The demographic makeup of regions can offer crucial insights into various socio$-$economic factors. For policymakers, understanding age distributions can be

particularly useful, as it can provide direction for initiatives ranging from educational policy to elderly care. In this section, we will work with a dataset detailing

the age distribution across United States counties, broken down into specific age bins.

The files county$\_$age$\_$dist.csv$,$ fips$\_$state.csv and fips$\_$county.csv contain information about the age distribution of counties in selected brackets

as well as names and FIPS codes and additional information.

$($a$,$ i$)$ Merge all three data frames into one pandas.DataFrame object names data with appropriate column names. $($ii$)$ Remove the column.

Standardize column names and entries to be capitalized according to spelling rules. Remove any preceding whitespace if present for any entries. Run:

data. head $(4)$