reconnect moved files to git repo

venv/lib/python3.11/site-packages/pmdarima/datasets/gasoline.py

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+# -*- coding: utf-8 -*-
+
+import pandas as pd
+
+from ..compat.numpy import DTYPE
+from ._base import fetch_from_web_or_disk
+
+__all__ = [
+    'load_gasoline'
+]
+
+url = 'http://alkaline-ml.com/datasets/gasoline.csv'
+
+
+def load_gasoline(as_series=False, dtype=DTYPE):
+    """Weekly US finished motor gasoline products
+
+    A weekly time series of US finished motor gasoline products supplied (in
+    thousands of barrels per day) from February 1991 to May 2005.
+
+    Parameters
+    ----------
+    as_series : bool, optional (default=False)
+        Whether to return a Pandas series. If True, the index will be set to
+        the observed years/months. If False, will return a 1d numpy array.
+
+    dtype : type, optional (default=np.float64)
+        The type to return for the array. Default is np.float64, which is used
+        throughout the package as the default type.
+
+    Notes
+    -----
+    The seasonal periodicity of this example is rather difficult, since it's
+    not an integer. To be exact, the periodicity is ``365.25 / 7``
+    (~=52.1785714285714). To fit the best possible model to this data, you'll
+    need to explore using exogenous features
+
+    See Also
+    --------
+    :class:`pmdarima.preprocessing.exog.FourierFeaturizer`
+
+    Examples
+    --------
+    >>> from pmdarima.datasets import load_gasoline
+    >>> load_gasoline()
+    array([6621. , 6433. , 6582. , ..., 9024. , 9175. , 9269. ])
+
+    >>> load_gasoline(True).head()
+    0    6621.0
+    1    6433.0
+    2    6582.0
+    3    7224.0
+    4    6875.0
+    dtype: float64
+
+    References
+    ----------
+    .. [1] http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=wgfupus2&f=W
+    .. [2] https://robjhyndman.com/hyndsight/forecasting-weekly-data/
+
+    Returns
+    -------
+    rslt : array-like, shape=(n_samples,)
+        The gasoline dataset. There are 745 examples.
+    """  # noqa
+    rslt = fetch_from_web_or_disk(url, 'gasoline', cache=True).astype(dtype)
+    if not as_series:
+        return rslt
+
+    return pd.Series(rslt)