Finding moving average from data points in Python

As numpy.convolve is pretty slow, those who need a fast performing solution might prefer an easier to understand cumsum approach. Here is the code:

cumsum_vec = numpy.cumsum(numpy.insert(data, 0, 0)) ma_vec = (cumsum_vec[window_width:] - cumsum_vec[:-window_width]) / window_width

where data contains your data, and ma_vec will contain moving averages of window_width length.

On average, cumsum is about 30-40 times faster than convolve.

Before reading this answer, bear in mind that there is another answer below, from Roman Kh, which uses numpy.cumsum and is MUCH MUCH FASTER than this one.

Best One common way to apply a moving/sliding average (or any other sliding window function) to a signal is by using numpy.convolve().

def movingaverage(interval, window_size):    window = numpy.ones(int(window_size))/float(window_size)    return numpy.convolve(interval, window, 'same')

Here, interval is your x array, and window_size is the number of samples to consider. The window will be centered on each sample, so it takes samples before and after the current sample in order to calculate the average. Your code would become:

plot(x,y)xlim(0,1000)x_av = movingaverage(interval, r)plot(x_av, y)xlabel("Months since Jan 1749.")ylabel("No. of Sun spots")show()

Hope this helps!

A moving average is a convolution, and numpy will be faster than most pure python operations. This will give you the 10 point moving average.

import numpy as npsmoothed = np.convolve(data, np.ones(10)/10)

I would also strongly suggest using the great pandas package if you are working with timeseries data. There are some nice moving average operations built in.