 So we've already talked about the CSV reader, but there's also another approach known as the dict reader and the entire idea is what this is going to do is now it takes our rows inside of our data set and it's going to convert them into individual dictionaries that we can then use our column headers as our reference points. So we've already seen sort of this approach inside of this example that we're working off of. There's only a few changes that really need to be made here. The first one is that I need to change this into a dict reader because I'm no longer converting it into a reader, I'm converting it into a different type of object. Now the other little approach that you may not notice is I actually no longer need this entire skip row line because the dict reader is going to see, oh well, by default it says I know what the field names are. I already know because I'm reading them in, that's the first line I'm going to process. Those are now all of the column headers or the keys that are going to be input into the row. So I actually can skip over this entirely. Now the last little bit is instead of referencing all of the individual data points by an index, again those column headers have been stored and processed as individual keys. And so I can reference each one of the data points at those keys with their column header. In our case I'm just coming in here and doing some quick little copy pasting. There we are. And so when I run through this, I should get the same values, which I do, and it's just a different way to do it. Now if you're thinking about which one's the better way, there is no better way. Sometimes you might want to process things, or sometimes your data set, these column headers could change over time. They could be dates, and so you don't really want to have dates for keys. Different things can really change depending on what you're dealing with. So it's really more of a play with each one and see which one works out for you.