 and then going into the positive and we could still see, you know, our center part over here and then it tailing off into the negative. Let's do another one here. So this is the GDP per capita current dollars. So we've got the GDP number. So now we're looking at economic data and when you do that you got to think per capita per person kind of thing that GDP divided by the number of people and so now we've got something that is skewed to the right again because we got the tail over to the right. So we took all of this data and you can see we sorted it by by the GDP per capita and from two to one to to the 17, 2, 2, 1 we have the largest amount here and then and then as we get the GDP per capita going up we have many fewer that are falling into those buckets. So most are falling into the bucket on the lower buckets and then as we have the GDP per capita going up we have fewer falling into those buckets with an outlier way out here with the GTP per capita at 221, 221 which is interesting. You would think that'd be a very well, you know, well-off place. So we can actually check it out down here. So if I scroll down it's saying Monaco in this data set. Alright let's go back up top and see what the next one looks like. So now we've got activity per hour calories. So calories and we have a lot of the 42 and the calories going up on a per hour. So if we look at this kind of medical data then we can compile tons of data, right? The stepping data, the calories per hour, the calories per day and whatnot and then obviously we can we may want to start to compile the data. So this one has a bunch of basically the outliers over here. So when we just simply plot this this information we've got it then skewed to the right. Now notice that these outliers are forcing us possibly to have these buckets, you know, maybe out here. So maybe it would be more useful for us to trim off some of these buckets and then we can then we can kind of zoom in on more of the of the data that's in in a relevant range. So and so those are some techniques we could do with the with the graph or with Excel. And so let's see the next one. And so I have the name and the total. Oh I think I think these are like Pokemon characters that way it was that was a this was another kind of I thought it was a funny data set from that was on the Kaggle website or Kaggle I'm not how you say I'm not sure how you say the website but I think it was Pokemon characters and I'm assuming this is like their power strength level you know so if we look if we look at all of the characters and I'm not that familiar with Pokemon but I think you know they fight each like they fight each other like a card game and then you have different power levels and who's gonna win if the two were to fight each other or something like that and there's different categories of the power and whatnot so it gets kind of complicated but if you if you just plot their power levels you've got to the 180 to the to the 241 and I'm assuming this is low power so these are the weak ones 241 to 302 302 to 363 363 to 424 424 to 485 and then pretty high power level most of them seem to be in this fairly high power level which is kind of interesting 45 to 546 and then it drops out and sharply sharply for the more powerful ones 546 to 607 and then you've got the super powerful one over here which apparently is if I scroll all the way down you've got the metto moto moto me you to me too as a to I don't know I don't know who that is but that's you don't want to if you're a Pokemon my takeaway from this data is you don't want to mess with that one hopefully but in any case you can plot just about any set of data that's the point and you can get a pictorial representation and get a better idea of possibly what's going on this might help you to determine how you play your play the Pokemon