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

Question

I created a DatetimeIndex from a "date" column:

sales.index = pd.DatetimeIndex(sales["date"]) 

Now the index looks as follows:

DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-04', '2003-01-06',                    '2003-01-07', '2003-01-08', '2003-01-09', '2003-01-10',                    '2003-01-11', '2003-01-13',                    ...                    '2016-07-22', '2016-07-23', '2016-07-24', '2016-07-25',                    '2016-07-26', '2016-07-27', '2016-07-28', '2016-07-29',                    '2016-07-30', '2016-07-31'],                   dtype='datetime64[ns]', name='date', length=4393, freq=None) 

As you see, the freq attribute is None. I suspect that errors down the road are caused by the missing freq. However, if I try to set the frequency explicitly:

--------------------------------------------------------------------------- ValueError                                Traceback (most recent call last) <ipython-input-148-30857144de81> in <module>()       1 #### DEBUG ----> 2 sales_train = disentangle(df_train)       3 sales_holdout = disentangle(df_holdout)       4 result = sarima_fit_predict(sales_train.loc[5002, 9990]["amount_sold"], sales_holdout.loc[5002, 9990]["amount_sold"])  <ipython-input-147-08b4c4ecdea3> in disentangle(df_train)       2     # transform sales table to disentangle sales time series       3     sales = df_train[["date", "store_id", "article_id", "amount_sold"]] ----> 4     sales.index = pd.DatetimeIndex(sales["date"], freq="d")       5     sales = sales.pivot_table(index=["store_id", "article_id", "date"])       6     return sales  /usr/local/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)      89                 else:      90                     kwargs[new_arg_name] = new_arg_value ---> 91             return func(*args, **kwargs)      92         return wrapper      93     return _deprecate_kwarg  /usr/local/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)     399                                          'dates does not conform to passed '     400                                          'frequency {1}' --> 401                                          .format(inferred, freq.freqstr))     402      403         if freq_infer:  ValueError: Inferred frequency None from passed dates does not conform to passed frequency D 

So apparently a frequency has been inferred, but is stored neither in the freq nor inferred_freq attribute of the DatetimeIndex - both are None. Can someone clear up the confusion?

Answer 1

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.

Answer 2

It seems to relate to missing dates as 3kt notes. You might be able to "fix" with asfreq('D') as EdChum suggests but that gives you a continuous index with missing data values. It works fine for some some sample data I made up:

df=pd.DataFrame({ 'x':[1,2,4] },     index=pd.to_datetime(['2003-01-02', '2003-01-03', '2003-01-06']) )  df Out[756]:              x 2003-01-02  1 2003-01-03  2 2003-01-06  4  df.index Out[757]: DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06'],            dtype='datetime64[ns]', freq=None) 

Note that freq=None. If you apply asfreq('D'), this changes to freq='D':

df.asfreq('D') Out[758]:                x 2003-01-02  1.0 2003-01-03  2.0 2003-01-04  NaN 2003-01-05  NaN 2003-01-06  4.0  df.asfreq('d').index Out[759]:  DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-04', '2003-01-05',                '2003-01-06'],               dtype='datetime64[ns]', freq='D') 

More generally, and depending on what exactly you are trying to do, you might want to check out the following for other options like reindex & resample: Add missing dates to pandas dataframe