another kind of data combination operation is alternatively referred to as concatenationbindingor stacking numpy has concatenate function for doing this with raw numpy arraysin [ ]arr np arange( reshape(( )in [ ]arr out[ ]array([ ] ] ]]in [ ]np concatenate([arrarr]axis= out[ ]array([ ] ] ]]in the context of pandas objects such as series and dataframehaving labeled axes enable you to further generalize array concatenation in particularyou have number of additional things to think aboutif the objects are indexed differently on the other axesshould the collection of axes be unioned or intersecteddo the groups need to be identifiable in the resulting objectdoes the concatenation axis matter at allthe concat function in pandas provides consistent way to address each of these concerns 'll give number of examples to illustrate how it works suppose we have three series with no index overlapin [ ] series([ ]index=[' '' ']in [ ] series([ ]index=[' '' '' ']in [ ] series([ ]index=[' '' ']calling concat with these object in list glues together the values and indexesin [ ]pd concat([ ]out[ ] combining and merging data sets www it-ebooks info