adding these together yieldsin [ ] out[ ] nan nan nan the internal data alignment introduces na values in the indices that don' overlap missing values propagate in arithmetic computations in the case of dataframealignment is performed on both the rows and the columnsin [ ]df dataframe(np arange( reshape(( ))columns=list('bcd')index=['ohio''texas''colorado']in [ ]df dataframe(np arange( reshape(( ))columns=list('bde')index=['utah''ohio''texas''oregon']in [ ]df out[ ] ohio texas colorado in [ ]df out[ ] utah ohio texas oregon adding these together returns dataframe whose index and columns are the unions of the ones in each dataframein [ ]df df out[ ] colorado nan nan nan nan ohio nan nan oregon nan nan nan nan texas nan nan utah nan nan nan nan arithmetic methods with fill values in arithmetic operations between differently-indexed objectsyou might want to fill with special valuelike when an axis label is found in one object but not the otherin [ ]df dataframe(np arange( reshape(( ))columns=list('abcd')in [ ]df dataframe(np arange( reshape(( ))columns=list('abcde')in [ ]df out[ ] in [ ]df out[ ] essential functionality www it-ebooks info