Hi, Chris. Look at this, _I'm_ answering one of _your_ questions (correctly, I hope):
--- On Tue, 7/21/09, Christopher Barker <[email protected]> wrote: > I don't see why: > > np.array('a string', dtype='S1') > > results in a length (1,) array, for instance. > > Actually, I think I do -- numpy is treating the string as a > single > scalar, rather than a sequence of characters, and doing its > best to > convert that scaler to a length one string. However, I Well, as for why "[it's doing] its best to convert that scalar to a length one string," that's because you used dtype='S1' instead of dtype='S8': >>> np.array('a string', dtype='S1') array('a', dtype='|S1') >>> np.array('a string', dtype='S8') array('a string', dtype='|S8') but as for shape, I can't reproduce your result at all: >>> np.array('a string', dtype='S1').shape () >>> np.array('a string', dtype='S8').shape () and as for size, yes, in both cases it is treating the entire string as a single "element": >>> np.array('a string', dtype='S1').size 1 >>> np.array('a string', dtype='S8').size 1 > don't know if > there is a compelling reason why it should do that -- in > other contexts, > Python generally treats strings as a sequence of > characters. I see your point (global consistency) but personally, IMO, if you want that kind of string behavior, work in the Python namesapace: I like (though I've yet to actually use it) options (if you want to process a single string array element in the "standard" way, extract it from the array first w/ an index - it's only a few more characters of code). :-) DG > -Chris > > > > > > > > > > -- > Christopher Barker, Ph.D. > Oceanographer > > Emergency Response Division > NOAA/NOS/OR&R > (206) 526-6959 voice > 7600 Sand Point Way NE (206) > 526-6329 fax > Seattle, WA 98115 > (206) 526-6317 main > reception > > [email protected] > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
