Now, NumPy also makes it incredibly easy to compute the standard deviation and variance. If you want to compute the actual standard deviation of this dataset that we generated, you just call the std function right on the dataset itself. So, when NumPy creates the list, it's not just a normal Python list, it actually has some extra stuff tacked onto it so you can call functions on it, like std for standard deviation. Let's do that now:

incomes.std() 

This gives us something like the following output (remember that we used random data, so your figures won't be exactly the same as mine):

20.024538249134373 

When we execute that, we get a number pretty close to 20, because that's what we specified when we created our random data. We wanted a standard deviation of 20. Sure enough, 20.02, pretty close.

The variance is just a matter of calling var.

incomes.var() 

This gives me the following:

400.98213209104557 

It comes out to pretty close to 400, which is 20². Right, so the world makes sense! Standard deviation is just the square root of the variance, or you could say that the variance is the standard deviation squared. Sure enough, that works out, so the world works the way it should.