the context of the analysisin [ ]df dataframe(np random randn( )columns=[' '' '' '' ']in [ ]df dataframe(np random randn( )columns=[' '' '' ']in [ ]df out[ ] - - - - in [ ]df out[ ] - - in this caseyou can pass ignore_index=truein [ ]pd concat([df df ]ignore_index=trueout[ ] - - - - nan - nan - table - concat function arguments argument description objs list or dict of pandas objects to be concatenated the only required argument axis axis to concatenate alongdefaults to join one of 'inner''outer'defaulting to 'outer'whether to intersection (inneror union (outertogether indexes along the other axes join_axes specific indexes to use for the other - axes instead of performing union/intersection logic keys values to associate with objects being concatenatedforming hierarchical index along the concatenation axis can either be list or array of arbitrary valuesan array of tuplesor list of arrays (if multiple level arrays passed in levelslevels specific indexes to use as hierarchical index level or levels if keys passed names names for created hierarchical levels if keys and or levels passed verify_integrity check new axis in concatenated object for duplicates and raise exception if so by default (falseallows duplicates ignore_index do not preserve indexes along concatenation axisinstead producing new range(total_lengthindex combining data with overlap another data combination situation can' be expressed as either merge or concatenation operation you may have two datasets whose indexes overlap in full or part as motivating exampleconsider numpy' where functionwhich expressed vectorized if-else data wranglingcleantransformmergereshape www it-ebooks info