upsample in a timeseries and interpolating data

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upsample in a timeseries and interpolating data



I need to perform an Upsample in a timeseries, and then to interpolate data and I would like to find the best way to do that. Timeseries has not constant interval. I show a DatFrame example and the result I'm looking. In the result example I'm interpolating just 1 row. It would be great to bea able to interpolate n rows.


data = 'time': ['08-12-2018 10:00:00','08-12-2018 10:01:00','08-12-2018
10:01:30','08-12-2018 10:03:00','08-12-2018 10:03:10'], 'value':[1,2,3,4,5]
df=pd.DataFrame(data)
df.time=pd.to_datetime(df.time)
df
Out[42]:
time value
0 2018-08-12 10:00:00 1
1 2018-08-12 10:01:00 2
2 2018-08-12 10:01:30 3
3 2018-08-12 10:03:00 4
4 2018-08-12 10:03:10 5



Result


time value
0 2018-08-12 10:00:00 1
1 2018-08-12 10:00:30 1.5
2 2018-08-12 10:01:00 2
3 2018-08-12 10:01:15 2.5
4 2018-08-12 10:01:30 3
5 2018-08-12 10:02:15 3.5
6 2018-08-12 10:03:00 4
7 2018-08-12 10:03:05 4.5
8 2018-08-12 10:03:10 5




1 Answer
1



You can multiple index, convert datetime to numeric - native numpy array in nanoseconds, so possible add new NaNs rows by reindex and interpolate. Last convert time column back to datetimes:


NaN


reindex


interpolate


time


datetime


N = 2
df.index = df.index * N
df.time= df.time.astype(np.int64)
df1 = df.reindex(np.arange(df.index.max() + 1)).interpolate()
df1.time=pd.to_datetime(df1.time)
print (df1)
time value
0 2018-08-12 10:00:00 1.0
1 2018-08-12 10:00:30 1.5
2 2018-08-12 10:01:00 2.0
3 2018-08-12 10:01:15 2.5
4 2018-08-12 10:01:30 3.0
5 2018-08-12 10:02:15 3.5
6 2018-08-12 10:03:00 4.0
7 2018-08-12 10:03:05 4.5
8 2018-08-12 10:03:10 5.0





Very nice! Thanks.
– Guido
Aug 12 at 14:12






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