I am trying to calculate the moving average in a large numpy array that contains NaNs. Currently I am using:

import numpy as npdef moving_average(a,n=5):ret = np.cumsum(a,dtype=float)ret[n:] = ret[n:]-ret[:-n]return ret[-1:]/n

When calculating with a masked array:

x = np.array([1.,3,np.nan,7,8,1,2,4,np.nan,np.nan,4,4,np.nan,1,3,6,3])
mx = np.ma.masked_array(x,np.isnan(x))
y = moving_average(mx).filled(np.nan)print y>>> array([3.8,3.8,3.6,nan,nan,nan,2,2.4,nan,nan,nan,2.8,2.6])

The result I am looking for (below) should ideally have NaNs only in the place where the original array, x, had NaNs and the averaging should be done over the number of non-NaN elements in the grouping (I need some way to change the size of n in the function.)

y = array([4.75,4.75,nan,4.4,3.75,2.33,3.33,4,nan,nan,3,3.5,nan,3.25,4,4.5,3])

I could loop over the entire array and check index by index but the array I am using is very large and that would take a long time. Is there a numpythonic way to do this?

Pandas has a lot of really nice functionality with this. For example:

x = np.array([np.nan, np.nan, 3, 3, 3, np.nan, 5, 7, 7])# requires three valid values in a row or the resulting value is nullprint(pd.Series(x).rolling(3).mean())#output
nan,nan,nan, nan, 3, nan, nan, nan, 6.333# only requires 2 valid values out of three for size=3 windowprint(pd.Series(x).rolling(3, min_periods=2).mean())#output
nan, nan, nan, 3, 3, 3, 4, 6, 6.3333

You can play around with the windows/min_periods and consider filling-in nulls all in one chained line of code.