KNN algorithm Assignment can't understand the question

[spoooder]

It's a set of steps. Do number 1 - load the data. Can you do that?

I could do the following way but my professor wantes me to do it using sklearn where I'm helpless

def knn(data, query, k, distance_fn, choice_fn):
neighbor_distances_and_indices = []

# 3. For each example in the data
for index, example in enumerate(data):
# 3.1 Calculate the distance between the query example and the current
# example from the data.
distance = distance_fn(example[:-1], query)

# 3.2 Add the distance and the index of the example to an ordered collection
neighbor_distances_and_indices.append((distance, index))

# 4. Sort the ordered collection of distances and indices from
# smallest to largest (in ascending order) by the distances
sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices)

# 5. Pick the first K entries from the sorted collection
k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:k]

# 6. Get the labels of the selected K entries
k_nearest_labels = [data[i][1] for distance, i in k_nearest_distances_and_indices]