Heavy numba user here. What Wes is saying is that while Pandas handles some of those missing values in an automated way, if you choose to use numba it uses nump

Heavy numba user here. What Wes is saying is that while Pandas handles some of those missing values in an automated way, if you choose to use numba it uses numpy arrays so you may have to handle some of those things yourself. I have at times used a separate numpy array to indicate whether values are missing or not. You could also use a value which is far out of the bounds of what you might ever see in your real data, then test for that while you're looping over those values (eg. fill missing values with -3.4E38 if you have a float32). Depending on what you're doing, you might be able to use numpy.nan as a value. It does work inside of numpy arrays. But some methods that operate on those objects might not work as you expect. For instance, if you run numpy.mean on a numpy array of [nan, 4, 5], it will return nan. If you run the same thing on a pandas dataframe of the same values, you'll get 4.5.