I have a created a function using these instructions:

Using the Gower distances in the matrix D computed by the provided function, find the most and least similar pairs of colleges in the dataset $(15$ points$).$

Note that an item is most similar to itself $($distance $=0.)$ but you need to disallow this case since we actually care about finding two distinct items not along the diagonal that are most similar. One quick way to accomplish this is to replace the zeros along the diagonal of the distance matrix D returned by the gower$\_$distances function, with a very large number $($e$.$g$.1000)$ that wouldn't occur as a distance in practice.

You may also find numpy's unravel$\_$index function, in combination with argmax or argmin, useful for finding min$/$max elements in an array. Remember that the least similar elements will have maximum distance from each other, and most similar will have minimum distance.

Your function should accept as an argument the college dataframe $($df$)$ provided above. The gower$\_$distances$()$ function will also utilize this dataframe. See the gower$\_$distances$()$ function definition above.

Your function should return a $2-$element tuple, consisting itself of two tuples: the first tuple should be the names $($via the College.Name field$)$ of the two colleges that are least similar according the Gower distance. The second tuple should name the most similar colleges.

My function returns: Why are the elements in the second tuple not distinct?

$((\text{'}$Augustana College IL$\text{'},$ 'Hope College'$),$

$(\text{'}$Abilene Christian University', 'Abilene Christian University'$))$

def answer$\_$mixed$\_$features$\_$a$($df$)$:

D $=$ gower$\_$distances$($df$)$ #a provided function that computes gower distances

np$.$fill$\_$diagonal$($D$,1000)$

# Find indices of least and most similar pairs

least$\_$similar$\_$idx $=$ np$.$unravel$\_$index$($np$.$argmin$($D$),$ D$.$shape$)$

most$\_$similar$\_$idx $=$ np$.$unravel$\_$index$($np$.$argmax$($D$),$ D$.$shape$)$

# Get names of least and most similar colleges

least$\_$similar$\_$colleges $=($

df$.$iloc$[$least$\_$similar$\_$idx$[0]]["$College$.$Name"$],$

df$.$iloc$[$least$\_$similar$\_$idx$[1]]["$College$.$Name"$],$

$)$

most$\_$similar$\_$colleges $=($

df$.$iloc$[$most$\_$similar$\_$idx$[0]]["$College$.$Name"$],$

df$.$iloc$[$most$\_$similar$\_$idx$[1]]["$College$.$Name"$],$

$)$

return $($least$\_$similar$\_$colleges, most$\_$similar$\_$colleges$)$