frequencies.py 文件源码

def infer_freq(index, warn=True):
    """
    Infer the most likely frequency given the input index. If the frequency is
    uncertain, a warning will be printed.

    Parameters
    ----------
    index : DatetimeIndex or TimedeltaIndex
      if passed a Series will use the values of the series (NOT THE INDEX)
    warn : boolean, default True

    Returns
    -------
    freq : string or None
        None if no discernible frequency
        TypeError if the index is not datetime-like
        ValueError if there are less than three values.
    """
    import pandas as pd

    if isinstance(index, com.ABCSeries):
        values = index._values
        if not (com.is_datetime64_dtype(values) or
                com.is_timedelta64_dtype(values) or
                values.dtype == object):
            raise TypeError("cannot infer freq from a non-convertible "
                            "dtype on a Series of {0}".format(index.dtype))
        index = values

    if com.is_period_arraylike(index):
        raise TypeError("PeriodIndex given. Check the `freq` attribute "
                        "instead of using infer_freq.")
    elif isinstance(index, pd.TimedeltaIndex):
        inferer = _TimedeltaFrequencyInferer(index, warn=warn)
        return inferer.get_freq()

    if isinstance(index, pd.Index) and not isinstance(index, pd.DatetimeIndex):
        if isinstance(index, (pd.Int64Index, pd.Float64Index)):
            raise TypeError("cannot infer freq from a non-convertible index "
                            "type {0}".format(type(index)))
        index = index.values

    if not isinstance(index, pd.DatetimeIndex):
        try:
            index = pd.DatetimeIndex(index)
        except AmbiguousTimeError:
            index = pd.DatetimeIndex(index.asi8)

    inferer = _FrequencyInferer(index, warn=warn)
    return inferer.get_freq()