TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed

Code Sample, a copy-pastable example if possible

samples = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data", sep=',',header=None)
varieties = pd.DataFrame(samples.iloc[:,0])
kmeans = KMeans(n_clusters = 3)
labels = kmeans.fit_predict(samples)
#setting 'labels' according to given data
labels += 1
#converting 'labels' to pandas DataFrame
labels = pd.DataFrame(labels)
df = pd.DataFrame({'labels':[labels], 'varieties':[varieties]})
ct = pd.crosstab(df['labels'],df['varieties'])

Problem description

My Dataset is like

                                          labels  \

0 0 0 2 1 2 2 2 3 2 4 3 5 …

                                       varieties  

0 0 0 1 1 1 2 1 3 1 4 1 5 …

Expected Output

output from “crosstab” function

] # TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed

[TomAugspurger]

You might want to ask on stackoverflow. But in general you don’t want nested data frames. The columns should just be the arrays themselves.

On Apr 5, 2017, at 12:03, aashray18521 [e-mail] wrote:

@TomAugspurger I want to use these dataframes (labels and varieties) for ‘crosstab’ function. Please do let me know how I can do that? scikit-learn is being used by me for other tasks in the remainder of my code

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[aashray18521]

@TomAugspurger I want to use these dataframes (labels and varieties) for ‘crosstab’ function. Please do let me know how I can do that? scikit-learn is being used by me for other tasks in the remainder of my code