The count() function returns column-wise count of the DataFrame.
Example here is only for count() as Time Series Aggregate function. For count() as regular aggregate, refer to count() in Regular Aggregate Mode.
Examples Prerequisite
Load the example datasets:
>>> load_example_data("dataframe", ["ocean_buoys", "ocean_buoys_seq", "ocean_buoys_nonpti"])
Example: Run count() on DataFrame created on sequenced PTI table
>>> ocean_buoys_seq = DataFrame("ocean_buoys_seq")
>>> ocean_buoys_seq.columns ['TD_TIMECODE', 'TD_SEQNO', 'buoyid', 'salinity', 'temperature', 'dates']
>>> ocean_buoys_seq
TD_TIMECODE TD_SEQNO salinity temperature dates
buoyid
44 2014-01-06 10:00:25.122200 6 55 43 2014-06-06
44 2014-01-06 10:01:25.122200 8 55 53 2014-08-08
44 2014-01-06 10:01:25.122200 20 55 54 2015-08-20
1 2014-01-06 09:01:25.122200 11 55 70 2014-11-11
1 2014-01-06 09:02:25.122200 12 55 71 2014-12-12
1 2014-01-06 09:02:25.122200 24 55 78 2015-12-24
1 2014-01-06 09:03:25.122200 13 55 72 2015-01-13
1 2014-01-06 09:03:25.122200 25 55 79 2016-01-25
1 2014-01-06 09:01:25.122200 23 55 77 2015-11-23
44 2014-01-06 10:00:26.122200 7 55 43 2014-07-07
>>> ocean_buoys_grpby1 = ocean_buoys_seq.groupby_time(timebucket_duration="2cy", value_expression="buoyid", fill="NULLS")
>>> ocean_buoys_grpby1.count().sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid count_TD_TIMECODE count_TD_SEQNO count_salinity count_temperature count_dates
0 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 0 5 5 5 4 5
1 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 1 6 6 6 6 6
2 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 2 3 3 3 3 3
3 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 22 1 1 1 1 1
4 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 44 13 13 13 13 13