I need to reindex the 2nd level of a pandas dataframe, so that the 2nd level becomes a (complete) list 0,...,(N-1) for each 1st level index.

• I tried using Allan/Hayden's approach, but unfortunately it only creates an index with as many rows as previously existing.
• What I want is that for each new index, new rows are inserted (with nan values).

Example:

df = pd.DataFrame({'first': ['one', 'one', 'one', 'two', 'two', 'three'], 'second': [0, 1, 2, 0, 1, 1],'value': [1, 2, 3, 4, 5, 6]
})
print dffirst  second  value
0    one       0      1
1    one       1      2
2    one       2      3
3    two       0      4
4    two       1      5
5  three       1      6# Tried using Allan/Hayden's approach, but no good for this, doesn't add the missing rows    
df['second'] = df.reset_index().groupby(['first']).cumcount()
print dffirst  second  value
0    one       0      1
1    one       1      2
2    one       2      3
3    two       0      4
4    two       1      5
5  three       0      6

My desired result is:

   first  second  value
0    one       0      1
1    one       1      2
2    one       2      3
3    two       0      4
4    two       1      5
4    two       2      nan <-- INSERTED
5  three       0      6
5  three       1      nan <-- INSERTED
5  three       2      nan <-- INSERTED

I think you can first set columns first and second as multi-level index, and then reindex.

# your data
# ==========================
df = pd.DataFrame({'first': ['one', 'one', 'one', 'two', 'two', 'three'], 'second': [0, 1, 2, 0, 1, 1],'value': [1, 2, 3, 4, 5, 6]
})dffirst  second  value
0    one       0      1
1    one       1      2
2    one       2      3
3    two       0      4
4    two       1      5
5  three       1      6# processing
# ============================
multi_index = pd.MultiIndex.from_product([df['first'].unique(), np.arange(3)], names=['first', 'second'])df.set_index(['first', 'second']).reindex(multi_index).reset_index()first  second  value
0    one       0      1
1    one       1      2
2    one       2      3
3    two       0      4
4    two       1      5
5    two       2    NaN
6  three       0    NaN
7  three       1      6
8  three       2    NaN