this gives the following output in our example vu the output turns out to be very close to zerojust like we would expect for normally distributed data centered around zero sothe world makes sense so far now we find the second momentwhich is just another name for variance we can do that with the following codeas we've seen beforeoqwbs wbmt providing the following output vu that output turns out to be about and againthat works out with nice sanity check remember that standard deviation is the square root of variance if you take the square root of it comes out to which is the standard deviation we specified while creating this dataso againthat checks out too the third moment is skewand to do that we're going to need to use the scipy package instead of numpy but that again is built into any scientific computing package like enthought canopy or anaconda once we have scipythe function call is as simple as our earlier twojnqpsutdjqztubutbttq tqtlfx wbmt this displays the following output vu we can just call tqtlfx on the wbmt listand that will give us the skew value since this is centered around zeroit should be almost zero skew it turns out that with random variation it does skew little bit leftand actually that does jive with the shape that we're seeing in the graph it looks like we did kind of pull it little bit negative the fourth moment is kurtosiswhich describes the shape of the tail againfor normal distribution that should be about [fsp dj provides us with another simple function call tqlvsuptjt wbmt