And Kyle Kastner further adds:

Seconding this - it means each step of your RNN (if you have a step() type function + loop) looks like a standard feedforward network without any extra work. I think in numpy C contiguous conventions it also has memory access benefits as your least accessed dimension is on the “farthest” axis
It also means if you are doing convolutional sequence processing you can have seq - n - c - h - w a little bit easier