For this task, you will use a dataset on airline routes. The data dictionary$/$glossary for this dataset is as follows:

S$\_$CITY: Starting City.

E$\_$CODE: Destination Airport Code.

E$\_$CITY: Destination City.

COUPON: Number of coupons for the flight $($a leg is a part of the trip covered by one flight number$).$

NEW: A variable indicating whether an airline recently started serving this route.

VACATION: A binary variable indicating whether this is a vacation destination.

SW: Southwest Airlines indicator, a binary variable.

HI: Herfindahl Index $-$ a measure of market concentration.

S$\_$INCOME: Starting city's average income.

E$\_$INCOME: Destination city's average income.

S$\_$POP: Starting city's population.

E$\_$POP: Destination city's population.

SLOT: Controlled $($or slot controlled$)$ airport indicator, a binary variable.

GATE: Gate controlled airport indicator, a binary variable.

DISTANCE: Distance between the starting city and the destination city in miles.

PAX: Number of passengers on the flight.

FARE: Average fare for the flight.

a$)$ Read in this dataset as df$,$ keeping only the following columns:

VACATION

S$\_$INCOME

E$\_$INCOME

GATE

DISTANCE

b$)$ Print the first five and the last five rows.

c$)$ Print the column names of df$.$

d$)$ Print the data type and the count of non$-$null values for each column.

e$)$ Print summary statistics of all numeric columns in the dataset. Interpret the output.

f$)$ Print the percentage of observations belonging to each Gate type