Imagine a process, where we do several pandas groupbys.

We start with a df like so:

import numpy as np
import pandas as pd
np.random.seed(1)

df = pd.DataFrame({
    'id': np.arange(10),
    'a': np.random.randint(1, 10, 10),
    'b': np.random.randint(1, 10, 10),
    'c': np.random.randint(1, 10, 10)
})
df
Out[23]: 
   id  a  b  c
0   0  2  8  2
1   1  8  8  9
2   2  7  2  9
3   3  3  8  4
4   4  5  1  9
5   5  6  7  8
6   6  3  8  4
7   7  5  7  7
8   8  3  2  6
9   9  5  1  2

We perform a groupby ‘a’ (The ‘max’ agg is just an example)

new_df = df.groupby('a').agg('max').reset_index()
Out[25]: 
   a  id  b  c
0  2   0  8  2
1  3   8  8  6
2  5   9  7  9
3  6   5  7  8
4  7   2  2  9
5  8   1  8  9

And I want to keep track of the original id to which group it belongs.
For example,

id 0 belongs to a = 2, 
1 to 8, 
2 to 7, 
(3, 6, 8) belongs to 3 
etc..

Afterword we perform another groupby:

new_df.groupby('b').agg('max').reset_index()
Out[28]: 
   b  a  id  c
0  2  7   2  9
1  7  6   9  9
2  8  8   8  9

Now we have a continued mapping,
Where

group a (2, 3, 8) belongs to group 8 (of b)
(5, 6) = 7
7 = 2

And this result in a long map where the original id:

0 => a = 2 => b = 8 (where b = 8 is the final group that interests me)
1 => a = 8 => b = 8
2 => a = 7 => b = 2

And so on..

Now I do this in order to reduce a lot of entities in my data so that I can group them in the same bucket somehow. And I need to map them from their original id to a new id, that is being represented after many iterations of groupby.

In the end I want to see something like so

Out[32]: 
   id  grp
0   0    8
1   1    8
2   2    2
3   3    8
4   4    7
5   5    7
6   6    8
7   7    7
8   8    8
9   9    7

again, because id=0 went to a=2 and a=2 went to b=8..

Any solution or suggestion will be most welcome. Even carrying the values in a column dedicated to this, with each group by.
And when we aggregate, we can do a set addition…