Hey everybody, welcome to another lesson. I will do revising aggregations, averages, for the self-section aggregation of the SQL section in HackerRank. We have this stable city with the column ID name, country code, district and population. And we're asked to query the average population in all the cities where district is California. So for this, we're going to filter first by district having the value of California. And then for those rows, we're going to take all those values and take the average, the values for population. And if you don't remember what the average is, here's some brief reminder. You're just going to take the sum of the elements and divide by the number of elements. In this case, if you have a sum of ak with k being from 1 to n, and then n has to be greater than or equal to 1, so you take the sum and divide by n. So if you want a more expanded version without the sum notation, this is pretty much you have a value plus an order plus an order up to n values and then divided by n. And here's a concrete example. If you have three values, you just sum them and divide by the total number of values. In this case, a plus b plus c, take that sum and then divide by 3. In any case, this will work for dv2 and my SQL at least. So let's get started. We're going to do select star from city. And then from this query, we're going to change it so it becomes what the solution wants. So first, let's make sure it's just the rows for Californians. So we're going to add a where. The district column value is equal to California. And then we have to take the average of population. So we're going to work with the column select population here. And then we're going to use the average function AVG and take that and pass population as the argument. And that will take all the values for all these rows from California and take the population values add them up and then divide by the total number of rows or values. Let's click submit code and see what we get. And here's the answer. We got 113,000.667. And that's revising aggregations averages.