I focused on the “armed” and “race” columns when creating a Python heatmap for the police-shootings dataset as part of my investigation. We used matplotlib, seaborn, and pandas to visualise the distribution of racial groups by armed status. I made the heatmap more precise such that it only displayed the values “gun,” “knife,” and “unarmed.” The resulting chart, which was primarily coloured red, provided a clear explanation of these particular armed statuses and racial populations. This helped me understand how heatmaps may be tailored to extract relevant information from large, complicated datasets, improving my data visualisation skills