Question

I have a 2D array of Numpy data read from a .csv file. Each row represents a data point with the final column containing a a ‘key’ which corresponds uniquely to ‘key’ in another Numpy array – the ‘lookup table’ as it were.

What is the best (most Numpythonic) way to match up the lines in the first table with the values in the second?

Answer 1

Some example data:

import numpy as np

lookup = 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.     ]])