Hi, +1 on the idea but how will this work with other numpy methods? suppose *arr* is a structured array with dtype:
[('name', 'S25'), ('height', float), ('age', int), ('gender', 'S8') ] Would you be able to first define a list of columns such as cols=['height', 'age'] arr[cols] This would be a handy feature. For example, for some compatible array A, would you be able to the following with a view np.linalg.lstsq(A, arr[['age']]) np.linalg.lstsq(A,arr[['height', 'age']] Bruce _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion