False Positive Rate in Confusion Matrix
It is possible to have FPR = 1 with TPR = 1 if your prediction is always positive no matter what your inputs are.
TPR = 1 means we predict correctly all the positives. FPR = 1 is equivalent to predicting always positively when the condition is negative.
As a reminder:

FPR = 1 - TNR = [False Positives] / [Negatives]
TPR = 1 - FNR = [True Positives] / [Positives]
I was trying to manually calculate TPR and FPR for the given data. But unfortunately I dont have any false positive cases in my dataset and even no true positive cases. 
So I am getting divided by zero error in pandas. So I have an intuition that fpr=1-tpr. Please let me know my intuition is correct if not let know how to fix this issue.
Thank you