Sorting of arrays based on another array

I have two arrays:

data_array=[4.4, 7.2, 10.1, 1.1, 5.5, 8.3, 2.2, 6.2, 3.3, 9.1, 1.3]
test_array=[2, 5, 9, 4, 10, 8, 7, 3, 6, 1, 1]

Required Output:

[2.2, 5.5, 9.1, 4.4, 10.1, 8.3, 7.2, 3.3, 6.2, 1.1, 1.3]

i.e., I need to arrange the data_array based on each element wise smallest difference of the two arrays in a unique manner (meaning: in the example test_array, value '1' is present twice whoes smallest difference->0.1 with values of data_array, hence 1.1 in output array, but for second value '1' in the test_array it should take the next smallest difference-> 0.3, hence 1.3 in output array)

This is an example. I want to execute this for arrays of larger lengths (in thousands/millions)

Thank you in advance

Solution in MATLAB, Python or any other efficient medium is appreciated!

def method1(A, B):
    result = np.empty_like(A)
    for i, val in tqdm(enumerate(A), desc="Processing", unit="iteration", unit_scale=True):
        closest_idx = np.argmin(np.abs(B- val))
        result[i] = B[closest_idx]
        comp_arr = np.delete(B, closest_idx)
    return result

#GPU execution - most efficient method found in stackoverflow!
@nb.njit('int_[:](float32[:],float32[:])', parallel=True)
def method2(A,B):
    mB = B.shape[0]
    output = np.empty(A.shape[0], dtype=np.int_)

    # Parallel loop
    for i in nb.prange(A.shape[0]):
        rowA = A[i]
        rowB = B

        index_rowB = np.argsort(rowB)
        sorted_rowB = rowB[index_rowB]

        idxs = np.searchsorted(sorted_rowB, rowA)
        left = np.fabs(rowA - sorted_rowB[np.maximum(idxs-1, 0)])
        right = np.fabs(rowA - sorted_rowB[np.minimum(idxs, mB-1)])
        prev_idx_is_less = (idxs == mB) | (left < right)

        output[i] = index_rowB[idxs - prev_idx_is_less]

    return output

Both methods takes years for execution for the length of my arrays!!!

I think I've got this. Apologies if I've not fully understood your question.

My solution:

1. Create dataframe with test data (saves original order of test_array through index)
2. Sort the dataframe by the test data
3. Add a column with sorted data_array
4. Sort the index to get original order of test_array.

This assumes that the smallest unique difference is the same as pairing the arrays by rank. Check that this is accurate for your use case.
I expect this to be quick given its low complexity.

Code:

import pandas as pd
data_array=[4.4, 7.2, 10.1, 1.1, 5.5, 8.3, 2.2, 6.2, 3.3, 9.1, 1.3]
test_array=[2, 5, 9, 4, 10, 8, 7, 3, 6, 1, 1]

df = pd.DataFrame(data={'test':test_array})
df.sort_values(by='test',inplace=True)
df['data']=sorted(data_array)
df.sort_index(inplace=True)
df['data']

#output: 2.2, 5.5, 9.1, 4.4, 10.1, 8.3, 7.2, 3.3, 6.2, 1.1, 1.3