In Python, I have two arrays: import numpy as np a = np.array([ [1, 0.2], [2, 0.5], [3, 0.8]]) b = np.array([ [2, 0.4], [3, 0.7], [4, 1.3], [5, 2]]) I need to do an "outer join" based on the values of the first column, and fill with 0 in case there is no match, i.e., end up with something like this: c = np.array([ [1, 0.2, 0], [2, 0.5, 0.4], [3, 0.8, 1.7], [4, 0, 1.3], [5, 0, 2]]) I was first thinking about converting to DataFrames and then doing an outer join, but I need this to be very fast, since I need to do it 18 million times. I don't necessarily need the entire array, two vectors in the same order would also suffice. Having the two arrays as DataFrames in the first place is not an option, because I use the arrays for several operations where NumPy has considerable speed advantage. How can I do this very fast? Continue reading...
