I have an np.array with over 330,000 rows. I simply try to take the average of it and it returns NaN. Even if I try to filter out any potential NaN values in my array (there shouldn't be any anyways), average returns NaN. Am I doing something totally wacky?
My code is here:
average(ngma_heat_daily)
Out[70]: nan
average(ngma_heat_daily[ngma_heat_daily != nan])
Out[71]: nan
try this:
>>> np.nanmean(ngma_heat_daily)
This function drops NaN values from your array before taking the mean.
Edit: the reason that average(ngma_heat_daily[ngma_heat_daily != nan]) doesn't work is because of this:
>>> np.nan == np.nan
False
according to the IEEE floating-point standard, NaN is not equal to itself! You could do this instead to implement the same idea:
>>> average(ngma_heat_daily[~np.isnan(ngma_heat_daily)])
np.isnan, np.isinf, and similar functions are very useful for this type of data masking.
Also, there is a function named nanmedian which ignores NaN values. Signature of that function is: numpy.nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=<no value>)
