Data mining is highly flexible and can provide more abundant APIs and tools. You can also write and expand modules based on other language tools such as C language and C++, which is compatible with third-party databases. Therefore, Python is more flexible in data mining and can be widely used in financial data collection.

Data analysis is efficient and fast. When the amount of data is huge and requires more complicated calculation and analysis, such as fast switching, aggregation, grouping and data modeling of multiple tables, Excel cannot handle it efficiently. At this time, using Python Panda Library for high-level data processing has obvious advantages and outstanding effects.

3. Visualization of data analysis, which shows the results of financial analysis in the form of digital reports or tables, can not show the changes of data intuitively, and the use of charts can make people better grasp the essence of information. Python's chart visualization function can fully meet this demand, vividly show the data core and provide the basis for the company's management decision.