Why is the Naive Bayes classifier of sklearn faster than sklearns SVM?
The differences in speed between Naive Bayes and SVM simply boils down to the formulation and the assumptions of each model, and has little to do with the particular library or implementation. 

Not only is naive bayes a simple probabilistic classifier, it also makes an additional assumption of independence between its features, so that parameter estimates can be calculated independently and thus possibly very quickly.

In comparison, with SVM, every single record in the data base will require a computation of the distance function to determine the optimal decision boundary. 

We can observe that for SVM, we have a complexity of $O(\text{#features} \times \text{#observations}^2)$ or more depending on the choice of SVM and kernel. On the other hand, for naive bayes we would have a complexity of $O(\text{#features} \times \text{#observations})$. 

Hence in general, we can say that SVM will take longer than Naive Bayes.