visualizing data how many to dump loaded messages subjects senders top email list participants steve swinsburg@gmail com azeckoski@unicon net ieb@tfd co uk csev@umich edu david horwitz@uct ac za top email list organizations gmail com umich edu uct ac za indiana edu unicon net note how much more quickly gbasic py runs compared to gmane py or even gmodel py they are all working on the same databut gbasic py is using the compressed and normalized data in index sqlite if you have lot of data to managea multistep process like the one in this application may take little longer to developbut will save you lot of time when you really start to explore and visualize your data you can produce simple visualization of the word frequency in the subject lines in the file gword pyrange of counts output written to gword js this produces the file gword js which you can visualize using gword htm to produce word cloud similar to the one at the beginning of this section second visualization is produced by gline py it computes email participation by organizations over time loaded messages subjects senders top oranizations ['gmail com''umich edu''uct ac za''indiana edu''unicon net''tfd co uk''berkeley edu''longsight com''stanford edu''ox ac uk'output written to gline js its output is written to gline js which is visualized using gline htm this is relatively complex and sophisticated application and has features to do some real data retrievalcleaningand visualization