Who Is Being Served: Demographics

What this module is about

The demographic columns — Race, Ethnicity, Sex_at_Birth, Preferred_Language — describe who the survey reached. Food_Security_Level describes what condition they were in when surveyed.

What you'll achieve

You'll produce basic demographic counts, then cross a need-level column (Food_Security_Level) against geography (City) in a pivot table — the first exercise where two columns are compared against each other instead of one.

Exercises

3. Count records by Race, Ethnicity, Sex_at_Birth, and Preferred_Language.
4. Build a pivot table comparing Food_Security_Level by City.

Why it matters

This is the sensitive core of the dataset. Every count here is a question about people's lives, and demographic breakdowns are exactly the kind of table that gets quoted out of context — write them carefully.

Discussion prompt (ethics)

Race, Ethnicity, and Preferred_Language are collected here. What's the legitimate program-planning use of a table like exercise 3 (e.g., language-access planning, equity reporting) versus the risk of misuse if it were published without context? Consider data minimization and purpose limitation, pointed at these specific columns.

Suggested tools

Pivot tables, or pandas groupby/crosstab.