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This article mainly explains "what is the application scenario of Python and R language". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Next, let the editor take you to learn "what is the application scenario of Python and R language?"

What is R language?

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

The source code of R is freely available for download, as well as compiled executable versions that can be run on a variety of platforms, including UNIX (including FreeBSD and Linux), Windows and MacOS. R is mainly operated on the command line, while several graphical user interfaces have been developed.

The functionality of R can be enhanced through user-written packages. The added functions include special statistical technology, drawing function, programming interface and data output / input function. These packages are written in the R language, LaTeX, Java and the most commonly used C language and Fortran. The downloaded executable version will be accompanied by a number of core functional software packages, and there are more than a thousand different packages according to CRAN records. Several of them are commonly used, such as econometrics, financial analysis, humanities research and artificial intelligence.

Common characteristics of Python and R language

Python and R have professional and comprehensive modules in data analysis and data mining, and many commonly used functions, such as matrix operation, vector operation, etc., have more advanced usage.

Python and R languages are adaptable to multiple platforms, linux and window can be used, and the code is highly portable.

Python and R are close to common mathematical tools such as MATLAB and minitab.

The difference between Python and R language

In terms of data structure, from the point of view of scientific calculation, the data structure in R is very simple. It mainly includes vector (one-dimensional), multi-dimensional array (two-dimensional matrix), list (unstructured data), data frame (structured data). On the other hand, Python contains richer data structures to achieve more precise data access and memory control, such as multi-dimensional arrays (read-write, ordered), tuples (read-only, ordered), collections (*, unordered), dictionaries (Key-Value), and so on.

Compared with R, Python is faster. Python can directly process the data on G, but R can not. When analyzing data, R needs to convert big data into small data through the database (through groupby) before it can be handed over to R for analysis, so R can not analyze behavior details directly, but can only analyze statistical results.

Python is a relatively balanced language, which can be used in all aspects. Python has obvious advantages in calling other languages, connecting and reading data sources, operating on the system, or regular expression and word processing. On the other hand, R is more prominent in statistics.

Application scenarios of Python and R language

Scenarios in which Python is applied

1. Web crawler and web page crawling

Python's beautifulsoup and Scrapy are more mature and more 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 connecting multiple databases, and is widely used in production environments. Based on Django,Python, the database and background management system can be quickly established through ORM, while the authentication function of Shiny in R still needs to be paid for use.

3. The construction of API

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

The scene of applying R language

1. Statistical analysis

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

2. Interactive panel

Shiny and shiny dashboard of R can quickly build custom visual pages. Faster and requires less code.

Generally speaking, Python's pandas draws lessons from rvest in R's dataframes,R and Python's BeautifulSoup. To some extent, the two languages are complementary. Generally speaking, we think that Python has more advantages than R in computer programming and web crawlers, while R is a more efficient independent data analysis tool in statistical analysis. So, learning Python and R at the same time is the king of data science.

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