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Pandas Plot Graph Plots Series Data

My details:

>>> 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 converted to strings with datetime.strptime

.

The plot is very edgy like this (these are not my actual plots): enter image description here

How can I smooth it out like this: enter image description here

I know about the scipy.interpolate

one mentioned in this article (where I got the images from), but how can I apply it to Pandas time series?

I found this great library called Vincent that deals with Pandas but does not support Python 2.6.

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3 answers


Got it. Using this question , here's what I did:

  • Repeat mine tsgroup

    for 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

  • Interpolate the data using .interpolate(method='cubic')

    . This passes data to scipy.interpolate.interp1d

    and uses the view cubic

    , so you need to set scipy ( pip install scipy

    ) to 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

  • Divide it with tsint.plot()

    . Here's a comparison of the original tsgroup

    and tsint

    :



1 If you get an error message .interpolate(method='cubic')

telling you that Scipy is not installed even though you have it installed, open /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

.

+4


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Check scipy.interpolate.UnivariateSpline



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You can also smooth your data with moving averages, effectively applying a low pass filter to your data. Pandas support this with the method rolling()

.

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