human
Solve the following python programming problem given the following code:
def knn_thresholding(df, column, threshold = 15, k = 3) : 
    'Cluster rare samples in data[column] with frequency less than
    threshold with one of k-nearest clusters

    parameters:
       data - pandas.DataFrame containing colums: latitude, longitude, column
       column - the name of the column to threshold
       threshold - the minimum sample frequency
       k - the number of k-neighbors to explore when selecting cluster partner
    ' 
    def ids_centers_sizes(data) : 
        dat = np.array ([(i, data.latitude [(data [column] == i)].mean (), data.longitude [(data [column] == i)].mean (), (data [column] == i).sum ()) for i in set (list (data [column]))]) 
        return (dat [:, 0], dat [:, 1 : (- 1)].astype (float), dat [:, (- 1)].astype (int)) 
knn = neighbors.NearestNeighbors (n_neighbors = k) 
    while True : 
        (ids, centers, sizes) = ids_centers_sizes (df) 
        asrt = np.argsort (sizes) 
        if (sizes [asrt [0]] >= threshold) : 
            break 
cids = np.copy (ids) 
        knn.fit (centers) 
        for i in asrt : 
            if (sizes [i] < threshold) : 
                nearest = knn.kneighbors (centers [i]) [1].flatten () 
                nearest = nearest [(nearest != i)] 
                sel = nearest [np.argmin (sizes [nearest])] 
                total_size = (sizes [sel] + sizes [i]) 
                df [column] [(sizes [column] == cids [i])] = cids [sel] 
                cids [(cids == i)] = cids [sel] 
                sizes [i] = total_size 
                sizes [sel] = total_size 
return df 

    
    

    Fix the buggy line: df [column] [(sizes [column] == cids [i])] = cids [sel]
assistant
#Here is Python code to fix the buggy line: df [column] [(sizes [column] == cids [i])] = cids [sel]
.

df [column] [(df [column] == cids [i])] = cids [sel]