Pandas Dataframe get maximum with respect to other entries

Question

I have a Dataframe like this:

name	phase	value
BOB	1	.9
BOB	2	.05
BOB	3	.05
JOHN	2	.45
JOHN	3	.45
JOHN	4	.05
FRANK	1	.4
FRANK	3	.6

I want to find which entry in column 'phase' has the maximum value in column 'value'.
If more than one share the same maximum value keep the first or a random value for 'phase'.

---

Desired result table:

name	phase	value
BOB	1	.9
JOHN	2	.45
FRANK	3	.6

---

my approach was:

df.groupby(['name'])[['phase','value']].max() 

but it returned incorrect values.

Answer 1

You don't need to use groupby. Sort values by value and phase (adjust the order if necessary) and drop duplicates by name:

out = (df.sort_values(['value', 'phase'], ascending=[False, True])
         .drop_duplicates('name')
         .sort_index(ignore_index=True))
print(out)

# Output
    name  phase  value
0    BOB      1   0.90
1   JOHN      2   0.45
2  FRANK      3   0.60

Answer 2

Try to sort the dataframe first:

df = df.sort_values(
    by=["name", "value", "phase"], ascending=[True, False, True]
)

x = df.groupby("name", as_index=False).first()
print(x)

Prints:

    name  phase  value
0    BOB      1   0.90
1  FRANK      1   0.60
2   JOHN      1   0.45

Answer 3

A possible solution, that could avoid sorting is with groupby:

df.loc[df.groupby('name', sort = False).value.idxmax()]

    name  phase  value
0    BOB      1   0.90
3   JOHN      2   0.45
7  FRANK      3   0.60

Answer 4

You may check

out = df.sort_values('value',ascending=False).drop_duplicates('name').sort_index()
Out[434]: 
    name  phase  value
0    BOB      1   0.90
3   JOHN      2   0.45
7  FRANK      3   0.60