 I want to show how I manipulate data frames in multiple languages. What I'll be showing is SQLite, Python, Julia, and R. I'll be using the same data set, the diamonds CSV. The first file I have loaded in the buffer right now is SQLite. I'm just kind of showing what the data looks like, just selecting the color and price. But the main idea here is the common table expression CTEs. In the first common table expression, I just show pretty much what a CTE is. CTEs start with a with statement. So I have with a. Everything within those parentheses are operations on the data frame. And then I can select a star from a. I have a second set of CTEs, which is kind of more the main point. This is what I'll be replicating in the other languages. What I'm doing here is I'm selecting color. I'm calculating the average price. And then I'm passing that to a second CTE where I do a case one statement, which is just a boolean where given a certain price, I say if it's a high price, I give it a boolean of one zero. Okay, now I'm going to change to Python. So this is I have another video on on chaining in pandas. So if you want some more details, I go ahead and search for for my pandas chaining method. But it's the same idea here where I select with loc group by color, calculate the mean, and then assign a new column called high end. The next example is going to be Julia Julia uses a few libraries data frames meta data frames CSV and statistics. I'm going to be loading the same diamonds data set. But there is a pipe command. So in the command line, you pipe things with just a vertical bar. With these libraries in Julia, there's a vertical bar and a right arrow. And so what this is saying is send the diamonds data frame to select, select the columns price and color, and then that's our new data frame, and then send that to this grouping by, and in this group by operation, we're going to be grouping by color and within the operational be averaging the price and also making a new column high end. And high end again, it's it's one if it's a high price, else zero. The final example I'll be showing is our using the dplyr library looks fairly similar to the Julia and pandas example, except for the pipe here is McGridder. The percent right arrow percent is is how we're piping. Again, we're just sending a data frame to select then sending that to group by then summarize and mutate. That's it.