You will love the answer to this one...
Take a look at your code and notice that you are calling the scoring function and each time you are passing in the exact same values i.e. they are all spitting out the lin_svc.score(). Try interweaving the four scoring calls below the four respective fit calls and you should see the desired variation in the result.
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
#rbf is for gaussian
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X_train, y_train)
print svc.score(X_test, y_test)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X_train, y_train)
print rbf_svc.score(X_test, y_test)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X_train, y_train)
print poly_svc.score(X_test, y_test)
lin_svc = svm.LinearSVC(C=C).fit(X_train, y_train)
print lin_svc.score(X_test, y_test)
Similarly, you are doing the same thing below also.
Hope this helps!