In this video, we will learn about another visualization tool: the box plot, and we will learn how to create using Matplotlib. So what is a box plot? A box plot is a way of statistically representing the distribution of given data through five main dimensions. The first dimension is minimum, which is the smallest number in the sorted data. The second dimension is first quartile, which is the point 25% of the way through the sorted data. In other words, a quarter of the datapoints are less than this value. The third dimension is median, which is the median of the sorted data. The fourth dimension is third quartile, which is the point 75% of the way through the sorted data. In other words, three-quarters of the data points are less than this value. And the final dimension is maximum, which is the highest number in the sorted data. Now let's see how we can create a box plot 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 data frame df_canada. So now that we know how our data is stored in the dataframe, df_canada, say we're interested in creating a box plot to visualize immigration from Japan to Canada. As with the other tools that we learned so far, we start by importing Matplotlib as mpl and the pyplot interface as plt. Then we create a new dataframe of the data pertaining to Japan and we're excluding the column total using the years variable. Then we transpose the resulting dataframe to make it in the correct format to create the box plot. Let's name this new dataframe df_japan. Following that we call the plot function on df_japan and we set kind equals box to generate a box plot. Then to complete the figure we give it a title and we label the vertical axis. Finally, we call the show function to display the figure. And there you have it: A box plot that provides a pleasing distribution of Japanese immigration to Canada from 1980 to 2013. In the lab session, we explore box plots in more details and learn how to create multiple box plots as well as horizontal box plots, so make sure to complete this module's lab session. And with this we conclude our video on box plots. I'll see you in the next video.