Assign changes$\_$by$\_$country to a table with one row per country that has two columns: the Country name and the Temperature changes statistic computed across all years in our data set for that country. It may be useful to split this process into two steps. The final table's first $2$ rows should look like this:

countryavg changesAfghanistan$18$Africa$8$

Hint: You can use a group method to apply your changes function to each column in the original data set while grouping on each country. See this example from Olympic data below:

Note This temperature dataset has a few peculiarities, such as including Africa in the country column.

$[53]$:

NORUSA $=$ Table.read$\_$table$(\text{'}$NORUSA$.$csv$\text{'})$

NORUSA$\_$NUMBERS $=$ NORUSA.group$([\text{'}$Year$\text{'},$'Team'$])$ # Number of athletes per year

NORUSA$\_$NUMBERS

$[53]$:

YearTeamcount$1924$Norway$721924$United States$411928$Norway$681928$United States$311932$Norway$661932$United States$541936$Norway$941936$United States$581948$Norway$1261948$United States$81$

$...(34$ rows omitted$)$

Now compute the increases $-$ decreases for the winter olympics for each team

Below code allows us to group 'Team' across all the years of the Olympics to give the following table.

TeamYear changescount changesNorway$2010$United States$2018$

Apply this concept to create the table showing net change for each country.

$[45]$:

NORUSA$\_$NUMBERS.group$(\text{'}$Team$\text{'},$changes$)$

$[45]$:

TeamYear changescount changesNorway$2010$United States$2018$

$[55]$:

changes$\_$by$\_$country $=$