Object arrays using record arrays
numpy supports working with arrays of python objects, but these arrays lack the type-uniformity of normal numpy arrays, so they can be quite inefficient in terms of space and time, and they can be quite cumbersome to work with. However, it would often be useful to be able to store a user-defined class in an array.
One approach is to take advantage of numpy's record arrays. These are arrays in which each element can be large, as it has named and typed fields; essentially they are numpy's equivalent to arrays of C structures. Thus if one had a class consisting of some data - named fields, each of a numpy type - and some methods, one could represent the data for an array of these objects as a record array. Getting the methods is more tricky.
One approach is to create a custom subclass of the numpy array which handles conversion to and from your object type. The idea is to store the data for each instance internally in a record array, but when indexing returns a scalar, construct a new instance from the data in the records. Similarly, when assigning to a particular element, the array subclass would convert an instance to its representation as a record.
Attached is an implementation of the above scheme.