I have one set of values measured at regular times. Say:

import pandas as pd
import numpy as np
rng = pd.date_range('2013-01-01', periods=12, freq='H')
data = pd.Series(np.random.randn(len(rng)), index=rng)

And another set of more arbitrary times, for example, (in reality these times are not a regular sequence)

ts_rng = pd.date_range('2013-01-01 01:11:21', periods=7, freq='87Min')
ts = pd.Series(index=ts_rng)

I want to know the value of data interpolated at the times in ts.
I can do this in numpy:

x = np.asarray(ts_rng,dtype=np.float64)
xp = np.asarray(data.index,dtype=np.float64)
fp = np.asarray(data)
ts[:] = np.interp(x,xp,fp)

But I feel pandas has this functionality somewhere in resample, reindex etc. but I can't quite get it.

You can concatenate the two time series and sort by index. Since the values in the second series are NaN you can interpolate and the just select out the values that represent the points from the second series:

 pd.concat([data, ts]).sort_index().interpolate().reindex(ts.index)

or

 pd.concat([data, ts]).sort_index().interpolate()[ts.index]