When you want to visualize legal case trends over time, you often need to group your data by important columns—such as by 'Year' and 'Outcome'—to reveal patterns. In Python, you can use pandas to read and group data, even from a hardcoded CSV string. To plot these groupings, libraries like matplotlib and seaborn are especially useful. For this challenge, you will work with a hardcoded CSV string that contains legal case data with 'Year' and 'Outcome' columns. First, you will group the data by both year and outcome, count the number of cases for each outcome in each year, and then create a bar chart to visualize these trends. matplotlib or seaborn can create the chart, and you can save the resulting figure as an image file using their built-in save functions. This approach allows you to share your insights visually without needing to read or write any data files.