Find common elements in 2D numpy arrays

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If I have two (or more) 2D arrays, how can I get only common elements between the arrays given a row number. For example, I have arrays in the format:

time, position, velocity, acceleration

I want to get the two arrays to only have the same time elements, so row 0. I can use

np.intersect1d(array1[:, 0], array2[:, 0])

which gives all the common times, but I want to either extract all matching rows/columns from array1/2 or remove non common time elements. In the end array1 and array2 will have the exact same dimensions so I could go:

pos_difference = array1[:, 1] - array2[:, 1]

The arrays could be different sizes, so for example:

array1 = [[1, 100.0, 0.0, 0.0], [2, 110.0, 0.0, 0.0], [3, 120.0, 0.0, 0.0]]
array2 = [[1, 101.0, 0.0, 0.0], [3, 119, 0.0, 0.0]]

And I want to extract only common time elements so array1 and array2 will only contain when Time=1, and Time=3, since those are the common time elements. Then I can go:

pos_difference = array1[:, 1] - array2[:, 1]

and this will be the position differences between the two arrays at the same time:

# First row will be when time=1 and second row will be when time=3
pos_difference = [[0, -1, 0.0, 0.0], [0, 1, 0.0, 0.0]]
intersection numpy python
3 Answers

I found using intersect1d more clearer way to find common elements in 2D numpy array. In this case recent_books and coding_books have been defined.

start = time.time()
recent_coding_books = np.intersect1d([recent_books], [coding_books]) 
print(len(recent_coding_books))
print('Duration: {} seconds'.format(time.time() - start))
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