The package plotly.express of Python is really handy to visualize the dynamic evolution of data over time with different types of graphs. In this exercise, I use the bar chart with a slider for the timeline to visualize the number of case of infection by COVID-19 confirmed in Belgium, separated into different groups of age and sexes. I use the data from Sciensano, with the dataset updated every day. So if you run the codes, you will have the most updated data to visualize.

One of the graph that you can make with the code will look like below. It represents the number of case of infection per day, separated into different groups of age, with females in red on the left and males in blue on the right. You can observe the dynamic of the evolution by clicking on the play button below the graph or manually drag on the slider to choose the dates.

If you are curious to make it yourself, you can check below my Jupyter Notebook on Github for a step-by-step tutorial.