DataCamp

Counting USA vs. USSR Cold War Olympic Medals

For this exercise, you want to see which country, the USA or the USSR, won the most medals consistently over the Cold War period.

There are several steps involved in carrying out this computation.

You'll need a pivot table with years ('Edition') on the index and countries ('NOC') on the columns. The entries will be the total number of medals each country won that year. If the country won no medals in a given edition, expect a NaN in that entry of the pivot table.
You'll need to slice the Cold War period and subset the 'USA' and 'URS' columns.
You'll need to make a Series from this slice of the pivot table that tells which country won the most medals in that edition using .idxmax(axis='columns'). If .max() returns the maximum value of Series or 1D array, .idxmax() returns the index of the maximizing element. The argument axis=columns or axis=1 is required because, by default, this aggregation would be done along columns for a DataFrame.
The final Series contains either 'USA' or 'URS' according to which country won the most medals in each Olympic edition. You can use .value_counts() to count the number of occurrences of each.

Construct medals_won_by_country using medals.pivot_table().
- The index should be the years ('Edition') & the columns should be country ('NOC')
- The values should be 'Athlete' (which captures every medal regardless of kind) & the aggregation method should be 'count' (which captures the total number of medals won).
Create cold_war_usa_urs_medals by slicing the pivot table medals_won_by_country. Your slice should contain the editions from years 1952:1988 and only the columns 'USA' & 'URS' from the pivot table.
Create the Series most_medals by applying the .idxmax() method to cold_war_usa_urs_medals. Be sure to use axis='columns'.
Print the result of applying .value_counts() to most_medals. The result reported gives the number of times each of the USA or the USSR won more Olympic medals in total than the other between 1952 and 1988.