fetch_electricity_demand¶

def fetch_electricity_demand(
    *,
    data_home: str | os.PathLike | None = None,
    download_if_missing: bool = True,
    n_retries: int = 3,
    delay: float = 1.0,
) -> Bunch:
    """Fetch the Australian Electricity Demand dataset from Monash/Zenodo.

    5 half-hourly time series of electricity demand from five
    Australian states: New South Wales, Queensland, South Australia,
    Tasmania, and Victoria (232 272 time steps).

    Parameters
    ----------
    data_home : str, PathLike, or None
        Specify another download and cache folder for the datasets.
        By default all yohou data is stored in ``~/yohou_data/``.
    download_if_missing : bool, default=True
        If ``False``, raise an ``OSError`` if the data is not locally
        available instead of trying to download it.
    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.
    delay : float, default=1.0
        Number of seconds between retries.

    Returns
    -------
    Bunch
        Dictionary-like object with the following attributes:

        frame : pl.DataFrame
            DataFrame with ``"time"`` (Datetime) and 5 state columns
            using the ``__`` separator convention
            (e.g. ``"nsw__demand"``).
        feature_names : list of str
            Non-time column names.
        DESCR : str
            Full description of the dataset.
        frequency : str
            ``"30m"``.
        n_series : int
            ``5``.
        filename : str
            Path to the cached parquet file.

    See Also
    --------
    - [`fetch_pedestrian_counts`][yohou.datasets._fetchers.fetch_pedestrian_counts] : Hourly pedestrian sensor series.
    - [`fetch_kdd_cup`][yohou.datasets._fetchers.fetch_kdd_cup] : Hourly air quality series.
    - [`get_data_home`][yohou.datasets._fetchers.get_data_home] : Return the path of the data directory.

    References
    ----------
    [1] Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., &
        Montero-Manso, P. (2021). "Monash Time Series Forecasting Archive."
        Neural Information Processing Systems Track on Datasets and
        Benchmarks. https://doi.org/10.5281/zenodo.4659727

    Examples
    --------
    >>> from yohou.datasets import fetch_electricity_demand
    >>> bunch = fetch_electricity_demand()  # doctest: +SKIP
    >>> bunch.frame.columns[:2]  # doctest: +SKIP
    ['time', 'nsw__demand']

    """
    return _fetch_dataset(
        metadata=ELECTRICITY_DEMAND,
        dataset_name="electricity_demand",
        value_column_name="demand",
        data_home=data_home,
        download_if_missing=download_if_missing,
        n_retries=n_retries,
        delay=delay,
    )