professional statistical analysis software, especially R itself has a lot of statistical functions, such as t-test, normality test, etc., which is relatively simple and convenient in analysis. But if you are doing statistical analysis of big data, mastery of python is also a must, and python is more conducive to crawlers and text mining.

It is recommended that if you just do simple data analysis, but feel that SPSS is too low, use R, because R is easy to learn, and R can load a lot of packages, such as ggplot, the painting function is very powerful, and it looks very tall in **, as well as TM packages, etc., which are very useful loading packages.

But if you need to do big data analysis and data scraping, you should master Python.

It's best to learn Python after learning R, at least, as statistics majors, we must learn R and Python is learned by ourselves, and the teacher only recommends but does not require it.

In addition, the R language feels slower when it comes to some more complex data processing. We often run for hours. However, python feels much faster.