Clustering histograms of different lengths in Python

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How can I cluster a large dataset of histograms with the same # of bins (8), but of different lengths? Specifically, I'd like to cluster their density distributions. I think I can do this with kmeans or hierarchical clustering, but it seems that the lengths are an issue or my setup is causing ValueError: setting an array element with a sequence.

hist_data[:, 1]
array([
   array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6,
   6, 6, 6, 6, 6, 6, 5, 4, 3, 3, 3, 3, 3, 3, 4, 5, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5,
   5], dtype=int64),
   ...,
   array([6, 6, 6, 6, 6, 6, 6, 5, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
   3, 4, 4, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3,
   3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6, 6, 6, 6,
   6, 6, 6, 6, 6, 6, 6, 5, 5, 4, 4, 3, 3, 3, 3, 4, 5, 6, 6, 6, 6, 6,
   6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
   5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
   5, 5], dtype=int64)], dtype=object)
numpy python
1 Answers

Does this fit the bill:

import numpy as np
from sklearn.cluster import KMeans

data = hist_data[:, 1]
data = np.array([np.bincount(datum, minlength=9) for datum in data])
km = KMeans(n_clusters = 10, init="k-means++").fit(data)  # 10 clusters
print(km.cluster_centers_)  # cluster centres, 10x8 array
user_28354270
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