Pandas pivot table selecting rows with maximum values - pandas

Question

I have pandas dataframe as:
df
Id Name CaseId Value
82 A1 case1.01 37.71
1558 A3 case1.01 27.71
82 A1 case1.06 29.54
1558 A3 case1.06 29.54
82 A1 case1.11 12.09
1558 A3 case1.11 32.09
82 A1 case1.16 33.35
1558 A3 case1.16 33.35
For each Id, Name pair I need to select the CaseId with maximum value.
i.e. I am seeking the following output:
Id Name CaseId Value
82 A1 case1.01 37.71
1558 A3 case1.16 33.35
I tried the following:
import pandas as pd
pd.pivot_table(df, index=['Id', 'Name'], columns=['CaseId'], values=['Value'], aggfunc=[np.max])['amax']
But all it does is for each CaseId as column it gives maximum value and not the results that I am seeking above.

Answer 1

sort_values + drop_duplicates
df.sort_values('Value').drop_duplicates(['Id'],keep='last')
Out[93]:
Id Name CaseId Value
7 1558 A3 case1.16 33.35
0 82 A1 case1.01 37.71
Since we post same time , adding more method
df.sort_values('Value').groupby('Id').tail(1)
Out[98]:
Id Name CaseId Value
7 1558 A3 case1.16 33.35
0 82 A1 case1.01 37.71

Answer 2

This should work:
df = df.sort_values('Value', ascending=False).drop_duplicates('Id').sort_index()
Output:
Id Name CaseId Value
0 82 A1 case1.01 37.71
7 1558 A3 case1.16 33.35

Answer 3

With nlargest and groupby
pd.concat(d.nlargest(1, ['Value']) for _, d in df.groupby('Name'))
Id Name CaseId Value
0 82 A1 case1.01 37.71
7 1558 A3 case1.16 33.35

Answer 4

Another idea is to create a joint column, take its max, then split it back to two columns:
df['ValueCase'] = list(zip(df['Value'], df['CaseId']))
p = pd.pivot_table(df, index=['Id', 'Name'], values=['ValueCase'], aggfunc='max')
p['Value'], p['CaseId'] = list(zip(*p['ValueCase']))
del p['ValueCase']
Results in:
CaseId Value
Id Name
82 A1 case1.01 37.71
1558 A3 case1.16 33.35