The file disney$\_$movies.csv in the data$/$ directory contains information about $579$ such Disney movies. Its columns are described below.

Column Description

'movie$\_$title' Name of the movie

'genre' Genre of the movie

'MPAA$\_$rating' Content rating, determining suitability for children

'total$\_$gross' Total box office revenue

'adjusted$\_$gross' Total box office revenue, after adjustment for inflation

'year' Year of release

Question $7\mathrm{.}1.$ Read this file into a DataFrame called disney$\_$movies.

$\text{'}$

disney$\_$movies $=$ bpd$.$read$\_$csv$(\text{'}$data$/$disney$\_$movies.csv$\text{'})$

disney$\_$movies

movie$\_$title genre MPAA$\_$rating total$\_$gross adjusted$\_$gross year

$0$ Snow White and the Seven Dwarfs Musical G $18492548552289532511937$

$1$ Pinocchio Adventure G $8430000021882290521940$

$2$ Fantasia Musical G $8332000021870908081940$

$3$ Song of the South Adventure G $6500000010785105791946$

$4$ Cinderella $(1950)$ Drama G $850000009206087301950$

$.....................$

$574$ The Light Between Oceans Drama PG$-1312545979125459792016$

$575$ Queen of Katwe Drama PG $887438988743892016$

$576$ Doctor Strange Adventure PG$-132325329232325329232016$

$577$ Moana Adventure PG $2460820292460820292016$

$578$ Rogue One: A Star Wars Story Adventure PG$-135294839365294839362016$

$579$ rows $\backslash$times $6$ columns

grader.check$("$q$7\_1")$

q$7\_1$ passed!

Question $7\mathrm{.}2.$ Since the values in total$\_$gross are so large, let's add a column to disney$\_$movies called 'total$\_$gross$\_$millions' that contains the total box office revenue in millions of dollars. Similarly, create a column called 'adjusted$\_$gross$\_$millions' with the inflation$-$adjusted amounts in millions of dollars. Then, drop the original 'total$\_$gross' and 'adjusted$\_$gross' columns. Do not round.