financial and economic data applications the use of python in the financial industry has been increasing rapidly since led largely by the maturation of libraries (like numpy and pandasand the availability of skilled python programmers institutions have found that python is well-suited both as an interactive analysis environment as well as enabling robust systems to be developed often in fraction of the time it would have taken in java or +python is also an ideal glue layerit is easy to build python interfaces to legacy libraries built in or +while the field of financial analysis is broad enough to fill an entire booki hope to show you how the tools in this book can be applied to number of specific problems in finance as with other research and analysis domainstoo much programming effort is often spent wrangling data rather than solving the core modeling and research problems personally got started building pandas in while grappling with inadequate data tools in these examplesi'll use the term cross-section to refer to data at fixed point in time for examplethe closing prices of all the stocks in the & index on particular date form cross-section cross-sectional data at multiple points in time over multiple data items (for exampleprices together with volumeform panel panel data can either be represented as hierarchically-indexed dataframe or using the three-dimensional panel pandas object data munging topics many helpful data munging tools for financial applications are spread across the earlier here 'll highlight number of topics as they relate to this problem domain www it-ebooks info