energy_analysis_toolbox.timeseries.create.from_intervals module#

Module converting tabular representations of interval data into timeseries.

energy_analysis_toolbox.timeseries.create.from_intervals.flatten_and_fill(data: DataFrame, fill_values: dict | None = None, start_f: str = 'start', end_f: str = 'end', time_f: str = 'timestamp') → DataFrame[source]#

Return data as a table of timeseries.

Parameters:

  • data (pd.DataFrame) – A table of overconsumption which are each defined by a start_f, used as index, and a end_f.

  • fill_values (dict or None) – A dict where keys are names of columns of data and values are used to fill the duration between the overconsumption for each of the column in the dict. Missing column receive np.nan. Default is None which is treated as an empty dict.

  • start_f (str, default EATK.start_f) – The name of the column defining interval starts.

  • end_f (str, default EATK.end_) – The name of the column defining interval ends.

  • time_f (str, default EATK.end_) – The name of the index of the returned Dataframe.

Returns:

pd.DataFrame – A table describing the input data as a timeseries. The dataframe is indexed with the times of the interval starts and ends, such that the rows indexed with an interval end in the dataframe contains only filler values.

Note

An empty table is considered to be flattened as an empty table.

Warning

The function assumes that the overconsumption are correctly defined meaning that :

  • no interval overlap one each other,

  • overconsumption have no common boundary.

The function does not check these assumptions.

Example

Consider e.g. the following dataframe (from the function tests).

>>> table
                                    end col_str  col_float
timestamp
2022-12-05 18:00:00 2022-12-05 18:05:00    toto          0
2022-12-05 18:10:00 2022-12-05 18:15:00    toto          2
2022-12-05 18:20:00 2022-12-05 18:25:00    toto          4

It can be flattened as follows :

>>> flatten_and_fill(table)
                    col_str  col_float
timestamp
2022-12-05 18:00:00    toto        0.0
2022-12-05 18:05:00     NaN        NaN
2022-12-05 18:10:00    toto        2.0
2022-12-05 18:15:00     NaN        NaN
2022-12-05 18:20:00    toto        4.0
2022-12-05 18:25:00     NaN        NaN

The nan values can be filled with fixed values for each column during the flattening process :

>>> flatten_and_fill(table, fill_values={'col_str': 'tata', 'col_float': 0})
                    col_str  col_float
timestamp
2022-12-05 18:00:00    toto          0
2022-12-05 18:05:00    tata          0
2022-12-05 18:10:00    toto          2
2022-12-05 18:15:00    tata          0
2022-12-05 18:20:00    toto          4
2022-12-05 18:25:00    tata          0