So let's get going with question number 6. All we want to do here is make a histogram of any one of the four features and label the axes and title as appropriate. So we can use the plt.axes to define our bounding box as ax. We can then call ax.hist and just pass in that Pandas series, which is going to be our petal_length here, our data.petal_length. And we can also specify the number of bins. And because we're working with the axes, we can just say ax.set and say that we're setting the different arguments xlabel, ylabel, and title according to the different string values that we have here. And we can see that output where we have our distribution of petal_length. So we have our histogram set. Another option would have been to directly use the Pandas plotting functionality. And often this is going to be a shortcut that's much easier. So here we're just going to call data.petal_length.plot.hist. So just to show you what this does, if we call this, we have our histogram already. And this is going to, again, just be that bounding box rather than the entire figure. And we're just calling from our Pandas series, which are data.petal_length. We just called .plot.hist and we specify the number of bins as 25. And then we can use the same way that we did before using ax.set in order to provide the labels that we'd like. So now moving to question 7. We now want to create a single plot with histograms for each one of petal_length, petal_width, sepal_length, and sepal_width, all over laid one on top of the other. And then ask, if you do have time, next try to create four individual histogram plots in a single figure, where each plot contains only one feature. And for some hints on how to do this with Pandas plotting methods, they say, look at the visualization guide, there's actually some really quick shortcuts here using Pandas. So all that we do, again, we're using the Pandas functionality, is we say data, so we want our full data frame rather than selecting a single feature .plot.hist. And just doing that on its own, will give us all four of the histograms overlaid one on top of the other. And then we also set the xlabel so that we know that that's going to be the size. So we see how easily we were able to use this data.plot.hist in order to quickly plot out each one of these histograms. And then setting alpha=0.5 is just making it more transparent. Just to show you it would look like without alpha=0.5, they overlap each other and you can't really see through them at all. So you're going to want to keep that alpha and you may want to use that in future graphs as well. Now if we wanted to create four separate plots, we can use the Pandas .hist method. So just straight rather than data.plot.hist, we just say data.hist and we are able to actually, let's pull this out. We're actually able to get these four boxes pulled out separately. Something that you may want to add on is changing the figure size. I'm going to change it to, I don't know, let's say 8 by 8. And now we can see those a bit clearer. So I'm going to change that here as well. So then we're going to end up with our axList. Now I'm going to show you so that you understand the rest of the code here how exactly this is working. We set axList equal to, our image there. And if we look at axList, it's actually going to be an array that contains just the metadata for each one of these different bounding boxes. So we can look at the shape and it's actually going to be an array of 2 by 2. What we have to do here is just flatten that array, so we'll use .flatten. And if we look here, if we do .flatten, we see that our shape is now just all four in one list, in one array. And then we're checking if that value, so each one of these being a bounding box, if that bounding box is in the last row, you can call is_last_row on each one of these boxes. So let's look just at the first value. We can actually call, is that first value is_last_row? We see that it goes to false, and then you wouldn't set the label to size. And if that came out as true, then you would label because it's in the last row as size. And then we do the same to see if it's in the first column. So you see here that it's labeling the x-axis on the bottom row with Size (cm). And for the first column, it is labeling the y labels as frequency. Now, let's move to question 8. Now using Pandas, we want to make a boxplot of each petal and sepal measurements. And hopefully you looked at the documentation there because it is as easy as just calling data.boxplot, and then specifying how you want to break out that boxplot. We may also want to resize this. And then we can see each a bit clearer, where we see the different boxplots broken down the petal_length by the different species or the petal_width by the different species. So that's going to be it for this portion of the lab. In the next section, we will close out with questions 9 and 10.