Python Smooth Time Series Data

I have some data in python that is unixtime, value:

[(1301672429, 274), (1301672430, 302), (1301672431, 288)...]

Time constantly steps by one second. How might I reduce this data so the timestamp is every second, but the value is the average of the surrounding 10 values?

Fancier rolling averages would be good too, but this data is graphed so it is mostly to smooth out开发者_如何学Go the graph.

Follow up of ( TSQL Rolling Average of Time Groupings after coming to the conclusion that trying to do this in SQL is a route of pain).

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Using http://www.scipy.org/Cookbook/SignalSmooth:

import numpy
def smooth(x,window_len=11,window='hanning'):
        if x.ndim != 1:
                raise ValueError, "smooth only accepts 1 dimension arrays."
        if x.size < window_len:
                raise ValueError, "Input vector needs to be bigger than window size."
        if window_len<3:
                return x
        if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
                raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
        s=numpy.r_[2*x[0]-x[window_len-1::-1],x,2*x[-1]-x[-1:-window_len:-1]]
        if window == 'flat': #moving average
                w=numpy.ones(window_len,'d')
        else:  
                w=eval('numpy.'+window+'(window_len)')
        y=numpy.convolve(w/w.sum(),s,mode='same')
        return y[window_len:-window_len+1]

I get what seems to be good results with (Not that I understand the math):

   if form_results['smooth']:
            a = numpy.array([x[1] for x in results])
            smoothed = smooth(a,window_len=21)
            results = zip([x[0] for x in results], smoothed)

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I find Savitzky-Golay filter. it's assume a window and fit a polynomial curve and shift window. Fortunately it's implement in scipy.

https://en.wikipedia.org/wiki/File:Lissage_sg3_anim.gif

use this code:

from scipy.signal import savgol_filter
result = savgol_filter(value, 13, 5) # window size 13, polynomial order 5