Why data analysts should learn python

The advantages of Python are also very prominent, such as it is easy to get started, the code is concise and efficient, and it has become a data analysis tool for many academic researchers and ordinary enthusiasts. So why should data analysts learn Python? Below we will introduce these contents to you.

# Obtaining data is the first step in data analysis. Without data, the work of data analysis is meaningless. (Recommended learning: Python video tutorial)

Of course, there are many ways for us to obtain data, but the best way is to use Python. Python can help us obtain data with its powerful functions . Of course, languages ​​such as Java can also implement crawler functions, but Python is relatively simple to implement. Moreover, the learning cost of Java is too high, while Python is very simple. Let's take a look at Python's data analysis function.

So what is the scope of use of Python?

In fact, python provides users with a series of data analysis packages. Frequently used analysis packages include Numpy and pandas; in addition, it also provides users with some efficient tools needed to operate large data sets. use tools. The amount of data processed by the average enterprise is actually between tens of thousands and hundreds of thousands. When it comes to larger-scale data, ordinary people may rarely have the opportunity to process large-scale data. However, the processing of tens of thousands or hundreds of thousands of data may be the normal data processing of small and medium-sized enterprises and research institutions at present and even in the future. In the face of such a scale of data, Excel will be so slow that people want to smash the computer, and SPSS Although professional statistical software such as , R and R are relatively better, most people do not use them. In this case, Python offers an excellent choice.

Python’s advantages are very outstanding, especially in data cleaning. It has been praised by data analysts. First of all, in terms of data cleaning, Python is not only flexible and easy to use, but also highly efficient. Compared with Traditional statistical software has great advantages. Experienced data analysts all know that data cleaning is almost the most time-consuming in the entire data analysis project. Then there is reusability. The program has good reusability. It can be written once and run directly next time, which can greatly reduce the amount of repeated work. Of course, with the ability to link to other data sources, Python can easily connect to the Internet to send/extract data, and can also access data from almost all storage format documents, including text documents, Excel, pictures, and various SQL databases. In this way, data analysts can not rely on others to provide data in a specific format, greatly improving the ability to use data. Finally, Python has good scalability. Python has the ability to process small data to big data, and its functions other than data analysis are also very powerful. There is absolutely no harm in learning it.

We have introduced to you the reasons why you must learn Python in the data analysis industry. It is not difficult to find that Python is indeed a very practical skill. Therefore, being able to use Python proficiently can help everyone better perform data analysis work.

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