timeatlas.time_series.TimeSeries.group_by

TimeSeries.group_by(freq: str, method: Optional[str] = 'mean') → timeatlas.time_series.time_series.TimeSeries

Groups values by a frequency.

This method is quite similar to resample with the difference that it gives the guaranty that the timestamps are full values. e.g. 2019-01-01 08:00:00.

Resample could make values spaced by 1 min but every x sec e.g. [2019-01-01 08:00:33, 2019-01-01 08:01:33], which isn’t convenient for further index merging operations.

The function has different aggregations methods taken from Pandas groupby aggregations[1]. By default, it’ll take the mean of the defined freq bucket.

[1] https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#aggregation

Parameters

• freq – string offset alias of a frequency

• method – string of the Pandas aggregation function.

Returns

TimeSeries

timeatlas.time_series.TimeSeries.resample timeatlas.time_series.TimeSeries.interpolate