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I have a time series that I want to lag and predict on for future data one year ahead that looks like:

Date Energy Pred Energy Lag Error . 2017-09-01 9 8.4 2017-10-01 10 9 2017-11-01 11 10 2017-12-01 12 11.5 2018-01-01 1 1.3 NaT (pred-true) NaT NaT NaT . .

All I want to do is impute dates into the NaT entries to continue from 2018-01-01 to 2019-01-01 (just fill them like we're in Excel drag and drop) because there are enough NaT positions to fill up to that point.

I've tried model['Date'].fillna() with various methods and either just repeats the same previous date or drops things I don't want to drop.

Any way to just fill these NaTs with 1 month increments like the previous data?

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Make the df and set the index (there are better ways to set the index):

""" Date,Energy,Pred Energy,Lag Error 2017-09-01,9,8.4 2017-10-01,10,9 2017-11-01,11,10 2017-12-01,12,11.5 2018-01-01,1,1.3 """ import pandas as pd df = pd.read_clipboard(sep=",", parse_dates=True) df.set_index(pd.DatetimeIndex(df['Date']), inplace=True) df.drop("Date", axis=1, inplace=True) df

Reindex to a new date_range:

idx = pd.date_range(start='2017-09-01', end='2019-01-01', freq='MS') df = df.reindex(idx)

Output:

 Energy Pred Energy Lag Error 2017-09-01 9.0 8.4 NaN 2017-10-01 10.0 9.0 NaN 2017-11-01 11.0 10.0 NaN 2017-12-01 12.0 11.5 NaN 2018-01-01 1.0 1.3 NaN 2018-02-01 NaN NaN NaN 2018-03-01 NaN NaN NaN 2018-04-01 NaN NaN NaN 2018-05-01 NaN NaN NaN 2018-06-01 NaN NaN NaN 2018-07-01 NaN NaN NaN 2018-08-01 NaN NaN NaN 2018-09-01 NaN NaN NaN 2018-10-01 NaN NaN NaN 2018-11-01 NaN NaN NaN 2018-12-01 NaN NaN NaN 2019-01-01 NaN NaN NaN

Help from: Pandas Set DatetimeIndex

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