we have , data points centered around with normal distribution and standard deviation of measure of the spread of this datayou can see that the most common occurrence is around and as we get further and further from thatthings become less and less likely the standard deviation point of that we specified is around and around you can see in the histogram that this is the point where things start to fall off sharplyso we can say that things beyond that standard deviation boundary are unusual using python to compute standard deviation and variance nownumpy 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 generatedyou just call the tue function right on the dataset itself sowhen numpy creates the listit' not just normal python listit actually has some extra stuff tacked onto it so you can call functions on itlike tue for standard deviation let' do that nowjodpnfttue this gives us something like the following output (remember that we used random dataso your figures won' be exactly the same as mine)when we execute thatwe get number pretty close to because that' what we specified when we created our random data we wanted standard deviation of sure enough pretty close the variance is just matter of calling wbs jodpnftwbs this gives me the followingit comes out to pretty close to which is rightso the world makes sensestandard deviation is just the square root of the varianceor you could say that the variance is the standard deviation squared sure enoughthat works outso the world works the way it should