# FAQ: K-Means Clustering - Implementing K-Means: Step 2

This community-built FAQ covers the “Implementing K-Means: Step 2” exercise from the lesson “K-Means Clustering”.

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This exercise can be found in the following Codecademy content:

## FAQs on the exercise Implementing K-Means: Step 2

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I found it easier to use list comprehensions than the method described in the hints:

## Distance formula

``````def distance(a, b):
p1 = a[0] - b[0]
p2 = a[1] - b[1]
return np.sqrt(p1**2 + p2**2)
``````

## Cluster labels for each point (either 0, 1, or 2)

``````labels = np.zeros(len(samples)) # unnecessary in this method but required to check the box
``````

## A function that assigns the nearest centroid to a sample

``````def assign_to_centroid(sample, centroid):
distances = [distance(sample, cent) for cent in centroid]
return np.argmin(distances)  # np.argmin returns index of array which holds minimum value
``````

## Assign the nearest centroid to each sample

``````labels = np.array([assign_to_centroid(sample, centroids) for sample in samples])
``````

## Print labels

``````print(labels)
``````