Homelessness Analysis with Pandas

Use Pandas to analyze demographic and social data. Social datasets often contain complex but meaningful information. In this project, you’ll work with a homelessness dataset that includes data about different regions or cities, their demographic details, and the number of people experiencing homelessness. The goal is to use Pandas to explore, organize, and better understand the relationships within this sensitive but important dataset. You’ll begin by loading the dataset into a Pandas DataFrame and examining its structure—rows, columns, and data types. Then, you’ll apply filtering and indexing to focus on specific states, cities, or population groups. You’ll calculate basic descriptive statistics to get an overview of homelessness across regions. Next, you’ll use groupby and aggregation to compare homelessness rates by state, region, or demographic category. You’ll also practice creating new calculated columns—such as the percentage of the population that is homeless—providing more meaningful context for comparisons. Sorting operations will help highlight areas with the highest and lowest rates, while subsetting will allow you to build focused views of the data. By the end of this project, you will be able to: Load and inspect a demographic dataset with Pandas. Filter and subset data to focus on specific groups or regions. Apply grouping and aggregation to summarize homelessness statistics. Create calculated columns for percentages and comparisons. Organize and transform the dataset to highlight key insights. This project gives you practice applying Pandas to social and demographic data, showing how simple manipulations can reveal important trends. By working hands-on, you’ll strengthen both your technical skills and your ability to interpret real-world issues through data.