Finding common rows between two 2D NumPy arrays

Asked Dec 10 '16 at 23:43

Active Dec 10 '16 at 23:57

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I have two numpy arrays:

a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
b = np.array([[1, 2, 3], [0, 1, 6], [3, 3, 3], [4, 5, 6]] )

How do I go about finding out the common rows between the two arrays? So, for these two sample arrays, the result should be:

[[1, 2, 3], [4, 5, 6]]

I searched on StackOverflow, but only saw solutions to find out unique rows in one array instead of two arrays...I figured there's a way to iterate through the first array and compare with second array, but that must be hugely inefficient compared with a native numpy way of doing things. Any help is appreciated.

edited Dec 10 '16 at 23:57

Divakar

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asked Dec 10 '16 at 23:43

George Liu

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From the linked dup, get the row indices as `idx` assuming `X` as `a` and then do : `a[idx]`. – Divakar Dec 10 '16 at 23:47
Yeah, that works. Would you mind explaining how this code works "np.where((a==b[:,None]).all(-1))[1]" Thank you@Divakar. – George Liu Dec 11 '16 at 00:08
This might shed some light - http://stackoverflow.com/a/40721148/3293881 – Divakar Dec 11 '16 at 00:15

Finding common rows between two 2D NumPy arrays

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