My data looks as follows:
ID my_val db_val
a X X
a X X
a Y X
b X Y
b Y Y
b Y Y
c Z X
c X X
c Z X
Expected result :
ID my_val db match
a X:2;Y:1 X full_match
b Y:2;X:1 Y full_match
c z:2;X:1 X partial_match
a full_match is when db_val matches the most abundant my_val a partial_match is when db_val is in the other values but doesn't match the top one.
My current approach consists of grouping by ID then counting values into a seperate column then concatenating the value and its count, then aggregating all values into one row for each ID.
This is how I aggregate the columns:
def all_hits_aggregate_df(df, columns=['my_val']):
grouped = data.groupby('ID')
l=[]
for c in columns:
res = grouped[c].value_counts(ascending=False, normalize=False).to_frame('count_'+c).reset_index(level=1)
res[c] = res[c].astype(str) +':'+ res['count_'+c].astype(str)
l.append(res.groupby('ID').agg(lambda x: ';'.join(x)))
return reduce(lambda x, y: pd.merge(x, y, on = 'ID'), l)
And for the comparison phase, I loop through each row and parse the my_val column into lists then do the comparison.
I am sure that the way I do the comparison step is extremely inefficient but I am unsure how I would do it before aggregation to avoid having to parse the generated string later in the process.
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