Add Future dates for missing Rows in a Dataframe

Question

How to impute the Missed dates with next Dates in a data frame?

wtg_at1.tail(10)

	AmbientTemperatue	Date
818	31.237499	2020-03-28
819	32.865974	2020-03-29
820	32.032558	2020-03-30
821	31.671166	NaN
822	31.389927	NaN
823	31.243660	NaN
824	31.206777	NaN
825	31.241503	NaN
826	31.309531	NaN
827	31.382531	NaN

I am expecting my output data frame something similar to below. After 30th March, I am expecting next dates from 31st March.

	AmbientTemperatue	Date
818	31.237499	2020-03-28
819	32.865974	2020-03-29
820	32.032558	2020-03-30
821	31.671166	2020-03-31
822	31.389927	2020-04-01
823	31.243660	2020-04-02
824	31.206777	2020-04-03
825	31.241503	2020-04-04
826	31.309531	2020-04-05
827	31.382531	2020-04-06

I tried below code but not giving desired output.

wtg_at1.append(pd.DataFrame({'Date': pd.date_range(start=wtg_at1.Date.iloc[-8], periods=7, freq='D', closed='right')}))
wtg_at1

	AmbientTemperatue	Date
0	32.032558	2017-12-31
1	26.667757	2018-01-01
2	25.655754	2018-01-02
3	25.514013	2018-01-03
4	24.927652	2018-01-04
...	...	...
823	31.243660	NaN
824	31.206777	NaN
825	31.241503	NaN
826	31.309531	NaN
827	31.382531	NaN

Answer 1

If there is only one group of missing values is possible forward filling them and add counter by cumulative sum converted to days timedeltas:

df['Date'] = pd.to_datetime(df['Date'])

df['Date'] = df['Date'].ffill() + pd.to_timedelta(df['Date'].isna().cumsum(), unit='d')
print (df)
     AmbientTemperatue       Date
818          31.237499 2020-03-28
819          32.865974 2020-03-29
820          32.032558 2020-03-30
821          31.671166 2020-03-31
822          31.389927 2020-04-01
823          31.243660 2020-04-02
824          31.206777 2020-04-03
825          31.241503 2020-04-04
826          31.309531 2020-04-05
827          31.382531 2020-04-06

Another possible idea is reassign values by minimal datetime and length of DataFrame:

df['Date'] = pd.date_range(df['Date'].min(), periods=len(df))

---

If there is multiple groups with missing values:

print (df)
     AmbientTemperatue        Date
818          31.237499  2020-03-28
819          32.865974  2020-03-29
820          32.032558  2020-03-30
821          31.671166         NaN
822          31.389927         NaN
823          31.243660         NaN
824          31.206777  2020-05-08
825          31.241503         NaN
826          31.309531         NaN
827          31.382531         NaN

df['Date'] = pd.to_datetime(df['Date'])

m = df['Date'].notna()
s = (~m).groupby(m.cumsum()).cumsum()
df['Date'] = df['Date'].ffill() + pd.to_timedelta(s, unit='d')
print (df)
    AmbientTemperatue       Date
818          31.237499 2020-03-28
819          32.865974 2020-03-29
820          32.032558 2020-03-30
821          31.671166 2020-03-31
822          31.389927 2020-04-01
823          31.243660 2020-04-02
824          31.206777 2020-05-08
825          31.241503 2020-05-09
826          31.309531 2020-05-10
827          31.382531 2020-05-11