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  1. [
  2.   {
  3.     "group": { "id": "01234" },
  4.     "measures": {
  5.       "measures": {
  6.         "...device 1 uuid...": {
  7.           "metric.name.here": {
  8.             "mean": [
  9.               ["2019-04-17T14:30:00+00:00", 300, 1],
  10.               ["2019-04-17T14:35:00+00:00", 300, 2],
  11.               ...
  12.             ]
  13.           }
  14.         },
  15.         "...device 2 uuid...": {
  16.           "metric.name.here": {
  17.             "mean": [
  18.               ["2019-04-17T14:30:00+00:00", 300, 0],
  19.               ["2019-04-17T14:35:00+00:00", 300, 1],
  20.               ...
  21.             ]
  22.           }
  23.         }
  24.       }
  25.     }
  26.   }
  27. ]
  28.      
  29. ["2019-04-17T14:30:00+00:00", 300, 1],
  30. ["2019-04-17T14:35:00+00:00", 300, 3],
  31.      
  32. with open('data.json') as fd:
  33.   data = pd.read_json(fd)
  34.  
  35. for i, group in enumerate(data.group):
  36.     project = group['project_id']
  37.     instances = data.measures[i]['measures']
  38.     series_for_group = []
  39.     for instance in instances.keys():
  40.         measures = instances[instance][metric][aggregate]
  41.  
  42.         # build an index from the timestamps
  43.         index = pd.DatetimeIndex(measure[0] for measure in measures)
  44.  
  45.         # extract values from the data and link it to the index
  46.         series = pd.Series((measure[2] for measure in measures),
  47.                            index=index)
  48.  
  49.         series_for_group.append(series)
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