I am trying to calculate the mean across many netcdf files. I cannot use numpy.mean because there are too many files to concatenate and I end up with a memory error. I have enabled the below code to do what I need but I have a few nan values in some of my arrays. Is there a way to ignore these somewhere in my code. I seem to face this problem often so I would love a command that ignores blanks in my array before I continue on to the next processing step.
Any feedback is greatly appreciated.

netCDF_list=[]
for dir in glob.glob(MainFolder + '*/01/')+ glob.glob(MainFolder + '*/02/')+ glob.glob(MainFolder + '*/12/'):
        for ncfile in glob.glob(dir + '*.nc'):
            netCDF_list.append(ncfile)

slice_counter=0
print netCDF_list

for filename in netCDF_list:
        ncfile=netCDF4.Dataset(filename)
        TSFC=ncfile.variables['T_SFC'][:]
        fillvalue=ncfile.variables['T_SFC']._FillValue
        TSFC=MA.masked_values(TSFC, fillvalue)
        for i in xrange(0,len(TSFC)-1,1):
                slice_counter +=1
                #print slice_counter
                try:
                        running_sum=N.add(running_sum, TSFC[i])
                except NameError:
                        print "Initiating the running total of my variable..."
                        running_sum=N.array(TSFC[i])

TSFC_avg=N.true_divide(running_sum, slice_counter)
N.set_printoptions(threshold='nan')
print "the TSFC_avg is:", TSFC_avg