k_means | A Python implementation of k-means clustering algorithm | Machine Learning library

While assigning ranks, Make sure to group it on the basis of month.

Complete code:

KMeans of sklearn often expect features to be a 2-d array, instead of a 1-d vector as you passed. So you need to modify your X to be an array. Besides, if you want to rely on group-by-combine mechanism, why not put column indexing within the to-apply function, since assigning from such an operation is cumbersome.

K-means is highly dependent on initialization conditions i.e. the starting point for the means. scikit-learn can do smart initialization based on the data. You can probably configure scikit-learn's version to match your own if you read the documentation carefully. Also, try looking at the source code for more clues.

I'll add the answer to document the issue.

First, when you are doing a lower dimensional embedding make sure that it doesn't need a random seed to ensure repeatability. In this case (PCA) I think it is ok, but other lower dimensional embedding's may vary.

Second, KMeans does not always converge to a global optima and thus can have varying convergence clusters. To keep KMeans repeatable Scikit Learn has the random_state input parameter.

You set this the first time you ran KMeans. This kept the first portion of your code repeatable. To ensure repeatability on the clustering after PCA embedding, set the random state in the same way: