pandas resample interpolate is producing NaNs

Question

Modified from this example:

import io
import pandas as pd
import matplotlib.pyplot as plt

data = io.StringIO(\'\'\'\\
Values
1992-08-27 07:46:48,1
1992-08-2         


        

Answer 1

Option 1
That's because '4s' aligns perfectly with your existing index. When you resample, you get representation from your old series and are able to interpolate. What you want to do is to create an index that is the union of the old index with a new index. Then interpolate and reindex with a new index.

oidx = s.index
nidx = pd.date_range(oidx.min(), oidx.max(), freq='5s')
res = s.reindex(oidx.union(nidx)).interpolate('index').reindex(nidx)
res.plot(style='.-')
s.plot(style='o')

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Option 2A
If you are willing to forgo accuracy, you can ffill with a limit of 1

res = s.resample('5s').ffill(limit=1).interpolate()
res.plot(style='.-')
s.plot(style='o')

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Option 2B
Same thing with bfill

res = s.resample('5s').bfill(limit=1).interpolate()
res.plot(style='.-')
s.plot(style='o')

---

Option 3
Intermediate complexity and accuracy

nidx = pd.date_range(oidx.min(), oidx.max(), freq='5s')
res = s.reindex(nidx, method='nearest', limit=1).interpolate()
res.plot(style='.-')
s.plot(style='o')