Python VS R language? Data analysis and mining which one should I choose?

What is the R language?

R language, a free software programming language and operating environment, mainly used for statistical analysis, mapping, data mining. R was originally developed by Ross Ihaka and Robert Jes (also known as R) from Oakland University in New Zealand 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 used as an implementation of the S language, and code written in the S language can be run without modification in the R environment. The syntax for r is from scheme.

R source code is freely available for download and has a compiled version of the executable that can be downloaded and run on a variety of platforms, including UNIX (also including FreeBSD and Linux), Windows and MacOS. R is primarily a command-line operation, while someone has developed several graphical user interfaces.

The functions of R can be enhanced by a package written by the user. The added features include special statistical techniques, drawing functions, as well as programming interfaces and data output/input functions. These packages are written by the R language, LaTeX, Java, and the most commonly used C language and Fortran. The version of the executable that you download will be accompanied by a batch of core features, and there are thousands of different packages based on the Cran record. Several of them are more commonly used, such as economic metrology, financial analysis, humanities research, and artificial intelligence.

The common features of Python and R language

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

Python and R two languages have multiple platform adaptability, Linux, window can be used, and code portability is strong

Python and R are close to MATLAB and common math tools like Minitab

The difference between Python and R language

In terms of data structure, the data structure in R is very simple from the point of view of scientific calculation, including vector (one-dimensional), multidimensional array (two-dimensional matrix), list (unstructured data), data frame (structured data). Python, however, contains richer data structures for more precise access and memory control, multidimensional arrays (readable, sequential), tuples (read-only, ordered), collections (unique, unordered), dictionaries (key-value), and so on.

Python is faster than R. Python can directly process the data on the G; R No, R analysis of data need to first through the database to transform big data into small data (through GroupBy) to the R for analysis, so R can not directly analyze the behavior of the list, can only analyze statistical results.

Python is a balanced language that can be used in all aspects, whether it is a call to another language, a connection to a data source, a read, an operation on the system, or regular expression and word processing, and Python has a clear advantage. and R is more prominent in statistics.

Application scenarios for Python and R languages

Scenarios for applying Python

1, web crawler and web 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 works with Sqlachemy through ORM, one package solves the problem of multiple database connections and is widely used in production environments. Based on Django,python, you can quickly build a database and a backend management system through ORM, while the authentication function of the Shiny in R is temporarily required for paid use.

3, the construction of the API

With standard network processing libraries such as flask and tornado, Python can also quickly implement lightweight APIs, while R is more complex.

Scenarios that apply R language

1. Statistical analysis

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

2. Interactive panel

R's shiny and shiny dashboard can quickly build custom visual pages. Faster and requires less code.

In general, Python's pandas borrowed from R's Dataframes,r rvest reference to Python's BeautifulSoup, two languages to some extent complementary, usually we think Python than R in computer programming, The web crawler has more advantages, and R is a more efficient independent data analysis tool in statistical analysis. So, at the same time learn Python and r These two brushes is the king of Data Science.

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