Let's face it. Sometimes our code just isn't working how we expect it to work. When this happens, we fall back to our trusty tools to help us debug: pdb, the logging module, or even simple print statements. But sometimes that just isn't enough, we still can't figure out why our code is broken. We need something more. There are a number of fantastic libraries in python for creating data visualizations. These libraries are commonly used for analyzing and visualizing large sets of data, but don't see as much usage when it comes to helping us write and debug our code. What if we could combine the two? What if we could leverage data visualization tools to understand what our program is doing? Sometimes, by seeing a visual representation of what our code is doing, it becomes much easier to understand why our code is not working. We don't need to create publication quality graphs and charts, we just need to generate quick, one-off visualizations to understand what our code is doing. What we want is the "data visualization" equivalent to print() statements. To help illustrate this point, I will walk through some of the hardest bugs I've had to track down while working on projects such as the AWS CLI and boto3, the AWS SDK for Python. For each bug, I'll show you how I was able to leverage data visualization techniques to troubleshoot and fix these bugs. Come learn how to debug more efficiently by leveraging data visualization.