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This article mainly introduces "what are the advantages of big data Python". In daily operation, I believe many people have doubts about the advantages of big data Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "what are the advantages of big data Python?" Next, please follow the editor to study!

Python is an excellent tool and is very suitable for data analysis as a combination of python and big data for the following reasons:

Open source

Library support

Numerical calculation.

Data analysis

Statistical analysis

Visualization

Machine learning

Python is regarded as one of the best data science tools to deal with big data. When you need to integrate data analysis with Web applications or statistical code with production databases, Python and big data are the most appropriate choices.

1. A bag of powerful scientific packaging

The Python big data combination is backed by its powerful library packages that meet the needs of analysis and data science, making it a popular choice in big data applications.

2. Compatible with Hadoop

Hadoop is one of the best big data tools. Because big data of Python is compatible, the more similar Hadoop and big data are synonymous with each other. Therefore, Python is inherently compatible with Hadoop to deal with big data. Python consists of a Pydoop software package that helps access HDFS API and write Hadoop MapReduce programming. In addition, Pydoop also supports MapReduce programming to solve complex big data problems with minimal effort.

3. Simple and easy to learn

Python is easy to learn because it abstracts a lot of things through its functionality, allowing users to write fewer lines of code. In addition, it also has scripting capabilities. Python combines user-friendly features such as code readability, simple syntax, automatic identification and data type association, and ease of implementation.

4. Scalability

Scalability is very important when you are dealing with huge amounts of data. Unlike other data science languages such as RJM MATLAB or Stata, Python is much faster. Despite initial complaints about its speed, its speed performance has been greatly improved when using Anaconda. This enables Python and big data to be compatible with each other with greater flexibility.

5. Large-scale community support

Big data's analysis usually deals with complex problems and needs the support of the community to solve them. Python, as a language, has a large and active community that helps data scientists and programmers provide expert support on coding-related issues, which is another reason for its popularity.

At this point, the study on "what are the advantages of big data Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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