Missing values in Time Series in python

Question

I have a time series dataframe, the dataframe is quite big and contain some missing values in the 2 columns(\'Humidity\' and \'Pressure\'). I would like to impute this missing v

Answer 1

You could use rolling like this:

frame = pd.DataFrame({'Humidity':np.arange(50,64)})

frame.loc[[3,7,10,11],'Humidity'] = np.nan

frame.Humidity.fillna(frame.Humidity.rolling(4,min_periods=1).mean())

Output:

0     50.0
1     51.0
2     52.0
3     51.0
4     54.0
5     55.0
6     56.0
7     55.0
8     58.0
9     59.0
10    58.5
11    58.5
12    62.0
13    63.0
Name: Humidity, dtype: float64