Python: Create DataFrame from highest frequency of occurrence per item - python-3.x

Question

I have a dataframe as given below
data = {
'Code': ['P', 'J', 'M', 'Y', 'P', 'Z', 'P', 'P', 'J', 'P', 'J', 'M', 'P', 'Z', 'Y', 'M', 'Z', 'J', 'J'],
'Value': [10, 10, 20, 30, 10, 40, 50, 10, 10, 20, 10, 50, 60, 40, 30, 20, 40, 20, 10]
}
example = pd.DataFrame(data)
Using Python 3, I want to create another dataframe from the dataframe example such that the Code associated with the greater number of Value is obtained.
The new dataframe should look like solution below
output = {'Code': ['J', 'M', 'Y', 'Z', 'P', 'M'],'Value': [10, 20, 30, 40, 50, 50]}
solution = pd.DataFrame(output)
As can be seen, J has more association to Value 10 than other Code so J is selected, and so on.

Answer 1

You could define a function that returns the most occurring items and apply it to the grouped elements. Finally explode to list to rows.
>>> def most_occurring(grp):
... res = Counter(grp)
... highest = max(res.values())
... return [k for k, v in res.items() if v == highest]
...
>>> example.groupby('Value')['Code'].apply(lambda x: most_occurring(x)).explode().reset_index()
Value Code
0 10 J
1 20 M
2 30 Y
3 40 Z
4 50 P
5 50 M
6 60 P

Answer 2

If I understood correctly, you need something like this:
grouped = example.groupby(['Code', 'Value']).indices
arr_tmp = []
[arr_tmp.append([i[0], i[1], len(grouped[i])]) for i in grouped]#['Int64Index'])
output = pd.DataFrame(data=arr_tmp, columns=['Code', 'Value', 'index_count'])
output = output.sort_values(by=['index_count'], ascending=False)
output.reset_index(inplace=True)
output