How to set k-Means clustering labels from highest to lowest with Python?

Transforming the labels through a lookup table is a straightforward way to achieve what you want.

To begin with I generate some mock data:

import numpy as np

np.random.seed(1000)

n = 38
X_morning = np.random.uniform(low=.02, high=.18, size=38)
X_afternoon = np.random.uniform(low=.05, high=.20, size=38)
X_night = np.random.uniform(low=.025, high=.175, size=38)
X = np.vstack([X_morning, X_afternoon, X_night]).T

Then I perform clustering on data:

from sklearn.cluster import KMeans
k = 4
kmeans = KMeans(n_clusters=k, random_state=0).fit(X)

And finally I use NumPy's argsort to create a lookup table like this:

idx = np.argsort(kmeans.cluster_centers_.sum(axis=1))
lut = np.zeros_like(idx)
lut[idx] = np.arange(k)

Sample run:

In [70]: kmeans.cluster_centers_.sum(axis=1)
Out[70]: array([ 0.3214523 ,  0.40877735,  0.26911353,  0.25234873])

In [71]: idx
Out[71]: array([3, 2, 0, 1], dtype=int64)

In [72]: lut
Out[72]: array([2, 3, 1, 0], dtype=int64)

In [73]: kmeans.labels_
Out[73]: array([1, 3, 1, ..., 0, 1, 0])

In [74]: lut[kmeans.labels_]
Out[74]: array([3, 0, 3, ..., 2, 3, 2], dtype=int64)

idx shows the cluster center labels ordered from lowest to highest consumption level. The appartments for which lut[kmeans.labels_] is 0 / 3 belong to the cluster with the lowest / highest consumption levels.