Python for Finance: Analyze Big Financial Data

Conclusions

Financial time series data is one of the most common and important forms of data in

finance. The library pandas is generally the tool of choice when it comes to working with

such data sets. Modeled after the data.frame class of R, the pandas DataFrame class

provides a wealth of attributes and methods to attack almost any kind of (financial)

analytics problem you might face. Convenience is another benefit of using pandas: even if

you might be able to generate the same result by using NumPy and/or matplotlib only,

pandas generally has some neat shortcuts based on a powerful and flexible API.

In addition, pandas makes it really easy to retrieve data from a variety of web sources, like

Yahoo! Finance or Google. Compared to “pure” NumPy or matplotlib, it automates the

management of financial time series data in many respects and also provides higher

flexibility when it comes to combining data sets and enlarging existing ones.