In this video, we will learn about an additional visualization tool: the scatter plot, and we will learn how to create it using Matplotlib. So what is a scatter plot? A scatter plot is a type of plot that displays values pertaining to typically two variables against each other. Usually it is a dependent variable to be plotted against an independent variable in order to determine if any correlation between the two variables exists. For example, here is a scatter plot of income versus education and by looking at the plotted data one can conclude that an individual with more years of education is likely to earn a higher income than an individual with fewer years of education. So how can we create a scatterplot with Matplotlib? Before we go over the code to do that, let's do a quick recap of our dataset. Recall that each row represents a country and contains metadata about the country such as where it is located geographically and whether it is developing or developed. Each row also contains numerical figures of annual immigration from that country to Canada from 1980 to 2013. Now let's process the dataframe so that the country name becomes the index of each row. This should make retrieving rows pertaining to specific countries a lot easier. Also let's add an extra column which represents the cumulative sum of annual immigration from each country from 1980 to 2013. So for Afghanistan for example, it is 58,639, total, and for Albania it is 15,699 and so on. And let's name our dataframe df_canada. So now that we know how our data is stored in the dataframe, df_canada, say were interested in plotting a scatter plot of the total annual immigration to Canada from 1980 to 2013. To be able to do that, we first need to create a new dataframe that shows each year and the corresponding total number of immigration from all the countries worldwide as shown here. Let's name this new dataframe, df_total. In the lab session, we will walk together through the process of creating df_total from df_canada, so make sure to complete this module's lab session. Then we proceed as usual. We import Matplotlib as mpl and its scripting layer, the pyplot interface, as plt. Then we call the plot function on the data frame df_total and we set kind equals scatter to generate a scatter plot. Now unlike the other data visualization tools were only passing the kind parameter was enough to generate the plot, with scatter plots we also need to pass the variable to be plotted on the horizontal axis as the x-parameter and the variable to be plotted on the vertical axis as the y-parameter. In this case, we're passing column year as the x-parameter and column total as the y-parameter. Then to complete the figure we give it a title and we label its axes. Finally, we call the show function to display the figure. And there you have it. A scatter plot that shows total immigration to Canada from countries all over the world from 1980 to 2013. The scatter plot clearly depicts an overall rising trend of immigration with time. In the lab session we explore scatter plots in more details and learn about a very interesting variation of this scatter plot, a plot called the bubble plot, and we learn how to create it using Matplotlib. So make sure to complete this module's lab session. And with this, we conclude our video on scatter plots. I'll see you in the next video.