I have a dataset like this:

[[0,1],[0,2],[0,3],[0,4],[1,5],[1,6],[1,7],[2,8],[2,9]]

I need to delete the first elements of each subview of the data as defined by the first column. So first I get all elements that have 0 in the first column, and delete the first row: [0,1]. Then I get the elements with 1 in the first column and delete the first row [1,5], next step I delete [2,8] and so on and so forth. In the end, I would like to have a dataset like this:

[[0,2],[0,3],[0,4],[1,6],[1,7],[2,9]]

EDIT: Can this be done in numpy? My dataset is very large so for loops on all elements take at least 4 minutes to complete.

As requested, a numpy solution:

import numpy as np
a = np.array([[0,1], [0,2], [0,3], [0,4], [1,5], [1,6], [1,7], [2,8], [2,9]])
_,i = np.unique(a[:,0], return_index=True)b = np.delete(a, i, axis=0)

(above is edited to incorporate @Jaime's solution, here is my original masking solution for posterity's sake)

m = np.ones(len(a), dtype=bool)
m[i] = False
b = a[m]

Interestingly, the mask seems to be faster:

In [225]: def rem_del(a):.....:     _,i = np.unique(a[:,0], return_index=True).....:     return np.delete(a, i, axis = 0).....: In [226]: def rem_mask(a):.....:     _,i = np.unique(a[:,0], return_index=True).....:     m = np.ones(len(a), dtype=bool).....:     m[i] = False.....:     return a[m].....: In [227]: timeit rem_del(a)
10000 loops, best of 3: 181 us per loopIn [228]: timeit rem_mask(a)
10000 loops, best of 3: 59 us per loop