In this video, we will learn about another visualization tool: the histogram, and we will learn how to create it using Matplotlib. Let's start by defining what a histogram is. A histogram is a way of representing the frequency distribution of a numeric dataset. The way it works is it partitions the spread of the numeric data into bins, assigns each datapoint in the dataset to a bin, and then counts the number of datapoints that have been assigned to each bin. So the vertical axis is actually the frequency or the number of datapoints in each bin. For example, let's say the range of the numeric values in the dataset is 34,129. Now, the first step in creating the histogram is partitioning the horizontal axis in, say, ten bins of equal width, and then we construct the histogram by counting how many datapoints have a value that is between the limits of the first bin, the second bin, the third bin, and so on. Say the number of datapoints having a value between 0 and 3,413 is 175. Then we draw a bar of that height for this bin. We repeat the same thing for all the other bins, and if no datapoints fall into a bin then that bin would have a bar of height 0. So how do we create a histogram using 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 we're interested in visualizing the distribution of immigrants to Canada in the year 2013. The simplest way to do that is to generate a histogram of the data in column 2013, and let's see how we can do that with Matplotlib. First, we import Matplotlib as mpl and its scripting interface as plt. Then we call the plot function on the data in column 2013 and we set kind equals hist to generate a histogram. 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 histogram that depicts the distribution of immigration to Canada in 2013, but notice how the bins are not aligned with the tick marks on the horizontal axis. This can make the histogram hard to read. So let's try to fix this in order to make our histogram more effective. One way to solve this issue is to borrow the histogram function from the Numpy library. So as usual we start by importing Matplotlib and its scripting interface, but this time we also import the Numpy library. Then we call the Numpy histogram function on the data in column 2013. What this function is going do is it is going to partition the spread of the data in column 2013 into 10 bins of equal width, compute the number of datapoints that fall in each bin, and then return this frequency of each bin which we're calling count here and the bin edges which we're calling bin_edges. We then pass these bin edges as an additional parameter in our plot function to generate the histogram. And there you go: A nice looking histogram whose bin edges are aligned with the tick marks on the horizontal axis. In the lab session, we explore histograms in more details, so make sure to complete this module's lab session. And with this, we conclude our video on histograms. I'll see you in the next video.
