Moving average or running mean

Question

Is there a SciPy function or NumPy function or module for Python that calculates the running mean of a 1D array given a specific window?

Answer 1

There are many answers above about calculating a running mean. My answer adds two extra features:

1. ignores nan values
2. calculates the mean for the N neighboring values NOT including the value of interest itself

This second feature is particularly useful for determining which values differ from the general trend by a certain amount.

I use numpy.cumsum since it is the most time-efficient method (see Alleo's answer above).

N=10 # number of points to test on each side of point of interest, best if even
padded_x = np.insert(np.insert( np.insert(x, len(x), np.empty(int(N/2))*np.nan), 0, np.empty(int(N/2))*np.nan ),0,0)
n_nan = np.cumsum(np.isnan(padded_x))
cumsum = np.nancumsum(padded_x) 
window_sum = cumsum[N+1:] - cumsum[:-(N+1)] - x # subtract value of interest from sum of all values within window
window_n_nan = n_nan[N+1:] - n_nan[:-(N+1)] - np.isnan(x)
window_n_values = (N - window_n_nan)
movavg = (window_sum) / (window_n_values)

This code works for even Ns only. It can be adjusted for odd numbers by changing the np.insert of padded_x and n_nan.

Example output (raw in black, movavg in blue):

This code can be easily adapted to remove all moving average values calculated from fewer than cutoff = 3 non-nan values.

window_n_values = (N - window_n_nan).astype(float) # dtype must be float to set some values to nan
cutoff = 3
window_n_values[window_n_values