Plot smooth curves of Pandas Series data

Got it. With help from this question, here's what I did:

1. Resample my tsgroup from minutes to seconds.

   \>>> tsres = tsgroup.resample('S')
   \>>> tsres
   2014-11-08 10:30:00     3
   2014-11-08 10:30:01   NaN
   2014-11-08 10:30:02   NaN
   2014-11-08 10:30:03   NaN
   ...
   2014-11-08 10:54:58   NaN
   2014-11-08 10:54:59   NaN
   2014-11-08 10:55:00     2
   Freq: S, Length: 1501

2. Interpolate the data using .interpolate(method='cubic'). This passes the data to scipy.interpolate.interp1d and uses the cubic kind, so you need to have scipy installed (pip install scipy) ¹.

   \>>> tsint = tsres.interpolate(method='cubic')
   \>>> tsint
   2014-11-08 10:30:00    3.000000
   2014-11-08 10:30:01    3.043445
   2014-11-08 10:30:02    3.085850
   2014-11-08 10:30:03    3.127220
   ...
   2014-11-08 10:54:58    2.461532
   2014-11-08 10:54:59    2.235186
   2014-11-08 10:55:00    2.000000
   Freq: S, Length: 1501

3. Plot it using tsint.plot(). Here's a comparison between the original tsgroup and tsint:

   

¹ If you're getting an error from .interpolate(method='cubic') telling you that Scipy isn't installed even if you do have it installed, open up /usr/lib64/python2.6/site-packages/scipy/interpolate/polyint.py or wherever your file might be and change the second line from from scipy import factorial to from scipy.misc import factorial.

You can smooth out your data with moving averages as well, effectively applying a low-pass filter to your data. Pandas supports this with the rolling() method.