'to_user_name'nonewe can then make list of the tweet fields of interest then pass the results list to dataframein [ ]tweet_fields ['created_at''from_user''id''text'in [ ]tweets dataframe(data['results']columns=tweet_fieldsin [ ]tweets out[ ]int index entries to data columnscreated_at non-null values from_user non-null values id non-null values text non-null values dtypesint ( )object( each row in the dataframe now has the extracted data from each tweetin [ ]tweets ix[ out[ ]created_at thu jul : : + from_user deblike id text pandaspowerful python data analysis toolkit name with bit of elbow greaseyou can create some higher-level interfaces to common web apis that return dataframe objects for easy analysis interacting with databases in many applications data rarely comes from text filesthat being fairly inefficient way to store large amounts of data sql-based relational databases (such as sql serverpostgresqland mysqlare in wide useand many alternative non-sql (so-called nosqldatabases have become quite popular the choice of database is usually dependent on the performancedata integrityand scalability needs of an application loading data from sql into dataframe is fairly straightforwardand pandas has some functions to simplify the process as an examplei'll use an in-memory sqlite database using python' built-in sqlite driverimport sqlite query ""create table test ( varchar( ) varchar( ) reald integer );"" data loadingstorageand file formats www it-ebooks info