Francesc Alted <faltet <at> pytables.org> writes: > > What are some common use cases for this feature? > > > > I use structured arrays quite a lot, but I haven't found myself > > wanting something like this. If I do need a subset of a structured > > array generally I use something like > > > > [rec[n] for n in 'name age gender'.split()] > > Good point. However, there are still some very valid reasons for having > an idiom like: > > newarr = arr[['name', 'age']] > > returning a record array. > > The first one (and most important IMO), is that newarr continues to be > an structured array (BTW, when changed this name from the original > record array?), and you can use all the features of these beasts with > it. Other reason (albeit a bit secondary) is that its data buffer can > be shared through the array interface with other applications, or plain > C code, in a relatively straightforward way. However, if newarr > becomes a list (or dictionary), this is simply not possible. > > Cheers, >
That's not a sample use case ;) One of the things I love about Python is that it has a small core set of features and tries to avoid having many ways to do the same thing. This makes it extremely easy to learn. With every new feature, numpy gets a little bit harder to learn, there's more to document and the code base gets larger and so harder to maintain. In those senses, whenever you add a new function/feature to numpy, it gets a little bit worse. So I think it would be nice to have some concrete examples of what the new feature will be useful for, just to show how it outweighs those negatives. As a bonus, they'd provide nice examples to put in the documentation :). Neil PS. Thanks for your work on pytables! I've used it quite a bit, mostly for reading hdf5 files. _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
