Python numpy moving average for data

The following examples produces a moving average of the preceding WINDOW values. We truncate the first (WINDOW -1) values since we can’t find the average before them. (The default behaviour for convolution is to assume that values before the start of our sequence are 0). (More formally, we construct the sequence y for the sequence x where y_i = (x_i + x_(i+1) + …. x_(i+n)) / n)

WINDOW = 10
data = [1,2,3,4,5,5,5,5,5,5,5,5,5,5,5]
weightings = numpy.repeat(1.0, WINDOW) / WINDOW
numpy.convolve(data, weightings)[WINDOW-1:-(WINDOW-1)]

This makes use of numpy’s convolution function. This is a general purpose moving average operation.

Changing weightings makes some values more important; offsetting appropriately allows you to view average as around point rather than before point.

Rather than truncating values we can fix the initial values in place, as illustrated in this example:

WINDOW = 10
data = [1,2,3,4,5,5,5,5,5,5,5,5,5,5,5]
extended_data = numpy.hstack([[data[0]] * (WINDOW- 1), data])
weightings = numpy.repeat(1.0, WINDOW) / WINDOW
numpy.convolve(extended_data, weightings)[WINDOW-1:-(WINDOW-1)]