def datetimes_to_durations(
    start_times, end_times, fill_date=datetime.today(), freq="D", dayfirst=False, na_values=None
):
    """
    This is a very flexible function for transforming arrays of start_times and end_times
    to the proper format for lifelines: duration and event observation arrays.

    Parameters
    ----------
    start_times: an array, Series or DataFrame
        iterable representing start times. These can be strings, or datetime objects.
    end_times: an array, Series or DataFrame
        iterable representing end times. These can be strings, or datetimes. These values can be None, or an empty string, which corresponds to censorship.
    fill_date: datetime, optional (default=datetime.Today())
        the date to use if end_times is a None or empty string. This corresponds to last date
        of observation. Anything after this date is also censored.
    freq: string, optional (default='D')
        the units of time to use.  See Pandas 'freq'. Default 'D' for days.
    dayfirst: boolean, optional (default=False)
         convert assuming European-style dates, i.e. day/month/year.
    na_values : list, optional
        list of values to recognize as NA/NaN. Ex: ['', 'NaT']

    Returns
    -------
    T: numpy array
        array of floats representing the durations with time units given by freq.
    C: numpy array
        boolean array of event observations: 1 if death observed, 0 else.

    Examples
    --------
    >>> from lifelines.utils import datetimes_to_durations
    >>>
    >>> start_dates = ['2015-01-01', '2015-04-01', '2014-04-05']
    >>> end_dates = ['2016-02-02', None, '2014-05-06']
    >>>
    >>> T, E = datetimes_to_durations(start_dates, end_dates, freq="D")
    >>> T # array([ 397., 1414.,   31.])
    >>> E # array([ True, False,  True])

    """
    fill_date = pd.to_datetime(fill_date)
    freq_string = "timedelta64[%s]" % freq
    start_times = pd.Series(start_times).copy()
    end_times = pd.Series(end_times).copy()

    C = ~(pd.isnull(end_times).values | end_times.isin(na_values or [""]))
    end_times[~C] = fill_date
    start_times_ = pd.to_datetime(start_times, dayfirst=dayfirst)
    end_times_ = pd.to_datetime(end_times, dayfirst=dayfirst, errors="coerce")

    deaths_after_cutoff = end_times_ > fill_date
    C[deaths_after_cutoff] = False

    T = (end_times_ - start_times_).values.astype(freq_string).astype(float)
    if (T < 0).sum():
        warnings.warn("Warning: some values of start_times are after end_times")
    return T, C.values