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”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Implementing K-Means: Step 2

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head here.

Looking for motivation to keep learning? Join our wider discussions.

Learn more about how to use this guide.

Found a bug? Report it!

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!


Distances to each centroid

for i in range(len(samples)):
for j in range(k):
distances[j] = distance(sepal_length_width[i],centroids[j])
cluster = np.argmin(distances)
labels[i] = cluster

Codecademy’s solution should probably include that second for loop for k, since everything else is based on a variable number of centroids.

Also, an alternative solution to using np.zeros is to just do [0] * k, or [0] * len(samples).