one of the most time-consuming issues in working with financial data is the so-called data alignment problem two related time series may have indexes that don' line up perfectlyor two dataframe objects might have columns or row labels that don' match users of matlabrand other matrix-programming languages often invest significant effort in wrangling data into perfectly aligned forms in my experiencehaving to align data by hand (and worsehaving to verify that data is alignedis far too rigid and tedious way to work it is also rife with potential for bugs due to combining misaligned data pandas take an alternate approach by automatically aligning data in arithmetic operations in practicethis grants immense freedom and enhances your productivity as an examplelet' consider couple of dataframes containing time series of stock prices and volumein [ ]prices out[ ]aapl in [ ]volume out[ ]jnj aapl spx jnj xom xom suppose you wanted to compute volume-weighted average price using all available data (and making the simplifying assumption that the volume data is subset of the price datasince pandas aligns the data automatically in arithmetic and excludes missing data in functions like sumwe can express this concisely asin [ ]prices volume out[ ]aapl jnj nan nan nan nan spx nan nan nan nan nan nan nan xom nan nan in [ ]vwap (prices volumesum(volume sum( financial and economic data applications www it-ebooks info