在多列上将具有相同列值的行分组

Question

I need to find a way to group rows that have the same value in a column, but with rows being grouped on multiple columns. What I need to achieve is grouping the rows that represent a single object having different IDs on different services.

I have a pandas dataframe that looks like

SERV1 SERV2 SERV3 SERV4 SERV5 SERV6
8766  NaN   NaN   0989  NaN   NaN   
8766  NaN   5434  NaN   NaN   NaN   
NaN   NaN   5434  3212  NaN   NaN   
NaN   1236  NaN   NaN   NaN   6543
NaN   3456  NaN   7862  NaN   NaN   
NaN   NaN   NaN   7862  NaN   4767

And the desired dataframe should look like

SERV1   SERV2   SERV3   SERV4         SERV5   SERV6
[8766]  NaN     [5434]  [0989,3212]   NaN     NaN
NaN     [1236]  NaN     NaN           NaN     [6543]   
NaN     [3456]  NaN    [7862]         NaN     [4767]

Columns represent the different services, values represent an ID that is univoque only for that specific column (same value on different columns, may happen by accident, but should not be considered as representing the same ID).

I managed to create a dictionary for each column with the corresponding values, but that is not the same as having a df as the one I'd like.

By using

df = grouped.aggregate(lambda x: tuple(x))

I could achieve a similar thing but that works only for grouping a single column and not to link it to the others, it puts together all the NaN which don't actually belong together.

I'm looking for ideas/solutions. Thanks.

Answer 1

Not having found a completely pandas solution, I have resolved to do it by using networkx module and extracting subgraph with connected_component_subgraphs function, and then unpacking the results into a dataframe. Not that elegant, but it works.