pandas.DatetimeIndex frequency is None and can’t be set

You have a couple options here:

• pd.infer_freq
• pd.tseries.frequencies.to_offset

I suspect that errors down the road are caused by the missing freq.

You are absolutely right. Here’s what I use often:

def add_freq(idx, freq=None):
    """Add a frequency attribute to idx, through inference or directly.

    Returns a copy.  If `freq` is None, it is inferred.
    """

    idx = idx.copy()
    if freq is None:
        if idx.freq is None:
            freq = pd.infer_freq(idx)
        else:
            return idx
    idx.freq = pd.tseries.frequencies.to_offset(freq)
    if idx.freq is None:
        raise AttributeError('no discernible frequency found to `idx`.  Specify'
                             ' a frequency string with `freq`.')
    return idx

An example:

idx=pd.to_datetime(['2003-01-02', '2003-01-03', '2003-01-06'])  # freq=None

print(add_freq(idx))  # inferred
DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'], dtype="datetime64[ns]", freq='B')

print(add_freq(idx, freq='D'))  # explicit
DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'], dtype="datetime64[ns]", freq='D')

Using asfreq will actually reindex (fill) missing dates, so be careful of that if that’s not what you’re looking for.

The primary function for changing frequencies is the asfreq function.
For a DatetimeIndex, this is basically just a thin, but convenient
wrapper around reindex which generates a date_range and calls reindex.