How to calculate rolling / moving average using NumPy / SciPy?

Question

There seems to be no function that simply calculates the moving average on numpy/scipy, leading to convoluted solutions.

My question is two-fold:

• What's the easiest way to (correctly) implement a moving average with numpy?
• Since this seems non-trivial and error prone, is there a good reason not to have the batteries included in this case?

Answer 1

If you just want a straightforward non-weighted moving average, you can easily implement it with np.cumsum, which may be is faster than FFT based methods:

EDIT Corrected an off-by-one wrong indexing spotted by Bean in the code. EDIT

def moving_average(a, n=3) :
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1:] / n

>>> a = np.arange(20)
>>> moving_average(a)
array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
        12.,  13.,  14.,  15.,  16.,  17.,  18.])
>>> moving_average(a, n=4)
array([  1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5])

So I guess the answer is: it is really easy to implement, and maybe numpy is already a little bloated with specialized functionality.