Python Tutorial: Comparing groups

Want to learn more? Take the full course at https://learn.datacamp.com/courses/improving-your-data-visualizations-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this lesson, we will look at how to compare groups. What exactly does comparing groups mean? For instance, say you wanted to compare how the pollution for the month of August compares to the rest of the months of the year. Does August generally have higher pollution values than the other months? Is the distribution of values wider? narrower? These comparisons can help shed light on patterns and are crucial for accurately representing your data. Say you want to compare just two classes. For instance, pollution values for Denver as compared to the rest of the cities in our dataset. If you have a continuous measure, as we do with our pollution data, a great way to compare the values is to use overlaid kernel density plots. The kernel density estimator (or KDE) plot is a kind of continuous histogram. To construct the distribution a series of small 'kernel' distributions (usually normal distributions) are stacked on top of each datapoint. The result is a continuous estimation of the underlying density of the data. This helps you avoid comparing overlaid histograms as you can plot a simple line instead of having the user guess if histogram bars are stacked or overlaid. Here we are again adding a column to our pollution data containing info if the city is Denver and then feeding this modified DataFrame to Seaborn's distplot() function. The two curves clearly show a difference in the shape of the two groups distribution's, with the red Denver curve, shifted to the left from the rest of the cities. The KDE here has the benefit of showing the area of overlap between the distributions much better than a histogram as the overlapping lines are much easier to decipher than overlapping bars. One caveat of KDEs is that the kernels fill