Randomly define the training size in train_test_split sklearn

I am trying to split data that I have into 40% training and 60% validation, then I want to repeat this 30 times, each time with random training and different validation. How can I do this? (not using Kfold)

This is what I wrote but I am getting the same results every time for accuracy, I do not know how to do this with different training and validation each time. My accuracy is the same for each iteration, I don't know why.

for i in range (30):
      X_train, X_test, y_train, y_test =train_test_split(df,y, 
      train_size=0.4, shuffle=True)
      metrics.accuracy_score(linsvc.predict(X_train), R_train)