Python regularise irregular time series with linear interpolation

Question

I have a time series in pandas that looks like this:

                     Values
1992-08-27 07:46:48    28.0  
1992-08-27 08:00:48    28.2  
1992-08-27 08:33:48    28.4  
1992-08-27 08:43:48    28.8  
1992-08-27 08:48:48    29.0  
1992-08-27 08:51:48    29.2  
1992-08-27 08:53:48    29.6  
1992-08-27 08:56:48    29.8  
1992-08-27 09:03:48    30.0

I would like to resample it to a regular time series with 15 min times steps where the values are linearly interpolated. Basically I would like to get:

                     Values
1992-08-27 08:00:00    28.2  
1992-08-27 08:15:00    28.3  
1992-08-27 08:30:00    28.4  
1992-08-27 08:45:00    28.8  
1992-08-27 09:00:00    29.9

However using the resample method (df.resample('15Min')) from Pandas I get:

                     Values
1992-08-27 08:00:00   28.20  
1992-08-27 08:15:00     NaN  
1992-08-27 08:30:00   28.60  
1992-08-27 08:45:00   29.40  
1992-08-27 09:00:00   30.00  

I have tried the resample method with different 'how' and 'fill_method' parameters but never got exactly the results I wanted. Am I using the wrong method?

I figure this is a fairly simple query, but I have searched the web for a while and couldn't find an answer.

Thanks in advance for any help I can get.

Answer 1

You can do this with traces. First, create a TimeSeries with your irregular measurements like you would a dictionary:

ts = traces.TimeSeries([
    (datetime(1992, 8, 27, 7, 46, 48), 28.0),
    (datetime(1992, 8, 27, 8, 0, 48), 28.2),
    ...
    (datetime(1992, 8, 27, 9, 3, 48), 30.0),
])

Then regularize using the sample method:

ts.sample(
    sampling_period=timedelta(minutes=15),
    start=datetime(1992, 8, 27, 8),
    end=datetime(1992, 8, 27, 9),
    interpolate='linear',
)

This results in the following regularized version, where the gray dots are the original data and the orange is the regularized version with linear interpolation.

The interpolated values are:

1992-08-27 08:00:00    28.189 
1992-08-27 08:15:00    28.286  
1992-08-27 08:30:00    28.377
1992-08-27 08:45:00    28.848
1992-08-27 09:00:00    29.891