the result of the join is to create extra-long "metarowswhich have both the fields from people and the matching fields from follows where there is more than one match between the id field from people and the from_id from peoplethen join creates metarow for each of the matching pairs of rowsduplicating data as needed the following code demonstrates the data that we will have in the database after the multi-table twitter spider program (abovehas been run several times import sqlite conn sqlite connect('friends sqlite'cur conn cursor(cur execute('select from people'count print('people:'for row in curif count print(rowcount count print(count'rows 'cur execute('select from follows'count print('follows:'for row in curif count print(rowcount count print(count'rows 'cur execute('''select from follows join people on follows to_id people id where follows from_id '''count print('connections for id= :'for row in curif count print(rowcount count print(count'rows 'cur close(codein this programwe first dump out the people and follows and then dump out subset of the data in the tables joined together here is the output of the programpython twjoin py people