In module one, We talked about principles which underlie good visualizations. And we pulled from work by Cairo and Tufte. In module two, we opened up this Matplotlib python library and we started with the architecture and we worked our way through some of the most common kinds of plots, scatter plots, line graphs, and bar charts. In this module, we're going to do a bit more of a deeper dive and talk about multiple plots with the same figure, interaction, animations and a few more different kinds of plots that you're going to find useful in your data science journey. Now if you've been doing the assignments, you've undoubtedly been visiting the course, discussion forums, going to Stack Overflow and reading the Matplotlib API documentation. I want to point you to the Matplotlib mailing list is another great resource for support. There's going to be a resource posted in the course on how to browse that list. Now it's pretty common with open source projects to have two different mailing lists, one for developers and one for users. The user's places where we would go to ask most of the questions about how to use the toolkit, but I really encourage you to take a look at the developer archives too to get an idea of how this project sort of evolves and get a look behind the scenes. Okay, let's start this module with a deeper look at subplots. Thus far, we've been using a single axis object to plot a single graph or figure. Sometimes it's useful to show two complex plots side by side to visually compare and let the viewer come to their own understanding. Matplotlib handles this within a single figure object. So let's first set our rendering back into the notebook and then import our pie plot module and NumPy as we're going to need them both. Okay, so I'm going to import Matplotlib pyplot scripting interface and NumPy. We're going to use NumPy lot for and we'll use pandas as well in this module for generating some data. And then I just want to look at and show you what the documentation looks like. So remember, you can always just put a question mark like I have here after a function if you want to know in line a little bit more what it's about. So we see that rendered here and we can see that we've got plt.subplot, takes in args and kwargs and we've got the doc string. And then we can see all sorts of information about the different parameters and what they mean and how to use them. And this should be the same as the web documentation, but it'll be very specific to whichever one you've got installed. So you don't have to try and figure out which version you're using. If we look at the subplot documentation, we see that the first argument is the number of rows, the second argument, the number of columns and the third is the plot number. In Matplotlib, a conceptual grid is being overlaid on the figure and a subplot command allows you to create an access to different portions of this grid. For instance if we wanted to plot side by side, we would call subplot with the parameters 1, 2, and then one is the third parameter. This would allow us to use one row with two columns and set the first axis to be the current axis. So let's do that. Let's create a new figure and then a new subplot with one row and two columns. The first axis object the pyplot will plot against is the left hand side. So I'm just going to go plt.figure to create a new figure. And then here, I'm going to make a subplot and this is the number of rows, this is the number of columns and then this is the axis in that set that we're interested in in working with. And then I'm just going to plot some fake data here. So this is just a linear array of data. You'll notice I'm using a NumPy array. So Matplotlib uses NumPy arrays for almost all of the data and that's important. They're not lists of data. I'm actually creating an array and then we'll just plot this data. Okay, great. So we've got the skinny plot on the left hand side. Now if we made a second call to subplot, we could indicate that we also want to plot on the right hand side. So let's do that. So you see here, I've just copy and pasted things. So remember from our earlier lecture that at the end of every cell, the figure is actually closed automatically by Jupyter. And that might be a little different than some of the docs that you see online which are showing, for instance how to use Matplotlib in a traditional application or in a different kind of editor or on the command line. So here, I'm going to create that same subplot, same data and I'm going to plot it, but now I'm going to get a reference, I'm going to set the active subplot to the second one, that's the right hand side. Then I'm just going to make our data exponential and I'm going to plot it as well. Okay, that's nice. Now we've got two plots each with their own axis object. Now the norm with Matplotlib is that you're going to store that axis object that you get back from the subplot. But then you can call subplot again and at any time the parameters that you're interested in, you can get back to that axis. So you can move back and forth between the axis objects in your scripts if you would like. You don't have to do everything with one axis, then everything with the next and everything with let's say the third, fourth and so forth. Take a look at this figure though, do you notice anything odd about this image thinking in particular of our first week of the course? The two images have different y axis values. Now this would be a problem and potentially mislead the reader if we didn't find a way to lock these axes between the two plots. When you create a new subplot, you're actually able to indicate that you want to share the x and y or both axes using the share x and share y parameters. Let's clean this up and try that. Okay, so I'm just going to plot the first figure and this time I'm actually going to get that axis back. So each time plt.subplot is actually going to return to you the axis that you're referencing, sometimes you want it, sometimes you don't care about it. This is kind of the magic of working with both the object and the pyplot scripting interface. So I just want to take that axis. I'm going to plot onto it this linear data. But then down here, what's really interesting here is that I'm actually going to indicate that we want to share the y axis between this. So I've got this other call to plot.subplot that's going to return the right hand side axis object as ax2. I'm actually not going to use that variable anyway, I could just get rid of it if I wanted to. But I've indicated when I set that up, that I actually want to sharey that. Let's take a look at what that looks like. Okay, there we go, two plots side by side and we've locked the y axis. So you actually see the left hand side plots been squished down a little bit and it makes it much more clear that the data on the right hand side goes much higher. Now those of you have been paying close attention will note that I use the subplot function a second time but I didn't pass in three parameters, I just passed in one. The Matplotlib developers allow you to specify the row, columns and number of the plot that you want with either three parameters like we did at the beginning, 1, 2, 1 or a single parameter like I did now 122 or 122. In this case, the hundreds value, the first is the first argument, the tens value will be taken to be the second argument and the ones value, the third argument. Now I'm frankly not a big fan of the second syntax. It feels pretty hacky and it really saves on typing two commas but actually it limits us to a single digit for each one of those. Computer science folks might feel a little twitch inside like there's something wrong with this notation and I'll say that I certainly get bugged by it every time I see it. But I wanted you to be aware of it so you'd be able to read it when you come across it in the wild whether it be on Stack Overflow or in the Docs. An important fact to remember is that the plot location in the matrix of items is index starting at one and not zero as would be the convention if you're using something like NumPy. So if you're iterating through a matrix or a list, create subplots, remember to start at position plus one. Now there's a nice function called subplots and note here the plural which allows you to get many axis objects at once. And I actually think this is great. You can set up all the subplots you want, you can get references to all those axes and then you can start filling them in. So if we wanted to get a three by three grid with all of the axes x and y ranges locked, we can do this actually pretty simply. So I'm going to create a 3x3 grid of subplots using tuple unpacking and it looks a little complex. So let me just walk through it. I want the figure and then I want a sequence of items and I actually want three items in it 1, 2 and 3 and each one of those items are themselves a sequence. So when I call plt.subplots(3, 3, indicating three rows and three columns, I want them to share all of the same x axis and y axis. I could of course change this back and forth. What actually comes back to me is the figure object always is my top item and then a three tuple of three items in each one being the axis objects and this will become a little bit more clear. I'm going to grab this one ax5. I'm just going to plot that linear data on it so that we can see it. Okay, so the syntax looks a little goofy maybe since we're unpacking the results of these subplots functions directly, but it's really an effective way to build a grid where everything shares an axis. The results look really nice. But note that this method turns off the y and x labels except for the plots which are on the left hand side or on the bottom of the figure. This is actually intentional. You're sharing the axis so Matplotlib trying to make it a little bit more readable, give you a little bit more space in each one of your plots. Of course we can actually just iterate through a list and plot one plot at a time and we don't have to store a reference to the axis if we don't want to. So I'm going to create a new figure. I'm going to call plot.gcf. Remember that's get current figure and if the figure doesn't exist, a brand new figures created for us. Now I'm going to iterate over six potential spots in our figure and I'm going to create a plot with two rows and three columns. So here I've intentionally set i to be in the range of 1 through 7 instead of 0 through 6. And we talked a little bit about how we index unfortunately Matplotlib plots starting at 1, not 0. Now for this demo, I've just decided that we're not going to plot something if we're in position 5 or 3, I'm just going to leave these as holes so you can see where they show up. So if where our i is not a 5 or a 3, then I'm going to add a subplot. Now in the subplot, we're going to specify the first two arguments as the structure we're expecting the figure to take, so two rows and three columns. The third argument is this floating one. It's the position of the item in the figure, in this case it's i. And while we're iterating linearly, this is going to be mapped into that 2, 3 column space, right? So even though i is going 1, 2, 3, 4, 5, 6, 7, it actually gets pushed into the 2, 3 space that we've identified that we want our subplot to be. Once I've added that, I just store the result in ax. And then I'm just going to add some text to the figures to make it more clear which item actually went where. And you can look at the lecture on annotation to read more about that. But I'm just going to essentially just convert i into a string value and throw it into our plot. Let's take a look. So we can see here that we have two rows. We have three columns and we have numbers in the 1, 2, 4 and 6 plot spots, not the 5 or the 3. Those are just blank. And here we haven't shared any of the axis, so everybody has their own axis values. So we now understand why there are abstractions of axis in a figure because a figure might have several axis objects which show multiple views of data. A common data science visual exploration technique is called the SPLOM which stands for scatter plot matrices. These are particularly useful for getting the relationship between a number of different variables from a quick glance. Now SLPLOM's actually similar to what Edward Tufte called a small multiple, a set of visuals that look at related data but slice that data into different small visuals so that you can see both the trees and the forest all at once. Let's take a look at a SPLOM. I'm going to make it by hand and I'm going to use some data from the iris dataset. Okay, let's first capture a list of the variables were interested in. I'm going to bring in pandas. I'm going to read in the iris.csv file into a data frame and I've decided that there's four columns that I'm interested in. I want to see if there's a relationship between the sepal length, the sepal width, the petal length and petal width. And these are all portions and features of a plant flower in particular. So now we need to create a grid of subplots of the size and width, height equal to the number of different variables that we want to explore. And in this case, it's a 4x4 grid. So I'm going to call plt.subplots. I'm going to say length of columns, length of columns and fig size of 10, 10. I can pass that fig size in, right? To set how big I want my figure because we take that star star keyword arcs. Now we want to iterate through each column in our data frame and compare it to each other column in our data frame. I'm going to do this with a couple of nested loops. I'm going to do that by saying for i in range( len( cols)), for j in range( len( cols)). And so this is just our 4x4. And now I just want to plot a scatter plot comparing the columns i and j. Sometimes this is going to be the same column so we would expect to see a diagonal line trend there. And I'm going to set the marker size to 5 just to make things a bit more clear. So again, I can just pass that in and you can check the Docs for scatter to take a look. Remember I've got this axis object back. So I can just say I want this row and this column and these are the values that I actually want to plot in there. Also we've seen that we can plot multiple axes things and it can tend to get cluttered with the axis tick marks and labels. So I'm just going to turn those off just so that we visually don't have to take a look at those. So we don't have to share the axis to shut those off. I can just get the x axis or the y axis at any time and set the label visibility to false. Okay, then I'm going to turn them back on only in that last row so that we can see them along the bottom. And so I'm just going to look for when i = len(cols) -1 and do the same thing for the first column. So let's take a look. Okay, great. We have a nice example of a SPLOM and we can easily compare the length and width of sepals and petals and look for trends at a glance. One that jumps out to me is that the petal width, the bottom row has a pretty linear relationship with the petal length, the third scatter plot over. This doesn't seem to be true if you compared the sepal with to the sepal length by looking at the second row and the first column for instance. In the next module, I want to exploit our new knowledge of subplots while introducing you to a pretty fundamental data science chart, the histogram.
