The resample() function resamples time series data to group the same by time on a datetime column of a teradataml DataFrame. It also allows grouping done based on teradataml DataFrame columns.
This function applies Group By Time to one or more columns of a teradataml DataFrame. Outcome of this function can be used to run Time Series Aggregate functions.
Although the grouping is optimized for DataFrames created for PTI tables, it is also supported on non-PTI tables when the argument 'timecode_column' is specified.
- This API is similar to groupby_time().
- You can still apply teradataml DataFrame methods (filters/sort/etc) on top of the result of this one.
- Consecutive operations of grouping, i.e., groupby_time(), resample() and groupby() are not permitted. An exception will be raised. Following are some cases where exception will be raised as "Invalid operation applied, check documentation for correct usage."
- df.resample().groupby()
- df.resample().resample()
- df.resample().groupby_time()
Examples Prerequisite
See Example Setup to set up the environment for the following examples.
Example 1: Group by timebucket of two calendar years, on DataFrame created on non-sequenced PTI table
Use formal notation and 'buoyid' column on DataFrame created on non-sequenced PTI table.
Fill missing values with Nulls.
>>> ocean_buoys_grpby1 = ocean_buoys.resample(rule="CAL_YEARS(2)", value_expression="buoyid", fill_method="NULLS")
>>> number_of_values_to_column = {2: "temperature"}
>>> ocean_buoys_grpby1.bottom(number_of_values_to_column).sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(CAL_YEARS(2)) buoyid bottom2temperature
0 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 0 10
1 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 0 10
2 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 1 71
3 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 1 70
4 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 2 80
5 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 2 81
6 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 44 43
7 ('2014-01-01 00:00:00.000000-00:00', '2016-01-... 2 44 43
Example 2: Group by timebucket of two minutes, on DataFrame created on non-PTI table
Use shorthand notation to specify timebucket, on DataFrame created on non-PTI table.
Fill missing values with Nulls.
Time column must be specified for non-PTI table to 'on' argument.
>>> ocean_buoys_nonpti_grpby2 = ocean_buoys_nonpti.resample(rule="2m", value_expression="buoyid", on="timecode", fill_method="NULLS")
>>> number_of_values_to_column = {2: "temperature"}
>>> ocean_buoys_nonpti_grpby2.bottom(number_of_values_to_column, with_ties=True).sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(MINUTES(2)) buoyid bottom_with_ties2temperature
0 ('2014-01-06 08:00:00.000000+00:00', '2014-01-... 11574961 0 10.0
1 ('2014-01-06 08:02:00.000000+00:00', '2014-01-... 11574962 0 NaN
2 ('2014-01-06 08:04:00.000000+00:00', '2014-01-... 11574963 0 NaN
3 ('2014-01-06 08:06:00.000000+00:00', '2014-01-... 11574964 0 NaN
4 ('2014-01-06 08:08:00.000000+00:00', '2014-01-... 11574965 0 99.0
5 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 100.0
6 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 10.0
7 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 70.0
8 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 77.0
9 ('2014-01-06 09:02:00.000000+00:00', '2014-01-... 11574992 1 71.0
Example 3: Group by timebucket of two minutes, on DataFrame created on non-PTI table
Use shorthand notation to specify timebucket, on DataFrame created on non-PTI table.
Fill missing values with previous values.
Time column must be specified for non-PTI table to 'on' argument.
>>> ocean_buoys_nonpti_grpby2 = ocean_buoys_nonpti.resample(rule="2m", value_expression="buoyid", on="timecode", fill_method="prev")
>>> number_of_values_to_column = {2: "temperature"}
>>> ocean_buoys_nonpti_grpby2.bottom(number_of_values_to_column, with_ties=True).sort(["TIMECODE_RANGE","buoyid"])
TIMECODE_RANGE GROUP BY TIME(MINUTES(2)) buoyid bottom_with_ties2temperature
0 ('2014-01-06 08:00:00.000000+00:00', '2014-01-... 11574961 0 10
1 ('2014-01-06 08:02:00.000000+00:00', '2014-01-... 11574962 0 10
2 ('2014-01-06 08:04:00.000000+00:00', '2014-01-... 11574963 0 10
3 ('2014-01-06 08:06:00.000000+00:00', '2014-01-... 11574964 0 10
4 ('2014-01-06 08:08:00.000000+00:00', '2014-01-... 11574965 0 99
5 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 10
6 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 100
7 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 77
8 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 70
9 ('2014-01-06 09:02:00.000000+00:00', '2014-01-... 11574992 1 71
Example 4: Group by timebucket of two minutes, on DataFrame created on non-PTI table
Use shorthand notation to specify timebucket, on DataFrame created on non-PTI table.
Fill missing values with numeric constant 12345.
Time column must be specified for non-PTI table to 'on' argument.
>>> ocean_buoys_nonpti_grpby2 = ocean_buoys_nonpti.resample(rule="2minute", value_expression="buoyid", on="timecode", fill_method=12345)
>>> number_of_values_to_column = {2: "temperature"}
>>> ocean_buoys_nonpti_grpby2.bottom(number_of_values_to_column, with_ties=True).sort(["TIMECODE_RANGE", "buoyid"])
TIMECODE_RANGE GROUP BY TIME(MINUTES(2)) buoyid bottom_with_ties2temperature
0 ('2014-01-06 08:00:00.000000+00:00', '2014-01-... 11574961 0 10
1 ('2014-01-06 08:02:00.000000+00:00', '2014-01-... 11574962 0 12345
2 ('2014-01-06 08:04:00.000000+00:00', '2014-01-... 11574963 0 12345
3 ('2014-01-06 08:06:00.000000+00:00', '2014-01-... 11574964 0 12345
4 ('2014-01-06 08:08:00.000000+00:00', '2014-01-... 11574965 0 99
5 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 10
6 ('2014-01-06 08:10:00.000000+00:00', '2014-01-... 11574966 0 100
7 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 77
8 ('2014-01-06 09:00:00.000000+00:00', '2014-01-... 11574991 1 70
9 ('2014-01-06 09:02:00.000000+00:00', '2014-01-... 11574992 1 71