Function infer_freq

xarray/coding/frequencies.py:61–104

Infer the most likely frequency given the input index. Parameters ---------- index : CFTimeIndex, DataArray, DatetimeIndex, TimedeltaIndex, Series If not passed a CFTimeIndex, this simply calls `pandas.infer_freq`. If passed a Series or a DataArray will use the valu

(index)

def infer_freq(index):
    """
    Infer the most likely frequency given the input index.

    Parameters
    ----------
    index : CFTimeIndex, DataArray, DatetimeIndex, TimedeltaIndex, Series
        If not passed a CFTimeIndex, this simply calls `pandas.infer_freq`.
        If passed a Series or a DataArray will use the values of the series (NOT THE INDEX).

    Returns
    -------
    str or None
        None if no discernible frequency.

    Raises
    ------
    TypeError
        If the index is not datetime-like.
    ValueError
        If there are fewer than three values or the index is not 1D.
    """
    from xarray.core.dataarray import DataArray
    from xarray.core.variable import Variable

    if isinstance(index, DataArray | pd.Series):
        if index.ndim != 1:
            raise ValueError("'index' must be 1D")
        elif not _contains_datetime_like_objects(Variable("dim", index)):
            raise ValueError("'index' must contain datetime-like objects")
        dtype = np.asarray(index).dtype

        if _is_numpy_subdtype(dtype, "datetime64"):
            index = pd.DatetimeIndex(index.values)
        elif _is_numpy_subdtype(dtype, "timedelta64"):
            index = pd.TimedeltaIndex(index.values)
        else:
            index = CFTimeIndex(index.values)

    if isinstance(index, CFTimeIndex):
        inferer = _CFTimeFrequencyInferer(index)
        return inferer.get_freq()

    return _legacy_to_new_freq(pd.infer_freq(index))


class _CFTimeFrequencyInferer:  # (pd.tseries.frequencies._FrequencyInferer):