.arff files with scikit-learn?

I really recommend liac-arff. It doesn't load directly to numpy, but the conversion is simple:

import arff, numpy as np
dataset = arff.load(open('mydataset.arff', 'rb'))
data = np.array(dataset['data'])

I found that scipy has a loader for arff files to load them as numpy record arrays. I am not 100% sure that those arrays are suitable for direct consumption by scikit-learn but that should get your started.

Follow renatopp's answer: assume your data is the iris dataset, there should be 5 dimensional with last one is the class label column.

s = svm.SVC()
data_input = data[:,0:4]
labels = data[:,4] # this is the class column
s.fit(data_input, labels)

I think this is something you want.

Solution with scipy.arff

Code:

from scipy.io import arff
import pandas as pd

data = arff.loadarff('file.arff')
df = pd.DataFrame(data[0])
df.head()