Python Tutorial: Exploring and visualizing spatial data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/working-with-geospatial-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the previous video and exercises, we have seen the GeoDataFrame and its basic functionality. A GeoDataFrame from the geopandas library is a spatially-enabled pandas DataFrame Thus, everything you know about how to work with a pandas DataFrame can be used here as well, which makes that you can easily work with the attribute information of the geometries, to manipulate, explore and analyse them. To give one example, taking a subset of the dataframe by filtering on one of the attributes. Let's take the countries dataset again as example, a polygon dataset with all the countries of the world. If we look at the first rows, you see that there is a column indicating the continent. So now we can do a comparison operation to look for all countries of the continent of Africa. This gives us a boolean Series with True and False values, also called a mask. We can then use this boolean mask to filter the original GeoDataFrame. Plotting the subset again, you can see we only have the countries of Africa. This was one example of basic pandas functionality. In the exercises we will encounter some more, such as groupby and joining dataframes. In the previous video and exercises, we have already seen the basic way to quickly plot the geometries in a GeoDataFrame: the plot method. In the remaining of this video, we will show some tricks to customize this plot and to make more advanced visualizations. To start, we will see two ways to adjust the color of the plotted geometries. First, we can specify a uniform color with the "color" keyword. For example, here we specify that all the countries should be plotted in red. Alternatively, you often want to color each polygon depending on one of the attributes of those geometries. For that, we can pass