Need help in understanding a small example
Regarding your first question, by dividing by the length, you get the percentage of each category, and you can see that one category has a very low percentage. 

If you use the regular train_test_split, the proportion of the categories will be different in each set, as the split is made randomly, and this can introduce a bias.

Imagine categories with a very low number of observations, you could even have categories missing entirely in either of the sets, which will cause trouble for your model.

The use of stratified sampling allows you to have the same proportion for the categories. That is why you see the same results: it is the goal of this sampling.