python - Find indices of duplicate rows in pandas DataFrame

Question

What is the pandas way of finding the indices of identical rows within a given DataFrame without iterating over individual rows?

While it is possible to find all unique rows with unique = df[df.duplicated()] and then iterating over the unique entries with unique.iterrows() and extracting the indices of equal entries with help of pd.where(), what is the pandas way of doing it?

Example: Given a DataFrame of the following structure:

  | param_a | param_b | param_c
1 | 0       | 0       | 0
2 | 0       | 2       | 1
3 | 2       | 1       | 1
4 | 0       | 2       | 1
5 | 2       | 1       | 1
6 | 0       | 0       | 0

Output:

[(1, 6), (2, 4), (3, 5)]

Answer 1

Use parameter duplicated with keep=False for all dupe rows and then groupby by all columns and convert index values to tuples, last convert output Series to list:

df = df[df.duplicated(keep=False)]

df = df.groupby(list(df)).apply(lambda x: tuple(x.index)).tolist()
print (df)
[(1, 6), (2, 4), (3, 5)]

If you want also see duplicate values:

df1 = (df.groupby(df.columns.tolist())
       .apply(lambda x: tuple(x.index))
       .reset_index(name='idx'))
print (df1)
   param_a  param_b  param_c     idx
0        0        0        0  (1, 6)
1        0        2        1  (2, 4)
2        2        1        1  (3, 5)