
All right, why don't we walk through the solution.

Here, we're going to start with our olympic_medal_counts_df DataFrame,

as we have in the previous exercises.

First, we're going to create a new, smaller dataframe called metal counts,

which is the olympic_medal_count_df DataFrame, but only the gold, silver, and

bronze columns.

Then we're going to use the numpy.dot function to matrix multiply this

metal_counts_matrix with the 4, 2, 1 array which represents the number of

points that each country would score for a gold, silver, or bronze medal.

So what we're going to get here is an array,

where the value is 4 times the number of gold medals plus 2 times the number of

silver medals plus 1 times the number of bronze medals.

Down here, I'm going to define this Python dictionary called olympic_points,

where again I provide us the keys,

the column names, and as the values pandas series, where I provide as

the argument first the list of country names and here, this array points,

which again has the total number of points that each country earned.

Finally, I'm just going to say olympic_points_df is DataFrame of olympic_points.

This is just the tip of the iceberg when it comes to pandas and

numpy's functionality.

If you're interested to read more about what these libraries can do,

I encourage you to check out the full documentation which are found in

the urls in the instructor comments below