CODEWITHSUNDEEP
pythonnumpypandasmatplotlib
jax
jax

Reputation: 4207

Debug TypeError: unhashable type: 'numpy.ndarray'

I am working on a kmeans clustering. I have write down a code with the help of some available references on the web but when I run this code it fires an error:

    Traceback (most recent call last):
  File "clustering.py", line 16, in <module>
    ds = df[np.where(labels==i)]
  File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 1678, in __getitem__
    return self._getitem_column(key)
  File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 1685, in _getitem_column
    return self._get_item_cache(key)
  File "/usr/lib/python2.7/dist-packages/pandas/core/generic.py", line 1050, in _get_item_cache
    res = cache.get(item)
TypeError: unhashable type: 'numpy.ndarray'

Though, many previous threads are available with the same error but there is no single solution available that can handle this error in my program. How can I debug this error ?

Code which i used:

from sklearn import cluster
import pandas as pd

df = [
[0.57,-0.845,-0.8277,-0.1585,-1.616],
[0.47,-0.14,-0.5277,-0.158,-1.716],
[0.17,-0.845,-0.5277,-0.158,-1.616],
[0.27,-0.14,-0.8277,-0.158,-1.716]]

df = pd.DataFrame(df,columns= ["a","b","c","d", "e"])

# df = pd.read_csv("cleaned_remove_cor.csv")

k = 3
kmeans = cluster.KMeans(n_clusters=k)
kmeans.fit(df)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
from matplotlib import pyplot
import numpy as np

for i in range(k):
    # select only data observations with cluster label == i
    ds = df[np.where(labels==i)]
    # plot the data observations
    pyplot.plot(ds[:,0],ds[:,1],'o')
    # plot the centroids
    lines = pyplot.plot(centroids[i,0],centroids[i,1],'kx')
    # make the centroid x's bigger
    pyplot.setp(lines,ms=15.0)
    pyplot.setp(lines,mew=2.0)
pyplot.show()

The shape of my DataFrame is (8127x600)

Upvotes: 1

Views: 7614

Answers (1)

jax
jax

Reputation: 4207

I tried and this works for me, conversion of pandas df to numpy matrix: 

df = df.as_matrix(columns= ["a","b","c","d", "e"])

Upvotes: 3

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