With the continuous development of the Internet, data analysis has become one of the main bases to guide our work direction. Today, let's look at how to use python programming to analyze data. The following computer training begins today's main content.

Why do you want to learn Python for data analysis?

Python, as a data analysis language, has recently attracted extensive interest. I have learned the basics of Python before. Here are some reasons to support learning Python:

Open source free installation

Great online community

Easy to learn

It can become a common language for data science and web-based analysis product generation.

Needless to say, it also has some disadvantages:

It is an interpreted language, not a compiled language-so it may take up more CPU time. However, considering saving programmers time (because it is easy to learn), it is still a good choice.

Python2.7 and 3.4

This is one of the controversial topics in Python. You are bound to meet it, especially if you are a beginner. There is no right/wrong choice here. It all depends on the situation and your needs. I will try my best to give you some advice to help you make a wise choice.

Why Python2.7?

Great community support! This is what you need in your early years. Python2 was released at the end of 2000 and has been used for more than 15 years.

There are too many tripartite libraries! Although many libraries provide 3.x support, there are still many modules that can only work on version 2.x. If you plan to use Python for specific applications, such as web development that relies heavily on external modules, then using 2.7 may be better.