In this talk, I will introduce the pandas library  (http://pandas.pydata.org), a rich, high performance Python-language data analysis toolkit especially well suited for common financial data analysis and statistical computing tasks. I will exhibit a variety of common data manipulation challenges and how these can be solved using the library.

I will also preview new, enhanced time series data functionality that is currently under active development. Background on the scientific Python ecosystem and the origins of the pandas library will also be provided.