Many friends may ask, why do you want to learn Python, even if you learn what Python can do? Generally, you will first think of reptiles.
The reptile is not the kind of worm. The reptile is actually similar to Baidu spider, Google spider. It will automatically crawl the content on the webpage.
General Python can be developed to a lot of convenience:
1, for example, can do web application development
In China, Douban used Python as the basic language for web development from the beginning. The whole architecture is also based on the Python language, which makes web development very good in China. Youtube The world's largest video site is also developed by Python. There is also a well-known instagram that is also developed in python.
2, web crawler
The crawler is a scene that is relatively operational. For example, Google's crawler was written in Python early. There is a library called Requests. This library is a library that simulates HTTP requests. It is very famous! Learned Python People don't know about this library. The data analysis and calculation after crawling is the area that Python is best at, and it is very easy to integrate. However, the current popular web crawler framework for Python is a very powerful scrapy.
3.AI artificial intelligence and machine learning
Nowadays, artificial intelligence is very popular, and various training classes are madly advertising and enrolling. Machine learning, especially the deep learning of the current, the tool framework mostly provides a Python interface. Python has a good reputation in the field of scientific computing. Its simple and clear syntax and rich computing tools are very popular among developers in this field. To put it bluntly, it is because Python is easy to learn and rich in framework. Many frameworks are very friendly to Python, and that's why I learn so much about Python!
4, data analysis
Generally, after we crawl a large amount of data with a crawler, we need to process the data for analysis. Otherwise, the crawler crawls. Our ultimate goal is to analyze the data. In this regard, the database for data analysis is also very rich, and various graphics. Analysis charts, etc. can be made. It is also very convenient. A visualization library such as Seaborn can draw data using only one or two lines. With Pandas and numpy, scipy can simply filter and regress large amounts of data. In subsequent complex calculations, docking machine learning related algorithms, providing a Web access interface, or implementing a remote call interface is very simple.
