Plot smooth curves of Pandas Series data

Question

My data is:

>>> ts = pd.TimeSeries(data,indexconv)
>>> tsgroup = ts.resample('t',how='sum')
>>> tsgroup
2014-11-08 10:30:00    3
2014-11-08 10:31:00    4
2014-11-08 10:32:00    7
  [snip]
2014-11-08 10:54:00    5
2014-11-08 10:55:00    2
Freq: T, dtype: int64
>>> tsgroup.plot()
>>> plt.show()

indexconv are strings converted using datetime.strptime.

The plot is very edgy like this (these aren't my actual plots):

How can I smooth it out like this:

I know about scipy.interpolate mentioned in this article (which is where I got the images from), but how can I apply it for Pandas time series?

I found this great library called Vincent that deals with Pandas, but it doesn't support Python 2.6.

Answer 1

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.

Answer 2

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.