Python And Data Analysis

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.

Python handles large data, and friends who need it can refer to it. The recent big data competition is very hot, I did not learn how long python, want to try to write, just realize the data processing, mainly used dict,list,file knowledge. Also have to say, I also use MATLAB to achieve, but run to almost two minutes, but the python seconds processing, there is wood, it shows Python processing text function powerful. Data format in file: ClientID shopingid num Date ...

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 ...

Based on the Python Sina Weibo data reptile week China; Zhang Huian; Xiaijiang at present, many social network research is using foreign platform data, while the domestic Sina Weibo does not have a good interface to facilitate the researchers to collect data for analysis. In order to quickly obtain the data in the micro-blog, developed a support for parallel micro-bo data crawling tool. The tool can capture the user's fan information, micro-Bo Zhengwen and so on in real time, and use the keyword matching technology to match the micro-blog with the specified conditions, and to crawl the related content.

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 ...

Large flow of log if the direct write Hadoop to Namenode load, so the merge before storage, you can each node log together into a file to write HDFs.  It is synthesized on a regular basis and written to the HDFs. Let's look at the size of the log, 200G DNS log files, I compress to 18G, if you can use Awk Perl, of course, but the processing speed is certainly not distributed as the force. Hadoop Streaming principle Mapper and reducer ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. 　　But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. 　　Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding ...