--- a/scripts/process.py	Wed Jul 11 15:29:44 2018 -0400
+++ b/scripts/process.py	Wed Jul 11 17:31:26 2018 -0400
@@ -280,7 +280,7 @@
     data = pd.read_csv(args.eventFilename, parse_dates = [2])
     #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000)
     # create time for end of each 15 min, then group by, using the agg method for each data column
-    headers = ['sites', 'date', 'intervalend15', 'duration', 'count']
+    headers = ['site', 'date', 'intervalend15', 'duration', 'count']
     aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles)
     dataColumns = list(data.columns[4:])
     for h in dataColumns: