Using iterrows$()$ to iterate over every observation of a Pandas DataFrame is easy to understand, but not very efficient. On every iteration, you're creating a new Pandas Series.

If you want to add a column to a DataFrame by calling a function on another column, the iterrows$()$ method in combination with a for loop is not the preferred way to go$.$ Instead, you'll want to use apply$().$

Compare the iterrows$()$ version with the apply$()$ version to get the same result in the brics DataFrame:

for lab, row in brics.iterrows$()$ :

brics.loc$[$lab$,$ "name$\_$length"$]=$ len$($row$["$country$"])$

brics$["$name$\_$length"$]=$ brics$["$country$"].$apply$($len$)$

We can do a similar thing to call the upper$()$ method on every name in the country column. However, upper$()$ is a method, so we'll need a slightly different approach:

Instructions

$100$ XP

Replace the for loop with a one$-$liner that uses $.$apply$($str$.$upper$).$ The call should give the same result: a column COUNTRY should be added to cars, containing an uppercase version of the country names.

As usual, print out cars to see the fruits of your hard labor