How to interpret FPR and TPR in ROC curve?
As explained here in the docs of the function returning the ROC Curve 
they return a array because when you interpret a ROC curve what you're looking the performance of the predictor given a threshold. So, it means that, if I'm looking for only classifications that the model is above 80% sure, the ROC curve will have a different TPR/FPR than a threshold of 90%.

If you have further questions, a great link explaining it: http://www.dataschool.io/roc-curves-and-auc-explained/