Let's finally deal with that pesky "Export commodities" column! It's not a number, but represents a list of items. It is common to deal with

data that is not numeric $($arbitrary strings, strings representing single items, strings representing list$/$sets of items, etc$).$ To deal with these

non$-$numeric columns, we need to encode them. That is$,$ we need to convert them from a non$-$numeric form to a numeric one.

In computer science, there are many encodings and encoding methods. An encoding that you are probably already familiar with is ASCII,

which is a way to represent Latin letters as numbers. Encodings are just agreed upon ways to represent non$-$numeric values as numbers.

In machine learning and data science, probably the most popular encoding method is the One$-$Hot encoding. In One$-$Hot encoding, we take

all the possible values an item can take and assign each possible value an index. Presence of that value results in a $1,$ which absence results in

a $0.$ Essentially, we are turning a list of items into a series of binary columns which ask $"$Do you have this value?".

For example, consider the following tables. In the first one, a student's major is kept as a string $($or a list of strings$).$ To encode it$,$ a column is

created for each possible major and a $1$ is present if that student has that major $($otherwise a $0$ is used$).$

Students:

Students with One$-$Hot:

Task $3.$A

Complete the function below that takes in a frame and a column name. The function should modify the frame to add multiple columns

represented a one$-$hot encoding of the specified column. Assume that the given column contains a comma$-$separated list of values. Each

value should be made lowercase and have additional whitespace removed from the beginning and end. The new column names should be

named: $"$old column name$>$: