pastas.timeseries.TimeSeries

class pastas.timeseries.TimeSeries(series: pandas.Series, name: str | None = None, settings: str | pastas.typing.OseriesSettingsDict | pastas.typing.StressSettingsDict | None = None, metadata: dict | None = None)

Class that deals with all user-provided time series.

Parameters:

• series (pandas.Series) – pandas.Series with pandas.DatetimeIndex.

• name (str, optional) – String with the name of the time series, if None is provided, pastas will try to derive the name from the series.

• settings (Time series) – The settings of the stress. This can be a string referring to a predefined settings dictionary (defined in ps.rcParams[“timeseries”]), or a dictionary with the settings to apply. For more information refer to Time series settings section below.

• metadata (dict, optional) – Dictionary with metadata of the time series.

• settings

• fill_nan ({"drop", "mean", "interpolate"} or float) –

  Method for filling NaNs.

  • drop: drop NaNs from time series

  • mean: fill NaNs with mean value of time series

  • interpolate: fill NaNs by interpolating between finite values

  • float: fill NaN with provided value, e.g. 0.0

• fill_before ({"mean", "bfill"} or float) –

  Method for extending time series into past.

  • mean: extend time series into past with mean value of time series

  • bfill: extend time series into past by back-filling first value

  • float: extend time series into past with provided value, e.g. 0.0

• fill_after ({"mean", "ffill"} or float) –

  Method for extending time series into future.

  • mean: extend time series into future with mean value of time series

  • ffill: extend time series into future by forward-filling last value

  • float: extend time series into future with provided value, e.g. 0.0

• sample_up ({"mean", "interpolate", "divide"} or float) –

  Method for up-sampling time series (increasing frequency, e.g. going from weekly to daily values).

  • bfill or backfill: fill up-sampled time steps by back-filling current values

  • ffill or pad: fill up-sampled time steps by forward-filling current values

  • mean: fill up-sampled time steps with mean of timeseries

  • interpolate: fill up-sampled time steps by interpolating between current values

  • divide: fill up-sampled steps with current value divided by length of current time steps (i.e. spread value over new time steps).

• sample_down ({"mean", "drop", "sum", "min", "max"}) –

  Method for down-sampling time series (decreasing frequency, e.g. going from daily to weekly values).

  • mean: resample time series by taking the mean

  • drop: resample the time series by taking the mean, dropping any NaN-values

  • sum: resample time series by summing values

  • max: resample time series with maximum value

  • min: resample time series with minimum value

Returns:

series – Returns a pastas.TimeSeries object.

Return type:

pastas.TimeSeries

Examples

To obtain the predefined TimeSeries settings, you can run the following line of code:

>>> ps.rcParams["timeseries"]

See also

pastas.timeseries.TimeSeries.update_series

For the individual options for the different settings.

Methods

update_series([force_update])	Method to update the series with new options.
to_dict(→ dict)	Method to export the Time Series to a json format.
copy(→ Self)	Method to copy a TimeSeries.