Python for Finance: Analyze Big Financial Data

Conclusions

There are several options to integrate Python with Excel. Some Python libraries — like

xlwt or xlsxwriter — allow the creation of Excel spreadsheets. Other libraries like xlrd

allow the reading of arbitrary spreadsheet files, or they allow both reading and writing of

spreadsheet files.

pandas is, at least for some tasks, also helpful. For example, it is useful when it comes to

writing larger data sets to a spreadsheet file or when it comes to reading data stored in

such a file format.

The most powerful solution, however, at the time of this writing is the one by DataNitro

that offers a tight integration of both worlds. It has similar (or even better) spreadsheet

manipulation capabilities than other libraries. In addition, DataNitro allows us, for

example, to expose Python plots to Excel spreadsheets. More importantly, it allows us to

define user-defined Python functions (UDFs) for usage with Excel that are callable in the

same way as Excel’s built-in functions are. xlwings, a new, open source library that has

been made available recently, is similar in scope and capabilities to the DataNitro

solution.

In particular, the DataNitro and xlwings approaches allow us to use Excel as a flexible

and powerful general GUI — available on almost every computer in the finance industry

— and combine it with the analytical capabilities of Python. The best of both worlds, so to

say.