| import re; | |
| handler = open('instru.txt', 'r') | |
| contador=0 | |
| acciones = 0 | |
| # create the 1000*1000 matrix | |
| light_park = [] | |
| dim = [] | |
| for i in xrange(1000): | |
| dim = [] |
| import re | |
| d2 = 0 | |
| cumul_raw = 0 | |
| with open('instru.txt', 'r') as f: | |
| for line in f: | |
| line=line.rstrip() | |
| d2+=4 |
Data Story
Due to a new entrepreneurial adventure related to airlines passengers experiences, I wanted to have an insight on traffic departure delays.
In that regard, I took the data from the US “Research and Innovative Technology Administration ” (RITA) data base From a customer point of view, I did not care about the different types of delays (CarrierDelay, WeatherDelay, NASDelay, SecurityDelay, LateAircraftDelay) so I aggregated (by sum) all of them into one variable : TotalDalay.
| [ | |
| { | |
| "spent_at": "2017-08-11T12:23:07+00:00", | |
| "read_at": "2017-08-11T10:57:42+00:00", | |
| "flight": "AA1134", | |
| "created_at": "2017-08-11T10:36:53+00:00", | |
| "terminal_id": "EZE2", | |
| "token": "b14i4ok", | |
| "store_proposal": [], | |
| "airline_id": "Air Korea", |
| [ | |
| { | |
| "status": "success", | |
| "flight": "AA1134", | |
| "sender": "160.48.199.149", | |
| "created_at": "2017-08-11T12:23:07+00:00", | |
| "terminal_id": "EZE2", | |
| "token_voucher": "b14i4ok", | |
| "checkout_id": "EZE2_Paul_1", | |
| "airline_id": "Air Korea", |
| [['company', req.params.message.toString()]] | |
| [{'company': req.params.message.toString()}] | |
| ["fields": {"company": req.params.message.toString()}}] | |
| [{Company:req.params.message.toString()}] | |
| // tested and works fine | |
| ML.Subscribers.addSubscriber(9301656,req.params.email.toString(),req.params.name.toString(),[],1).then( | |
| function(data) { | |
| console.log(data); | |
| res.send(data); |
