Python VS R language? Which one should you choose for data analysis and mining?

What is R language?

R language, a free software programming language and operating environment, is mainly used for statistical analysis, graphics, and data mining. R was originally developed by Ross Ihaka and Robert Jetman from the University of Auckland, New Zealand (also called R), and is now developed by the "R Development Core Team". R is a GNU project based on the S language, so it can also be regarded as an implementation of the S language. Usually, codes written in the S language can be run in the R environment without modification. R's syntax comes from Scheme.

R’s source code can be freely downloaded and used, and compiled executable file versions are also available for download, which can run on a variety of platforms, including UNIX (also FreeBSD and Linux), Windows and MacOS. R is mainly operated from the command line, and several graphical user interfaces have been developed.

R’s functionality can be enhanced through user-written packages. Added capabilities include special statistical techniques, graphing capabilities, as well as programming interfaces and data output/import capabilities. These packages are written in R, LaTeX, Java and most commonly C and Fortran. The downloaded executable version will come with a batch of core functional software packages, and according to CRAN records, there are more than a thousand different software packages. Several of them are commonly used, such as for economic econometrics, financial analysis, humanities research, and artificial intelligence.

Common features of Python and R languages

Python and R have relatively professional and comprehensive modules in data analysis and data mining. Many commonly used functions, such as matrix operations, vector operations, etc., have relatively advanced uses

Python and R are multi-platform adaptable languages. They can be used on Linux and Windows, and the code is highly portable. Python and R are closer to commonly used mathematical tools such as MATLAB and Minitab. The difference between Python and R languages.

In terms of data structure, since it is from the perspective of scientific computing, the data structure in R is very simple, mainly including vectors (one-dimensional), multi-dimensional arrays (matrix when two-dimensional), lists (unstructured data), and data frames. (structured data). Python contains richer data structures to achieve more precise access to data and memory control, such as multi-dimensional arrays (readable, writable, ordered), tuples (read-only, ordered), sets (unique, unordered), and dictionaries. (Key-Value) and so on.

Python is faster compared to R. Python can directly process the data of G; R cannot. When R analyzes the data, it needs to convert the big data into small data through the database (through groupby) before it can be handed over to R for analysis. Therefore, it is impossible for R to directly analyze the behavior details. It can only Analyze statistical results.

Python is a relatively balanced language that can be used in all aspects. Whether it is calling other languages, connecting and reading data sources, operating the system, or regular expressions and word processing, Python has obvious advantages. . And R is more prominent in statistics.

Application scenarios of Python and R language

Scenarios of applying Python

1. Web crawler and web crawling

Python’s beautifulsoup and Scrapy are more mature and powerful. Combined with django-scrapy, we can quickly build a Customized crawler management system.

2. Content management system

Python only uses sqlachemy. Through ORM, one package solves the problem of multiple database connections and is widely used in production environments. Based on Django, Python can quickly build databases and backend management systems through ORM, while the authentication function of Shiny in R still requires payment for the time being.

3. API construction

Through standard network processing libraries such as Flask and Tornado, Python can also quickly implement lightweight APIs, while R is more complex.

Scenarios for applying R language

1. Statistical analysis

Although Scipy, Pandas, and statsmodels in Python provide a series of statistical tools, R itself is specially built for statistical analysis applications, so it has more such tools.

2. Interactive panel

R’s shiny and shiny dashboard can quickly build customized visualization pages. It's faster and requires less code.

In general, Python’s pandas draws on R’s dataframes, and rvest in R draws on Python’s BeautifulSoup. The two languages ​​are complementary to a certain extent. Generally, we think that Python is better than R in computer programming and networking. It has more advantages in crawlers, and R is a more efficient independent data analysis tool in statistical analysis. Therefore, learning Python and R at the same time is the king of data science.