Using Numpy arrays as lookup tables

Some example data:

import numpy as nplookup = np.array([[  1.     ,   3.14   ,   4.14   ],                   [  2.     ,   2.71818,   3.7    ],                   [  3.     ,  42.     ,  43.     ]])a = np.array([[ 1, 11],              [ 1, 12],              [ 2, 21],              [ 3, 31]])

Build a dictionary from key to row number in the lookup table:

mapping = dict(zip(lookup[:,0], range(len(lookup))))

Then you can use the dictionary to match up lines. For instance, if you just want to join the tables:

>>> np.hstack((a, np.array([lookup[mapping[key],1:]                             for key in a[:,0]])))array([[  1.     ,  11.     ,   3.14   ,   4.14   ],       [  1.     ,  12.     ,   3.14   ,   4.14   ],       [  2.     ,  21.     ,   2.71818,   3.7    ],       [  3.     ,  31.     ,  42.     ,  43.     ]])    

In the special case when the index can be calculated from the keys, the dictionary can be avoided. It's an advantage when the key of the lookup table can be chosen.

For Vebjorn Ljosa's example:

lookup:

>>> lookup[a[:,0]-1, :]array([[  1.     ,   3.14   ,   4.14   ],       [  1.     ,   3.14   ,   4.14   ],       [  2.     ,   2.71818,   3.7    ],       [  3.     ,  42.     ,  43.     ]])

merge:

>>> np.hstack([a, lookup[a[:,0]-1, :]])array([[  1.     ,  11.     ,   1.     ,   3.14   ,   4.14   ],       [  1.     ,  12.     ,   1.     ,   3.14   ,   4.14   ],       [  2.     ,  21.     ,   2.     ,   2.71818,   3.7    ],       [  3.     ,  31.     ,   3.     ,  42.     ,  43.     ]])