Combining an indexed array in Python

Question

Suppose I have two numpy arrays of the form

x = [[1,2] [2,4] [3,6] [4,NaN] [5,10]] y = [[0,-5] [1,0] [2,5] [5,20] [6,25]] 

there is an effective way to merge them so that I have

 xmy = [[0, NaN, -5 ] [1, 2, 0 ] [2, 4, 5 ] [3, 6, NaN] [4, NaN, NaN] [5, 10, 20 ] [6, NaN, 25 ] 

I can implement a simple function using search to find the index, but this is not elegant and potentially inefficient for a large number of arrays and large sizes. Any pointer is evaluated.

Answer 1

See numpy.lib.recfunctions.join_by

It only works with structured arrays or recursions, so there are a few kinks.

First you need to get at least a little familiarity with structured arrays. See here if you haven’t.

 import numpy as np import numpy.lib.recfunctions # Define the starting arrays as structured arrays with two fields ('key' and 'field') dtype = [('key', np.int), ('field', np.float)] x = np.array([(1, 2), (2, 4), (3, 6), (4, np.NaN), (5, 10)], dtype=dtype) y = np.array([(0, -5), (1, 0), (2, 5), (5, 20), (6, 25)], dtype=dtype) # You want an outer join, rather than the default inner join # (all values are returned, not just ones with a common key) join = np.lib.recfunctions.join_by('key', x, y, jointype='outer') # Now we have a structured array with three fields: 'key', 'field1', and 'field2' # (since 'field' was in both arrays, it renamed x['field'] to 'field1', and # y['field'] to 'field2') # This returns a masked array, if you want it filled with # NaN's, do the following... join.fill_value = np.NaN join = join.filled() # Just displaying it... Keep in mind that as a structured array, # it has one dimension, where each row contains the 3 fields for row in join: print row 

It is output:

 (0, nan, -5.0) (1, 2.0, 0.0) (2, 4.0, 5.0) (3, 6.0, nan) (4, nan, nan) (5, 10.0, 20.0) (6, nan, 25.0) 

Hope this helps!

Edit1: added example Edit2: Really should not join the floats ... Changed the "key" field for int.