colorado colorado utah new york this might seem inconsistent to some readersbut this syntax arose out of practicality and nothing more another use case is in indexing with boolean dataframesuch as one produced by scalar comparisonin [ ]data out[ ]one two ohio true true colorado true false utah false false new york false false three true false false false four true false false false in [ ]data[data in [ ]data out[ ]one two ohio colorado utah new york three four this is intended to make dataframe syntactically more like an ndarray in this case for dataframe label-indexing on the rowsi introduce the special indexing field ix it enables you to select subset of the rows and columns from dataframe with numpylike notation plus axis labels as mentioned earlierthis is also less verbose way to do reindexingin [ ]data ix['colorado'['two''three']out[ ]two three namecolorado in [ ]data ix[['colorado''utah'][ ]out[ ]four one two colorado utah in [ ]data ix[ out[ ]one two three four nameutah in [ ]data ix[:'utah''two'out[ ]ohio colorado utah nametwo in [ ]data ix[data three : out[ ]essential functionality www it-ebooks info