许多Numpy数组与少量Python类

Question

I'm working on a project that heavily relies on reasonably small 2D Numpy arrays to represent our data (where small is around 100 x 4, at worst). The problem with this representation is that is extremely cumbersome and unclear given the context of the problem, terrible to maintain, and very bug prone. For example, my current task, in the context of these arrays, requires adding and removing multiple rows, and allocating a large number of them. I decided that an object-oriented, graph-based approach would be far clearer to use on my end and more generic for future development. After sitting down and designing most of the functionality on paper, there will probably be 5 or 6 larger classes with internal dictionaries (representing adjacency lists for graphs), that remove the need for Numpy arrays and are much clearer to interpret.

It is well known that Numpy arrays blow Python data structures out of the water in terms of indexing speed and most other functionality. However, I also know that Python dictionaries are highly optimized and are fairly efficient in their own right. My dilemma is whether or not reallocating many Numpy arrays and copying values back and forth is more or less efficient than a few persisting classes that can be edited easily without reallocation, but will have slower indexing time than a Numpy array.

It's worth noting that I'm not using much of Numpy functionality other than np.where() , which can be worked around if necessary. Also the algorithm scales not in the size of arrays but in the number of arrays; a harder problem will have more arrays, but not necessarily larger ones.

Thanks!