How can I sort by a column while binning another

Question

I am trying to cut float values of each country while grouping them by continent. I had posted this previously but I got a little farther so I am going to put where I am at now. I wrote the following code:

groups =  pd.value_counts(pd.cut(renew['% Renewable'], 5))
groups = pd.DataFrame(groups).reset_index()

and I got the following DataFrame:

    % Renewable counts
0   (2.213, 15.754]     7
1   (15.754, 29.228]    4
2   (29.228, 42.702]    2
3   (56.176, 69.65]     2
4   (42.702, 56.176]    0

Now I would like to merge with the 'Continent' and create a Series with a multindex of 'Continent' and '% Renewable' and the counts as the Eeries value.

I would like to merge these so it is a count in a multindex Series grouped by continents with the counts of each country in each bin. An example would be like this:

Asia      (2.213, 15.754]       3
          (15.754, 29.228]      1
          (29.228, 42.702]      2
          (56.176, 69.65]       0
          (42.702, 56.176]      0
Europe    (2.213, 15.754]       2
          (15.754, 29.228]      2
          (29.228, 42.702]      0
          (56.176, 69.65]       1
          (42.702, 56.176]      0
>>....and so on

Here is a piece of the data frame I am getting the info from:

    Country Continent   % Renewable
0   China   Asia    (15.754, 29.228]
1   United States   North America   (2.213, 15.754]
2   Japan   Asia    (2.213, 15.754]
3   United Kingdom  Europe  (2.213, 15.754]
4   Russian Federation  Europe  (15.754, 29.228]

Answer 1

Hard to say why that particular error happens without seeing the data, but first put all the variables you want to group by in the first argument, e.g.

groups = renew.groupby(['Continent', pd.cut(renew['% Renewable'], 5)])