Data analysis using Python reading notes-the 11th chapter on financial and economic data applications

Since 2005, Python has been used more and more in the financial industry, thanks to increasingly sophisticated libraries (numpy and pandas) and a wealth of experienced programmers. Many organizations find that Python is not only a great fit for an interactive analysis environment, but also a very useful system for developing files, which takes much less time than Java or C + +. Python is also a very good glue layer that makes it very easy to build Python interfaces for libraries written in C or C + +.

The field of financial analysis is extensive and profound. The effort spent on data normalization is often much more time-consuming than solving core modeling and research issues.

In this chapter, the term section (cross-section) is used to represent data at a point in time. For example, all constituent stocks in the S & P 500 index form a cross section at the close of a particular date. Multiple data at multiple point-in-time section data constitutes a panel. Panel data can be represented either as dataframe of a hierarchical index or as a three-dimensional panel pandas object.

1, the topic of data normalization

Data analysis using Python reading notes-the 11th chapter on financial and economic data applications