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pastas.timeseries.TimeSeries.update_series#

TimeSeries.update_series(force_update=False, **kwargs)[source]#

Method to update the series with new options.

Parameters
  • force_update (bool, optional) – argument that is used to force an update, even when no changes are found. Internally used by the __init__ method. Default is False.

  • freq (str, optional) – String representing the desired frequency of the time series. Must be one of the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. “7D”.

  • sample_up (str or float, optional) – String with the method to use when the frequency is increased (e.g., Weekly to daily). Possible values are: “backfill”, “bfill”, “pad”, “ffill”, “mean”, “interpolate”, “divide” or a float value to fill the gaps.

  • sample_down (str, optional) – String with the method to use when the frequency decreases (e.g., from daily to weekly values). Possible values are: “mean”, “drop”, “sum”, “min”, “max”.

  • fill_nan (str or float, optional) – Method to use when there ar nan-values in the time series. Possible values are: “mean”, “drop”, “interpolate” (default) or a float value.

  • fill_before (str or float, optional) – Method used to extend a time series before any measurements are available. possible values are: “mean” or a float value.

  • fill_after (str or float, optional) – Method used to extend a time series after any measurements are available. Possible values are: “mean” or a float value.

  • tmin (str or pandas.Timestamp, optional) – String that can be converted to, or a Pandas Timestamp with the minimum time of the series.

  • tmax (str or pandas.Timestamp, optional) – String that can be converted to, or a Pandas Timestamp with the maximum time of the series.

Return type

None

Notes

The method will validate if any of the settings is changed to determine if the series need to be updated.