Different accuracy for python (Scikit-Learn) and R (e1071)

For the same dataset (here Bupa) and parameters i get different accuracies.

What did I overlook?

R implementation:

data_file = "bupa.data"
dataset = read.csv(data_file, header = FALSE)
nobs <- nrow(dataset) # 303 observations 
sample <- train <- sample(nrow(dataset), 0.95*nobs) # 227 observations
# validate <- sample(setdiff(seq_len(nrow(dataset)), train), 0.1*nobs) # 30 observations
test <- setdiff(seq_len(nrow(dataset)), train) # 76 observations
svmfit <- svm(V7~ .,data=dataset[train,],
              type="C-classification",
              kernel="linear",
              cost=1,
              cross=10)
testpr <- predict(svmfit, newdata=na.omit(dataset[test,]))
accuracy <- sum(testpr==na.omit(dataset[test,])$V7)/length(na.omit(dataset[test,])$V7)

I get accuracy: 0.94

but when i do as following in python (scikit-learn)

import numpy as np
from sklearn import cross_validation
from sklearn import datasets
import pandas as pd
from sklearn import svm, grid_search

f = open("data/bupa.data")
dataset =  np.loadtxt(fname = f, delimiter = ',')
nobs = np.shape(dataset)[0]
print("Number of Observations: %d" % nobs)
y = dataset[:,6]
X = dataset[:,:-1]
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.06, random_state=0)

clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
scores = cross_validation.cross_val_score(clf, X, y, cv=10, scoring='accuracy')

I get accuracy 0.67

please help me.