rich and healthy and what this visualization does is take a bunch of data that looks like this and it's quite difficult to interpret if you just look at the raw numbers and turns it into something that you can understand pretty quickly it's also aesthetically pleasing it looks nice so it turns data into art so let's plot the data i'm going to be using python and plotly to create this data visualization if you don't have python installed on your computer and you don't know how to do that then do a search for anaconda python follow that link and follow those instructions once you've installed anaconda you will also need to install plotly because it doesn't come pre-installed with anaconda so search how to do that it's quite straightforward i'm going to create this plot in a jupyter notebook let's get plotting open up your jupiter notebook and in the first cell write import URL_TOKEN as px and then we're going to create a variable that has this data so write gapminder underscore df equals px dot data dot gaap binder execute that cell by pressing shift and enter underneath that write gapminder underscore df execute that cell and let's take a look at this data just looking at these numbers it's very difficult to see what this data is telling us so let's now create the data visualization in a new cell type px dot scatter open brackets data frame equals gapminder underscore df comma x equals gdp per cap y equals life x size equals pop so here we are telling plotly express now what we want the x and y coordinates to be and what we want the size of the point to represent color equals continent so we want each continent to have a different color we're going to give it a title we're going to change the labels from the column name to something more meaningful we want the scale on the x axis to be a log scale because the distribution is quite wide we're going to constrain the range on the y-axis to NUMBER_TOKEN to NUMBER_TOKEN when we hover over the points we want the hover name to be the country and we want to animate this on a year-by-year basis we're going to make the height of the plot NUMBER_TOKEN and we're going to set the maximum size of each point to be NUMBER_TOKEN. now let's take a look
NO_SEGMENT_TOKEN