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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.
238
Linear interpolation is the most straightforward and commonly used interpolation method. It comes naturally when we have two points, we connect them with a straight line to fill out the missing information in between.
1) Linear Interpolation Linear Interpolation simply means to estimate a missing value by connecting dots in a straight line in increasing order. In short, It estimates the unknown value in the same increasing order from previous values.
Resample Hourly Data to Daily Dataresample() method. To aggregate or temporal resample the data for a time period, you can take all of the values for each day and summarize them. In this case, you want total daily rainfall, so you will use the resample() method together with . sum() .
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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