Multiple sets of duplicate records from a pandas dataframe

Performing a groupby on df.index can take you places.

v = df.index.to_series().groupby(df.flight_id).apply(pd.Series.tolist)
v[v.str.len().gt(1)]

flight_id
4    [2, 5]
9    [3, 6]
dtype: object

You can also get cute with just groupby on df.index directly.

v = pd.Series(df.index.groupby(df.flight_id))
v[v.str.len().gt(1)].to_dict()

{
    "4": [
        2,
        5
    ],
    "9": [
        3,
        6
    ]
}

Is this what you need ? duplicated+groupby

(df.loc[df['flight_id'].duplicated(keep=False)].reset_index()).groupby('flight_id')['index'].apply(tuple)
Out[510]: 
flight_id
4    (2, 5)
9    (3, 6)
Name: index, dtype: object

Adding tolist at the end

(df.loc[df['flight_id'].duplicated(keep=False)].reset_index()).groupby('flight_id')['index'].apply(tuple).tolist()
Out[511]: [(2, 5), (3, 6)]

And another solution ... for fun only

s=df['flight_id'].value_counts()
list(map(lambda x : tuple(df[df['flight_id']==x].index.tolist()), s[s.gt(1)].index))
Out[519]: [(2, 5), (3, 6)]

Using apply and a lambda

df.groupby('flight_id').apply(
    lambda d: tuple(d.index) if len(d.index) > 1 else None
).dropna()

flight_id
4    (2, 5)
9    (3, 6)
dtype: object

---

Or better with an iteration through the groupby object

{k: tuple(d.index) for k, d in df.groupby('flight_id') if len(d) > 1}

{4: (2, 5), 9: (3, 6)}

---

Just the tuples

[tuple(d.index) for k, d in df.groupby('flight_id') if len(d) > 1]

[(2, 5), (3, 6)]

---

Leaving this for posterity
But I now highly dislike this approach. It's just too gross.
I was messing around with itertools.groupby
Others may find this fun

from itertools import groupby

key = df.flight_id.get
s = sorted(df.index, key=key)
dict(filter(
    lambda t: len(t[1]) > 1,
    ((k, tuple(g)) for k, g in groupby(s, key))
))

{4: (2, 5), 9: (3, 6)}