Data, in the present and minimal sense, is about 2D tables of numbers, which NumPy handles very well. Keep this in mind in case you forget the NumPy syntax specifics. Scikit-learn accepts only 2D NumPy arrays of real numbers with no missing np.nan values.
From experience, it tends to be best to change np.nan to some value instead of throwing away data. Personally, I like to keep track of Boolean masks and keep the data shape roughly the same, as this leads to fewer coding errors and more coding flexibility.
