Python Tutorial: Customizing your plots

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you know how to add data to a plot, let's start customizing your plots. First let's customize the appearance of the data in the plot. Here is the code that you previously used to plot the data about the weather in Seattle. One of the things that you might want to improve about this plot is that the data appears to be continuous, but it was actually only measured in monthly intervals. A way to indicate this would be to add markers to the plot that show us where the data exists and which parts are just lines that connect between the data points. The plot method takes an optional keyword argument, marker, which lets you indicate that you are interested in adding markers to the plot and also what kind of markers you'd like. For example, passing the lower-case letter "o" indicates that you would like to use circles as markers. If you were to pass a lower case letter "v" instead, you would get markers shaped like triangles pointing downwards. To see all the possible marker styles, you can visit this page in the Matplotlib online documentation. In these versions of the plot, the measured data appears as markers of some shape, and it becomes more apparent that the lines are just connectors between them. But you can go even further to emphasize this by changing the appearance of these connecting lines. This is done by adding the linestyle keyword argument. Here two dashes are used to indicate that the line should be dashed. Like marker shapes, there are a few linestyles you can choose from, listed in this documentation page. You can even go so far as to eliminate the lines altogether, by passing the string "None" as input to this keyword argument. Finally, you can choose the color that you would like to