Python Tutorial: Census Geography

Want to learn more? Take the full course at https://learn.datacamp.com/courses/analyzing-us-census-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Census data are available for many different geographic levels, from the nation down to the Census block. In this lesson, we will learn about these geographies. So far we have been requesting all US states at once. What if we only want the data for one state, say, Pennsylvania? In that case, replace the star wild card with the state code, 42. This is the geographic identifier, or GEOID, for Pennsylvania. Where can we find these identifiers? Although there are many sources online, in this lesson you will use the Geographic Codes Lookup website maintained by the Missouri Census Data Center. The bureau reports summary statistics for both legal and statistical geographies. Legal/administrative geographies are those that exist as legally defined entities, such as states or counties. Statistical geographies, which include Census tracts, are created by the Census Bureau for purposes of statistical reporting. ZIP Code Tabulation Area, or "ZCTA" is a statistical equivalent to the postal ZIP Code. These geographies exist in a hierarchy, with larger units built from smaller units. Census blocks are the smallest reporting unit and the building blocks for all other geographies. In this image, the connecting lines indicate nesting, so blocks nest in block groups, which nest in Census tracts, which nest in counties. But school districts, shown in green in the middle left of the chart, can cross county lines, so only blocks nest in school districts. We can use this information to specify containing geography using the optional "in" predicate. Here, we request all counties in two states: New Hampshire and Vermont. You cannot use a wildcard with the "in" predicate. You can also request *specific* counties in *one* state. If you specify GEOIDs in the