Examples for Time Series Aggregates functions share same steps to set up the environment.
The following lists the procedure to load the example datasets and create required DataFrames to prepare for the examples of Time Series Aggregate functions.
- Load the example datasets
>>> load_example_data("dataframe", ["ocean_buoys", "ocean_buoys_seq", "ocean_buoys_nonpti"]) - Create required DataFrames:
- DataFrame on non-sequenced PTI table
>>> ocean_buoys = DataFrame("ocean_buoys")Check the DataFrame columns:
>>>ocean_buoys.columns ['buoyid', 'TD_TIMECODE', 'temperature', 'salinity']
Check the head of the DataFrame:
>>> ocean_buoys.head() TD_TIMECODE temperature salinity buoyid 0 2014-01-06 08:10:00.000000 100.0 55 0 2014-01-06 08:08:59.999999 NaN 55 1 2014-01-06 09:01:25.122200 77.0 55 1 2014-01-06 09:03:25.122200 79.0 55 1 2014-01-06 09:01:25.122200 70.0 55 1 2014-01-06 09:02:25.122200 71.0 55 1 2014-01-06 09:03:25.122200 72.0 55 0 2014-01-06 08:09:59.999999 99.0 55 0 2014-01-06 08:00:00.000000 10.0 55 0 2014-01-06 08:10:00.000000 10.0 55 - DataFrame on sequenced PTI table
>>> ocean_buoys_seq = DataFrame("ocean_buoys_seq")Check the DataFrame columns:
>>> ocean_buoys_seq.columns ['TD_TIMECODE', 'TD_SEQNO', 'buoyid', 'salinity', 'temperature', 'dates']
Check the head of the DataFrame:
>>> ocean_buoys_seq.head() TD_TIMECODE TD_SEQNO salinity temperature dates buoyid 0 2014-01-06 08:00:00.000000 26 55 10.0 2016-02-26 0 2014-01-06 08:08:59.999999 18 55 NaN 2015-06-18 1 2014-01-06 09:02:25.122200 24 55 78.0 2015-12-24 1 2014-01-06 09:01:25.122200 23 55 77.0 2015-11-23 1 2014-01-06 09:02:25.122200 12 55 71.0 2014-12-12 1 2014-01-06 09:03:25.122200 13 55 72.0 2015-01-13 1 2014-01-06 09:01:25.122200 11 55 70.0 2014-11-11 0 2014-01-06 08:10:00.000000 19 55 10.0 2015-07-19 0 2014-01-06 08:09:59.999999 17 55 99.0 2015-05-17 0 2014-01-06 08:10:00.000000 27 55 100.0 2016-03-27 - DataFrame on non-PTI table
>>> ocean_buoys_nonpti = DataFrame("ocean_buoys_nonpti")Check the DataFrame columns:
>>> ocean_buoys_nonpti.columns ['buoyid', 'timecode', 'temperature', 'salinity']
Check the head of the DataFrame:
>>> ocean_buoys_nonpti.head() buoyid temperature salinity timecode 2014-01-06 08:09:59.999999 0 99.0 55 2014-01-06 08:10:00.000000 0 10.0 55 2014-01-06 09:01:25.122200 1 70.0 55 2014-01-06 09:01:25.122200 1 77.0 55 2014-01-06 09:02:25.122200 1 71.0 55 2014-01-06 09:03:25.122200 1 72.0 55 2014-01-06 09:02:25.122200 1 78.0 55 2014-01-06 08:10:00.000000 0 100.0 55 2014-01-06 08:08:59.999999 0 NaN 55 2014-01-06 08:00:00.000000 0 10.0 55
- DataFrame on non-sequenced PTI table