Ctrl+K

rics.ml.time_split.integration.pandas#

Integration with the Pandas library.

Examples

Splitting a pandas.Series with split_pandas().

>>> import pandas as pd
>>> index = pd.date_range("2022", "2022-1-10", freq="h")
>>> series = pd.Series(range(len(index)), index=index)
>>> series.sample(3, random_state=1999)
2022-01-04 00:00:00    72
2022-01-03 21:00:00    69
2022-01-04 14:00:00    86
dtype: int64

Series may only be split on the index. The schedule keyword argument is required, but log_progress is not.

>>> for fold in split_pandas(
...     series, schedule="1d", log_progress="progress"
... ):  
...     print(
...         f"Summary of fold {tuple(map(pd.Timestamp.isoformat, fold.bounds))}:"
...         f"\n  {fold.data.mean()=}"
...         f"\n  {fold.future_data.mean()=}",
...     )
INFO:progress:Begin fold 1/2: ('2022-01-01' <= [schedule: '2022-01-08' (Saturday)] < '2022-01-09').
Summary of fold ('2022-01-01T00:00:00', '2022-01-08T00:00:00', '2022-01-09T00:00:00'):
  fold.data.mean()=83.5
  fold.future_data.mean()=179.5
INFO:progress:Finished fold 1/2 [schedule: '2022-01-08' (Saturday)] after 1ms.  
INFO:progress:Begin fold 2/2: ('2022-01-02' <= [schedule: '2022-01-09' (Sunday)] < '2022-01-10').
Summary of fold ('2022-01-02T00:00:00', '2022-01-09T00:00:00', '2022-01-10T00:00:00'):
  fold.data.mean()=107.5
  fold.future_data.mean()=203.5
INFO:progress:Finished fold 2/2 [schedule: '2022-01-09' (Sunday)] after 873μs.

When splitting dataframes, you may optionally pass a time_column argument as well. By default, both frames and series are split along the index.

Functions

split_pandas(data[, time_column, log_progress])

Split a pandas type.
split_pandas(data: PandasT, time_column: Hashable = None, *, log_progress: str | bool | dict[str, Any] | Logger | LoggerAdapter = False, **kwargs: Unpack[DatetimeIndexSplitterKwargs]) → Iterable[DatetimeSplit[PandasT]][source]#

Split a pandas type.

This function splits indexed data (i.e. Series and DataFrame, not the index itself. Use time_split.split for pandas Index types, setting available=data.index.

Parameters:

  • data – A pandas data container type to split; either Series or a DataFrame.

  • time_column – A column in data to split on. Use data`.index if None.

  • log_progress – Controls logging of fold progress. See log_split_progress() for details.

  • **kwargs – See split(). The available keyword is managed by the integration.

For more information about the schedule, before/after and flex-arguments, see the User guide.

Yields:

Tuples (data, future_data, bounds).

Raises:

TypeError – If time_column does not denote a datetime index-like field.

On this page