Python Tutorial: Learn Python For Data Science - Python Tutorial : Basic plots with python matplotlib

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-python-for-data-science at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- This first chapter is about data visualization, which is a very important part of data analysis. First of all, you will use it continuously to explore your dataset. The better you understand your data, the better you'll be able to extract insights. And once you've found those insights, again, you'll need visualization to be able to share your precious insights with other people. As an example, have a look at this beautfil plot. It's made by the Swedish professor Hans Rosling. His talks about global development have been viewed millions of times. And what makes them so intriguing, is that by making beautiful plots, he allows the data to tell their own story. Here we see a bubble chart, where each bubble represents a country. The bigger the bubble, the bigger the country's population, so the two biggest bubbles here are China and India. There are 2 axes. The horizontal axis shows the GDP per capita, in US dollars. The vertical axis shows life expectancy. We clearly see that people live longer in countries with a higher GDP per capita. Still, there is a huge difference in life expectancy between countries on the same income level. Now why do I tell you all of this? Well, because by the end of this chapter, you'll be able to build this beautiful plot yourself! There are many visualization packages in python, but the mother of them all, is matplotlib. You will need its subpackage pyplot. By convention, this subpackage is imported as plt, like this. For our first example, let's try to gain some insights in the evolution of the world population. I have a list with years here, year, and a list with corresponding populations, expressed in billions, pop. In the year 1970, for example, 3.7 billion people lived on planet Earth. To plot this data as a line chart, we call plt.plot() and use our two lists as arguments. The first argument corresponds to the hozontal axis, and the second one to the vertical axis. You might think that a plot will pop up right now, but Python's pretty lazy. It will wait for the show() function to actually display the plot. This is because you might want to add some extra ingredients to your plot before actually displaying it, such as titles and label customizations. I'll talk about that some more later on. Just remember this: the plot() function tells Python what to plot and how to plot it. show() actually displays the plot. When we look at our plot, we see that the years are indeed shown on the horizontal axis, and the populations on the vertical axis. There are four datapoints, and Python draws a line between them. In 1950, the world population was around 2 point 5 billion. In 2010, it was around 7 billion. So the world population has almost tripled in sixty years, that's pretty scary. What if the population keeps on growing like that? Will the world become over populated? You'll find out in the exercises. Let me first introduce you to another type of plot: the scatter plot. To create it, we can start from the code from before. This time, though, you change the plot function to scatter. The resulting scatter plot simply plots all the individual datapoints; Python doesn't connect the dots with a line. For many applications, the scatter plot is often a better choice than the line plot, so remember this scatter function well. You could also say that this is a more -honest- way of plotting your data, because you can clearly see that the plot is based on just four data points. Now that we've got the basics of matplotlib covered, it's your turn to build some awesome plots!