Government analysts often work with structured data that describes populations, budgets, or services. Common formats include demographic tables—where each row represents a region with columns for population, income, or age—and budget allocations, which might list departments and their yearly funding. In Python, such tabular data is typically represented as a list of dictionaries. Each dictionary corresponds to a record (such as a region or department), with keys for each attribute and values for the corresponding data. This approach allows you to organize complex datasets in a way that is both readable and easy to manipulate.

With your data organized as a list of dictionaries, you can easily access and manipulate information. For instance, to find all regions with a population above 100,000, you can filter the list by checking each dictionary's population value. If you want to calculate the average median income, you can loop through the list, extract each region's median_income, and compute the mean. This structure makes it straightforward to extract insights, generate summaries, or prepare data for visualization.