Learn to build histograms in Python using matplotlib.pyplot's hist() function. The tutorial shows how to divide data into bins and count values in each, then demonstrates with twelve sample numbers split into three bins. A real example compares EU population by age and gender in 2010 versus projected 2050.
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In this video, I'll introduce you to the histogram. The histogram is a type of visualization that's very useful to explore your data. It can help you to get an idea about the distribution of your variables. To see how it works, imagine 12 values between 0 and 6. I've put them along a number line here. To build a histogram for these values, you can divide the line into equal chunks called bins. Suppose you go for three bins that each have a width of two. Next, you count how many data points sit inside each bin. There's four data points in the first bin, six in the second bin and two in the third bin. Finally, you draw a bar for each bin. The height of the bar corresponds to the number of data points that fall on this bin. The result is a histogram, which gives us a nice overview on how the 12 values are distributed. Most values are in the middle, but there are more values below 2 than there are above 4. Of course. Also, Matplotlib is able to build histograms as before. You should start by importing the pyplot package that's inside matplotlib. Next, you can use the hist function. Let's open up its documentation. There's a bunch of arguments you can specify, but the first two here are the most important ones. X should be a list of values you want to build a histogram for. You can use the second argument bins to tell Python in how many bins the data should be divided. Based on this number, HIST will automatically find appropriate boundaries for all bins and calculate how many values are in each one. If you don't specify the bins argument, it will be 10 by default. So to generate the histogram that you've seen before, let's start by building a list with the 12 values. Next, you simply call hist and pass this list as an input so it's matched to the argument X. I also specified the bins argument to be three so that the values are divided in three bins. If you finally call the show function a nice histogram results. Histograms are really useful to give a bigger picture. As an example, have a look at this so called population pyramid. The age distribution is shown for both males and females in the European Union. Notice that the histograms are flipped 90 degrees. The bins are horizontal. Now the bins are largest for the ages 40 to 44 where there are 20 million males and 20 million females. They are the so called baby boomers. These are figures of the year 2010. What do you think will have changed in 2050? Let's have a look. The distribution is flatter. And the baby boom generation has gotten older. With the blink of an eye, you can easily see how demographics will be changing over time. And that's the true power of histograms at work here. Now head over to the exercises to experiment with histograms yourself.
