How to set frequency with pd.to_datetime()?

Question

When fitting a statsmodel, I'm receiving a warning about the date frequency.

First, I import a dataset:

import statsmodels as sm
df = sm.datasets.get_rdataset(package='datasets', dataname='airquality').data

df['Year'] = 1973
df['Date'] = pd.to_datetime(df[['Year', 'Month', 'Day']])

df.drop(columns=['Year', 'Month', 'Day'], inplace=True)
df.set_index('Date', inplace=True, drop=True)

Next I try to fit a SES model:

fit = sm.tsa.api.SimpleExpSmoothing(df['Wind']).fit()

Which returns this warning:

/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency D will be used. % freq, ValueWarning)

My dataset is daily so inferred 'D' is ok, but I was wondering how I can manually set the frequency.

Note that the DatetimeIndex doesn't have the freq (last line) ...

DatetimeIndex(['1973-05-01', '1973-05-02', '1973-05-03', '1973-05-04',
               '1973-05-05', '1973-05-06', '1973-05-07', '1973-05-08',
               '1973-05-09', '1973-05-10',
               ...
               '1973-09-21', '1973-09-22', '1973-09-23', '1973-09-24',
               '1973-09-25', '1973-09-26', '1973-09-27', '1973-09-28',
               '1973-09-29', '1973-09-30'],
              dtype='datetime64[ns]', name='Date', length=153, freq=None)

As per this answer I've checked for missing dates, but there doesn't appear to be any:

pd.date_range(start = '1973-05-01', end = '1973-09-30').difference(df.index)

DatetimeIndex([], dtype='datetime64[ns]', freq='D')

How should I set the frequency for the index?

Answer 1

I think pd.to_datetime not set default frequency, need DataFrame.asfreq:

df = df.set_index('Date').asfreq('d')
print (df.index)

DatetimeIndex(['1973-05-01', '1973-05-02', '1973-05-03', '1973-05-04',
               '1973-05-05', '1973-05-06', '1973-05-07', '1973-05-08',
               '1973-05-09', '1973-05-10',
               ...
               '1973-09-21', '1973-09-22', '1973-09-23', '1973-09-24',
               '1973-09-25', '1973-09-26', '1973-09-27', '1973-09-28',
               '1973-09-29', '1973-09-30'],
              dtype='datetime64[ns]', name='Date', length=153, freq='D')

But if duplicated values in index get error:

df = pd.concat([df, df])
df = df.set_index('Date')

print (df.asfreq('d').index)

ValueError: cannot reindex from a duplicate axis

Solution is use resample with some aggregate function:

print (df.resample('2D').mean().index)

DatetimeIndex(['1973-05-01', '1973-05-03', '1973-05-05', '1973-05-07',
               '1973-05-09', '1973-05-11', '1973-05-13', '1973-05-15',
               '1973-05-17', '1973-05-19', '1973-05-21', '1973-05-23',
               '1973-05-25', '1973-05-27', '1973-05-29', '1973-05-31',
               '1973-06-02', '1973-06-04', '1973-06-06', '1973-06-08',
               '1973-06-10', '1973-06-12', '1973-06-14', '1973-06-16',
               '1973-06-18', '1973-06-20', '1973-06-22', '1973-06-24',
               '1973-06-26', '1973-06-28', '1973-06-30', '1973-07-02',
               '1973-07-04', '1973-07-06', '1973-07-08', '1973-07-10',
               '1973-07-12', '1973-07-14', '1973-07-16', '1973-07-18',
               '1973-07-20', '1973-07-22', '1973-07-24', '1973-07-26',
               '1973-07-28', '1973-07-30', '1973-08-01', '1973-08-03',
               '1973-08-05', '1973-08-07', '1973-08-09', '1973-08-11',
               '1973-08-13', '1973-08-15', '1973-08-17', '1973-08-19',
               '1973-08-21', '1973-08-23', '1973-08-25', '1973-08-27',
               '1973-08-29', '1973-08-31', '1973-09-02', '1973-09-04',
               '1973-09-06', '1973-09-08', '1973-09-10', '1973-09-12',
               '1973-09-14', '1973-09-16', '1973-09-18', '1973-09-20',
               '1973-09-22', '1973-09-24', '1973-09-26', '1973-09-28',
               '1973-09-30'],
              dtype='datetime64[ns]', name='Date', freq='2D')

Answer 2

The problem is caused by the not explicitly set frequence. In most cases you can't be sure that your data does not have any gaps, so generate a data range with

rng = pd.date_range(start = '1973-05-01', end = '1973-09-30', freq='D')

reindex your DataFrame with this rng and fill the np.nan with your method or value of choice.