What can you do after learning python?

Many friends may ask, why should we learn Python? What can we use it for even if we learn Python?

# #Generally, learning Python can lead to many convenient developments. (Recommended learning:

Python video tutorial)

1. For example, you can do web application development

In China, Douban has used Python as a The basic language of web development, Zhihu's entire architecture is also based on the Python language, which makes web development develop very well in China. YouTube, the world's largest video website, is also developed in Python. The very famous Instagram is also developed in Python

2. Web crawlers

are operated. There is a common scenario. For example, Google's crawlers were written in Python in the early days. There is a library called Requests. This library is a library that simulates HTTP requests. It is very famous! Anyone who has learned Python doesn't know this. Library bar, data analysis and calculation after crawling are the areas where Python is best at, and it is very easy to integrate. However, the most popular web crawler framework in Python is the very powerful scrapy.

3.AI Artificial Intelligence and Machine Learning

Artificial intelligence is very popular now, and various training courses are advertising and recruiting students like crazy. Machine learning, especially Most of the current popular deep learning tool frameworks provide Python interfaces. Python has always had a good reputation in the field of scientific computing. Its concise and clear syntax and rich computing tools are deeply loved by developers in this field. To put it bluntly, it is because Python is easy to learn and has rich frameworks. Many frameworks are very friendly to Python, and this is why I learn so many Python!

4, Data Analysis

Generally, after we use a crawler to crawl a large amount of data, we need to process the data for analysis, otherwise the crawler will crawl in vain, and our final The purpose is to analyze data. In this regard, the libraries for data analysis are also very rich, and various graphical analysis charts can be made. It is also very convenient. Visualization libraries such as Seaborn can plot data using only one or two lines, while using Pandas, numpy, and scipy can simply perform calculations such as screening and regression on large amounts of data. In subsequent complex calculations, it is very simple to connect machine learning-related algorithms, provide a Web access interface, or implement a remote calling interface.

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