Question $2-$ Data Manipulating: Pandas $(18$ Pts$)$

Implement the following tasks by using Pandas library.

$[$Task$-2\mathrm{.}1](2$ Pts$)$ Load "state$\_$areas.csv$",$ "state$\_$abbrevs.csv$"$ and "state$\_$population.csv$"$ files into separate DataFrames and print them.

$[$Task$-2\mathrm{.}2](4$ Pts$)$ Merge "state$\_$abbrevs" and "state$\_$areas". Name the DataFrame as $"$df$1".$ Print the result.

$[$Task$-2\mathrm{.}3](4$ Pts$)$ Merge $"$df$1"$ and "state$-$pop" using necessary keywords to obtain the Data Frame given in Figure$-1.$ Name the DataFrame as $"$df$2".$ Print the result.

$2$

$\backslash$table$[[,$state,abbreviation,rea,ages,year,population$],[0,$Alabama,$AL,52423,$total,$2012\mathrm{.}0,4817528\mathrm{.}0],[1,$Alaska,$AK,656425,$total,$2012\mathrm{.}0,730307\mathrm{.}0],[2,$California,CA$,163707,$total,NaN,$37999878\mathrm{.}0],[3,$Louisiana,LA$,51843,$total,$2012\mathrm{.}0,4602134\mathrm{.}0],[4,$Louisiana,LA$,51843,$under$18,2011\mathrm{.}0,1116579\mathrm{.}0],[5,$Mississippi,MS$,48434,$under$18,2012\mathrm{.}0,742941\mathrm{.}0],[6,$Washington,WA$,71303,$total,$2011\mathrm{.}0,$NaN