Documentation is mostly organized around trying to explain how to use some specific feature, which is usually not the best format for me, but it may be for othe

Documentation is mostly organized around trying to explain how to use some specific feature, which is usually not the best format for me, but it may be for others. The argument possibilities has always been an issue for me. In general, I have found, if you have non-homogenous data, Pandas is your best bet due to how general it is, even if it is sometimes frustrating when it forces a generalization on your data (e.g., try doing type conversions on numpy.datetime64, it lacks any sort of intuition). The distinction between a Series and a dataframe is something I always found pretty silly/frustrating and I wonder if it was the result of an early implementation issue rather than a logical simplification. maybe I'm particularly ignorant of some issues since my use case is perhaps more straightforward than some others but I've built an entire labelled data library for my team and it is easier for us to operate under similar primitive beliefs to the numpy ndarray, i.e., it's always an ndarray, adding a column (or dimension) does not change the type of the object and the associated methods and indexing in one particular way vs another (df['a'] vs df[['a']]) does not change the type of your object. If I'm missing the point of Series I would love to see them justified or a use case referenced.