Filtering an np array using values from each row

Question

I have an array with the shape (29,2):

arr = [[ 405.95168576 1033.        ]
 [ 406.23572583 1033.        ]
 [ 407.49812423 1028.        ]
 [ 402.66145728 1029.        ]
 [ 404.11080846 1032.        ]
 [ 401.75897118 1033.        ]
 [ 402.29352509 1029.        ]
 [ 402.34504752 1024.        ]
 [ 402.69938672 1027.        ]
 [ 400.55298544 1029.        ]
 [ 401.41432112 1027.        ]
 [ 400.89318038 1027.        ]
 [ 401.07444532 1029.        ]
 [ 400.43212193 1033.        ]
 [ 400.38178995 1027.        ]
 [ 399.89895625 1025.        ]
 [ 399.88394127 1031.        ]
 [ 399.97766298 1021.        ]
 [ 399.68084993 1027.        ]
 [ 399.65810987 1029.        ]
 [ 399.40565484 1020.        ]
 [ 399.34339145 1023.        ]
 [ 399.39613518 1019.        ]
 [ 399.37733697 1020.        ]
 [ 399.38314402 1020.        ]
 [ 399.47479381 1025.        ]
 [ 399.44134998 1025.        ]
 [ 399.43511907 1020.        ]
 [ 399.40346787 1020.        ]]

I would like to filter this array to find whether each row contains the maximum value for column arr[:,0], of all rows in which the value of arr[:,1] is equal to or lower than the one contained in that row.

At the moment, I have the following code, which produces the correct result:

import numpy as np

res = np.array([])

for i in range(arr.shape[0]):
    print(np.max(arr[:,0][arr[:,1] <= arr[i][1]]))
    if arr[i][0] >= np.max(arr[:,0][arr[:,1] <= arr[i][1]]):
        res = np.hstack((res, True))
    else:
        res = np.hstack((res, False))

print(res)

Is there a way to perform this operation in pure numpy, i.e. without using the loop?