how to split a numpy array into subarrays based on values of one colums

First you find all arrays which contains 2 or which do not contains 2. This array will be full with True and False values. Transform this array to an array with zeros and ones. Check where there are differences (like [0, 0, 1, 1, 0] will be: 0, 1, 0, -1.

Based on the change one can use numpy where to find the indices of those values.

Insert the index 0 and the last index for the big array, so you are able to zip them in a left and right slice.

import numpy as np
big_array = np.array([[0., 10., 2.],
                      [2., 6., 2.],
                      [3., 1., 7.1],
                      [3.3, 6., 7.8],
                      [4., 5., 2.],
                      [6., 6., 2.],
                      [7., 1., 2.],
                      [8., 5., 2.1]])
idx = [2 in array for array in big_array]
idx *= np.ones(len(idx))
slices = list(np.where(np.diff(idx) != 0)[0] + 1)
slices.insert(0,0)
slices.append(len(big_array))

result = list()
for left, right in zip(slices[:-1], slices[1:]):
    result.append(big_array[left:right])

'''
[array([[ 0., 10.,  2.],
        [ 2.,  6.,  2.]]),
 array([[3. , 1. , 7.1],
        [3.3, 6. , 7.8]]),
 array([[4., 5., 2.],
        [6., 6., 2.],
        [7., 1., 2.]]),
 array([[8. , 5. , 2.1]])]
'''

You can do this with numpy

np.split(
    big_array,
    np.flatnonzero(np.diff(big_array[:,2] == 2) != 0) + 1
)

Output

[array([[ 0., 10.,  2.],
        [ 2.,  6.,  2.]]),
 array([[3. , 1. , 7.1],
        [3.3, 6. , 7.8]]),
 array([[4., 5., 2.],
        [6., 6., 2.],
        [7., 1., 2.]]),
 array([[8. , 5. , 2.1]])]