Python Analysis Tools

We've sorted out 15 of the most popular Python open source frameworks from GitHub, including event I/O, OLAP, web development, high-performance network communications, testing, reptiles, and more. 1. Django:python Web application Development Framework Django should be the most famous Python framework, and Gae and even erlang have frameworks that are affected by it. Django is the direction of walking all-inclusive, it is the most famous is its fully automated management background: Just to use ORM, simple ...

With the development and popularity of artificial intelligence technology, Python has surpassed many other programming languages and has become one of the most popular and most commonly used programming languages in the field of machine learning.

R: It's not a real language. Part of the reason we learn about R is that it's not really a programming language. John Cook, an R expert, said: "R is a statistical interactive environment, not a real programming language." It is more helpful to think of R as an interactive environment containing programming languages. "But as Bob Muenchen emphasizes, R is even harder for people who are proficient in SAS and SPSS data tools." About R for analyst ...

Anaconda is the first choice for beginner Python and entry machine learning. It is a Python distribution for scientific computing that provides package management and environment management capabilities to easily handle multi-version python coexistence, switching, and various third-party package installation issues.

This series of articles will show you how to use Pyomo's ability to integrate Python to model and optimize your application. The first article in this series will introduce http://www.aliyun.com/zixun/aggregation/22929.html > basics. Part 2nd will describe how to add more tools and build a scalable architecture. Part 3rd will provide practical examples of investment analysis and statistical analysis using IPython and pandas. ...

The "Editor's note" machine learning seems to have turned from obscurity to the limelight overnight, as well as more open source tools for machine learning, but the challenge now is how to get developers interested in machine learning and the data they are prepared to use to actually use them, This paper collects the common and practical open source machine learning tools in several languages, which is worth paying attention to, which is from InfoWorld. The following is the original: After decades of development as a professional discipline, machine learning seems to appear overnight as a popular business tool ...

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

Wingide is a fairly good IDE; its editors include a large number of language syntax tags highlighted, although it is just a tool for Python. The source browser is very useful for browsing items or modules (tables are now navigable in source code and document line summaries). Although there are no monitors, the debugger is well designed. The editor has excellent command autocomplete and function jump lists, but no code merging. The project-oriented IDE is useful for large products (in this respect, it is in most free ides except Komodo ...).

Intermediary transaction SEO diagnosis Taobao guest Cloud host Technology Hall This is the latest Web Development toolkit that Mashable collects, including drag-and-drop Web application creation tools, code libraries, project management, test programs, and frameworks that support a variety of programming languages, from Ajax to Ruby to Pytho N。 　　This is the second part. 　　Reference and Data Cofundos-a platform to discuss open source software for investment. Mac Yenta-...

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Dougcutting based on Google's published academic paper. Users can easily develop and run applications that process massive amounts of data in Hadoop without knowing the underlying details of the distribution. The features of low cost, high reliability, high scalability, high efficiency and high fault tolerance make Hadoop the most popular large data analysis system, yet its HDFs and mapreduc ...