 Hi, in this video we're going to keep on using the EPA flight data set, these are facility level emissions of greenhouse gas data, but now to exemplify how to calculate measures of spread. So we're going to use those data to find standard deviation, and then the interquartile range in Python. So let's get to it. So first off to find the standard deviation we can find this in a very similar way as to how we found the mean and median above. So, I'll do this in a print function as we did before sustained deviation is equal to. And then we call our whatever variable it is we want to do this on so total reported direct emissions in this case. And our function now is not mean or median but STD for standard deviation. And this actually shares the same units as the underlying variable so in this case million millions metric tons of CO2 equivalent. Okay, so measure of spread basically our measurement of how much these direct emissions vary across all the facilities in this data set comes out to be a little bit more than a million. Pretty large number. Okay, well let's compare this to the interquartile range now. So we're going to calculate the interquartile range using the describe function and there's different ways to do this, this describe function. I want to do it this way because the describe function is actually quite handy for a number of things so let's first find that. So we'll call our variable. Total reported direct emissions. And our function here is just describe and here we have to put in parentheses at the very end. And then we'll, we'll view the output. Here we go so this is a series actually. And it's got some a lot of nice value so we see our standard deviation here, we see our mean value. It also tells us how many values are being used here so 6481. In this case note the use of scientific notation here. And then the minimum zero max of 20 million or so. And then 25% 50% 75% this 50% is the median. So this agrees with exactly what we had in the median above because this 50% refers to the 50th percentile and that is another name for the median. So those are synonymous. And then we also have the 25th percentile and the 75th percentile. So 25% of the values are less than 31,000 and some change here. And 75% of the data values are less than 186,000 and some change here. So our interculture range is the difference between the 75th percentile and the 25th percentile. We can simply find this by referencing these values in the described series above. So it's our 75th percentile minus our 25th percentile. And you can view that value. That's not to be 154,000 or so. So quite a bit less than the standard deviation. So these are two different measures of the spread. So the standard deviation is the typical distance away from the mean. And if you recall, in the comparison of the mean and median, we have a right skewed distribution up here. And so our mean was a lot larger than our median. And so there's a lot more spread about that mean for looking about it that way. Whereas the median and interquartile range are much more concentrated down in this lower end where we have more observations. So in summary, that's how you can find the standard deviation and the interquartile range along the way. We found this handy described function, which is useful for summarizing the data in one fell swoop. And again, we got to do all this with the fascinating flight data set on greenhouse gas emissions across the US. Okay, thank you.