Resampling

We can resample by either:

• upsampling (moving data to a higher frequency)
• downsampling (moving data to a lower frequency)
• combinations of these e.g. first upsample and then downsample

Downsampling to a lower frequency

Polars views downsampling as a special case of the group_by operation and you can do this with group_by_dynamic and group_by_rolling - see the temporal group by page for examples.

Upsampling to a higher frequency

Let's go through an example where we generate data at 30 minute intervals:

{{code_block('user-guide/transformations/time-series/resampling','df',['DataFrame','datetime_range'])}}

--8<-- "python/user-guide/transformations/time-series/resampling.py:setup"
--8<-- "python/user-guide/transformations/time-series/resampling.py:df"

Upsampling can be done by defining the new sampling interval. By upsampling we are adding in extra rows where we do not have data. As such upsampling by itself gives a DataFrame with nulls. These nulls can then be filled with a fill strategy or interpolation.

Upsampling strategies

In this example we upsample from the original 30 minutes to 15 minutes and then use a forward strategy to replace the nulls with the previous non-null value:

{{code_block('user-guide/transformations/time-series/resampling','upsample',['upsample'])}}

--8<-- "python/user-guide/transformations/time-series/resampling.py:upsample"

In this example we instead fill the nulls by linear interpolation:

{{code_block('user-guide/transformations/time-series/resampling','upsample2',['upsample','interpolate','fill_null'])}}

--8<-- "python/user-guide/transformations/time-series/resampling.py:upsample2"