The median() function returns column-wise median value of the dataframe.
- This function is valid only on columns of numeric types.
- Nulls are not included in the result computation.
Examples here are only for median() as Time Series Aggregate function. For median() as regular aggregate, refer to median() in Regular Aggregate Mode.
Examples Prerequisite
See Example Setup to set up the environment for the following examples.
To use median() as Time Series Aggregate, you must run groupby_time() first, followed by median().
>>> ocean_buoys_grpby1 = ocean_buoys.groupby_time(timebucket_duration="2cy", value_expression="buoyid", fill="NULLS")
Example: Run median() on DataFrame created on non-sequenced PTI table, for all rows
In this example, consider all rows for the columns while calculating the median value.
>>> ocean_buoys_grpby1.median().sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid median_temperature median_salinity
0 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 0 54.5 55.0
1 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 1 74.5 55.0
2 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 2 81.0 55.0
3 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 44 43.0 55.0
Example: Run median() on DataFrame created on non-sequenced PTI table, for DISTINCT rows
In this example, consider DISTINCT rows for the columns while calculating the median value.
>>> ocean_buoys_grpby1.median(distinct = True).sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid median_temperature median_salinity
0 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 0 99.0 55.0
1 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 1 74.5 55.0
2 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 2 81.0 55.0
3 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 44 54.0 55.0