![]() It has two parameters: x_axis_label and _axis_label. ![]() Create the figure p with the figure() function.Your job is to plot the Latin America data with the circle() glyph, and the Africa data with the x() glyph.įigure has already been imported for you from otting. Each set of x and y data has been loaded separately for you as fertility_africa, female_literacy_africa, fertility_latinamerica, and female_literacy_latinamerica. In this exercise, you will plot female literacy vs fertility for two different regions, Africa and Latin America. Create and display the output file using show() and passing in the figure p.īy calling multiple glyph functions on the same figure object, we can overlay multiple data sets in the same figure.Use the output_file() function to specify the name 'fert_lit.html' for the output file.Add a circle glyph to the figure p using the function p.circle() where the inputs are, in order, the x-axis data and y-axis data.It has two parameters: x_axis_label and y_axis_label. Import the figure function from otting, and the output_file and show functions from bokeh.io.Note: You may have to scroll down to view the lower portion of the figure. You can click on the question mark sign for more details on any of these tools. Your job is to create a figure, assign x-axis and y-axis labels, and plot female_literacy vs fertility using the circle glyph.Īfter you have created the figure, in this exercise and the ones to follow, play around with it! Explore the different options available to you on the tab to the right, such as "Pan", "Box Zoom", and "Wheel Zoom". ![]() The x-axis data has been loaded for you as fertility and the y-axis data has been loaded as female_literacy. This dataset highlights that countries with low female literacy have high birthrates. In this example, you're going to make a scatter plot of female literacy vs fertility using data from the European Environmental Agency. # AAPL Stock aapl_url = '' # Automobile miles per gallon auto_url = '' # Gapminder gap_url = '' # Blood glucose levels glucose_url = '' # Female literacy and birth rate female_url = '' # Olympic medals (100m sprint) sprint_url = '' # State coordinates state_url = '' ![]()
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