On Mon, Jan 30, 2012 at 11:31 AM, Ted To <rainexpec...@theo.to> wrote: > On 01/30/2012 12:13 PM, Brett Olsen wrote: >> On Mon, Jan 30, 2012 at 10:57 AM, Ted To <rainexpec...@theo.to> wrote: >>> Sure thing. To keep it simple suppose I have just a two dimensional >>> array (time,output): >>> [(1,2),(2,3),(3,4)] >>> I would like to look at all values of output for which, for example time==2. >>> >>> My actual application has a six dimensional array and I'd like to look >>> at the contents using one or more of the first three dimensions. >>> >>> Many thanks, >>> Ted >> >> Couldn't you just do something like this with boolean indexing: >> >> In [1]: import numpy as np >> >> In [2]: a = np.array([(1,2),(2,3),(3,4)]) >> >> In [3]: a >> Out[3]: >> array([[1, 2], >> [2, 3], >> [3, 4]]) >> >> In [4]: mask = a[:,0] == 2 >> >> In [5]: mask >> Out[5]: array([False, True, False], dtype=bool) >> >> In [6]: a[mask,1] >> Out[6]: array([3]) >> >> ~Brett > > Thanks! That works great if I only want to search over one index but I > can't quite figure out what to do with more than a single index. So > suppose I have a labeled, multidimensional array with labels 'month', > 'year' and 'quantity'. a[['month','year']] gives me an array of indices > but "a[['month','year']]==(1,1960)" produces "False". I'm sure I simply > don't know the proper syntax and I apologize for that -- I'm kind of new > to numpy. > > Ted
You'd want to update your mask appropriately to get everything you want to select, one criteria at a time e.g.: mask = a[:,0] == 1 mask &= a[:,1] == 1960 Alternatively: mask = (a[:,0] == 1) & (a[:,1] == 1960) but be careful with the parens, & and | are normally high-priority bitwise operators and if you leave the parens out, it will try to bitwise-and 1 and a[:,1] and throw an error. If you've got a ton of parameters, you can combine these more aesthetically with: mask = (a[:,[0,1]] == [1, 1960]).all(axis=1) ~Brett _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
