> While I haven't had a chance to really look in-depth at the changes > myself (I'm a busy man! So many mailing lists!), I so far like the > look and sound of them. That's just my opinion, though.
If people are okay with the attribute magic, I have a proposal for more of it. In my own project where I use labeled arrays (http://github.com/commonsense/divisi2), I don't have labeled axes. But I assumed everything was 1 or 2-D, and gave the 2-D matrices methods like "row_named", "col_named", etc., to encourage readable code. With the current implementation of datarray, I could get that by labeling the axes "row" and "col", except the moment you transpose a matrix like that you get rows named "col" and columns named "row", so that's not the right answer. My proposal is that datarray.row should be equivalent to datarray.axes[0], and datarray.column should be equivalent to datarray.axes[1], so that you can always ask for something like "arr.column.named(2010)" (replace those with square brackets if you like). Not sure yet what the right way is to generalize this to 1-D and n-D. -- Rob _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
