Shulou(Shulou.com)06/02 Report--

In this issue, the editor will bring you about the advantages of using Python for data analysis. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

Under the tide of big data, many industries begin to use data to guide the implementation of various business decisions. So how should we analyze the data? At this time, Python appears in front of us. As a sharp tool for data analysis, what is the difference between it and other data analysis tools? Let's take a look at the advantages of using Python when doing data analysis, in addition to its own advantages of concise, efficient and easy to use, but also has a strong performance that many tools do not have.

1. Python's own advantages.

Python is easy to learn and readable, and a good piece of code reads like reading a foreign language article, so that you can only care about what you do instead of obsessing about Python grammar. In addition, it also has many excellent libraries for data analysis. Currently, there are more than 125000 third-party Python libraries. For data-centric libraries like pandas,NumPy and matplotlib, anyone who understands the syntax rules of Python can operate and deploy. The most important thing is that it is free and open source!

2. The comparison between Python and other data analysis tools.

(1) Python processes Excel tables by calling modules to process the data and generate reports. Compared with Excel,Python, it can deal with larger data sets, realize automatic analysis more easily, and build complex machine learning models more easily.

(2) compared with spss,spss, it is a statistical software, which is only suitable for the analysis of experimental data in the field of scientific research, but not suitable for the analysis of data in practical application scenarios, while Python can deal with complex data logic, so it has more advantages in these scenarios.

(3) compared with R language, Python's machine learning library has only one sklearn, and all machine learning methods are concentrated in this library, while R language, I don't know how many machine learning libraries it has. The machine learning methods in R language are so scattered that they are difficult to master. And the number of Python users is on the rise, and some people who used to only use R are turning to Python to invest in an upward trend of technology, so that the future will be broader.

(4) compared with the above tools, Python is more handy in doing machine learning, web crawler and big data analysis. At present, many applications in data science can be easily implemented with Python. Including data collection, cleaning, collation, visualization, machine learning, artificial intelligence, development, operation and maintenance, etc. So a Python alone can do a full range of services.

These are the advantages of using Python for data analysis. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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