Task

You will study the dataset called nycflights$13.$ It gives information about all $336,776$ flights that departed

in $2013$ from the three New York $($in the US$)$ airports $($EWR$,$ JFK$,$ and LGA$)$ to destinations in the United

States, Puerto Rico, and the American Virgin Islands.

Download the following data files from our unit site $($Learning Resources $->$ Data$)$:

$$ nycflights$13\_$flights.csv$.$gz $$ flights information,

$$ nycflights$13\_$airlines.csv$.$gz $$ decodes two letter carrier codes,

$$ nycflights$13\_$airports.csv$.$gz $$ airport data,

$$ nycflights$13\_$planes.csv$.$gz $$ plane data,

$$ nycflights$13\_$weather.csv$.$gz $$ hourly meteorological data for LGA, JFK$,$ and EWR.

Refer to the comment lines in the CSV files $($note that they are gzipped$)$ for more details about each

column.

You aim is to use pandas to come up with results equivalent to those that correspond to example SQL

queries.

$1.$ Establish a connection with a new SQLite database on your disk.

$2.$ Export all the CSV files to the said database.

$3.$ For each of the SQL queries below $($each query in a separate section$),$ write the code that yields

equivalent results using pandas only and explain $$ in your own words $$ what it does.