drop same values in different columns by pair (drop connected components)

Question

after applying levenshtein distance algorithm I get a dataframe like this:

Elemento_lista	Item_ID	Score	idx	ITEM_ID_Coincidencia
4	691776	100	5	691777
4	691776	100	6	691789
4	691776	100	7	691791
5	691777	100	4	691776
5	691777	100	6	691789
5	691777	100	7	691791
6	691789	100	4	691776
6	691789	100	5	691777
6	691789	100	7	691791
7	691791	100	4	691776
7	691791	100	5	691777
7	691791	100	6	691789
9	1407402	100	10	1407424
10	1407424	100	9	1407402

Elemento_lista column is the index of the element that is compared to others, Item_ID is the id of the element, Score is Score generated by the algorithm, idx is the index of the element that was found as similar (same as Elemento_lista , but for elements that were found as similar), ITEM_ID_Coincidencia is the id of the element found as similar

It´s a small sample of the real DF (More than 300000 rows), I´ll need to drop lines that are the same , for example...if Elemento_lista 4, is equal to idx 5,6,and 7...they are all the same, so I don't need lines where 5 is equal to 4, 6 and 7/ 6 is equal to 4,5,7 and 7 is equal to 4,5,6. The same for each Elemento_Lista : value=9 is equal to idx 10, so...I don't need the line Elemento_Lista 10 is equal to idx 9...How could I drop these lines in order to reduce DF len ???

Final DF should be:

Elemento_lista	Item_ID	Score	idx	ITEM_ID_Coincidencia
4	691776	100	5	691777
4	691776	100	6	691789
4	691776	100	7	691791
9	1407402	100	10	1407424

I don´t know how to do this...is it possible?

Thanks in advance

Answer 1

Preparing data like example:

a = [
[4,691776,100,5,691777],
[4,691776,100,6,691789],
[4,691776,100,7,691791],
[5,691777,100,4,691776],
[5,691777,100,6,691789],
[5,691777,100,7,691791],
[6,691789,100,4,691776],
[6,691789,100,5,691777],
[6,691789,100,7,691791],
[7,691791,100,4,691776],
[7,691791,100,5,691777],
[7,691791,100,6,691789],
[9,1407402,100,10,1407424],
[10,1407424,100,9,1407402]
]
c = ['Elemento_lista', 'Item_ID', 'Score', 'idx', 'ITEM_ID_Coincidencia']
df = pd.DataFrame(data = a, columns = c)
df

Now, you insert one column: it will contain an array of 2 sorted indexes.

tuples_of_indexes = [sorted([x[0], x[3]]) for x in df.values]
df.insert(5, 'tuple_of_indexes', (tuples_of_indexes))

Then all the dataframe is sorted by the inserted column:

df = df.sort_values(by=['tuple_of_indexes'])

Then you eliminate rows that repeat inserted column:

df = df[~df['tuple_of_indexes'].apply(tuple).duplicated()]

For last, you eliminate inserted column: 'tuple_of_indexes':

df.drop(['tuple_of_indexes'], axis=1)

The output is:

Elemento_lista  Item_ID Score   idx ITEM_ID_Coincidencia
0   4   691776  100 5   691777
1   4   691776  100 6   691789
2   4   691776  100 7   691791
4   5   691777  100 6   691789
5   5   691777  100 7   691791
8   6   691789  100 7   691791
12  9   1407402 100 10  1407424

Answer 2

This can be approached using graph theory.

You have the following relationships between your IDs:

So what you need to do is find the subgraphs.

For this we can use networkx's connected_components function:

# pip install networkx
import networkx as nx
G = nx.from_pandas_edgelist(df, source='Elemento_lista', target='idx')

# get "first" (arbitrary) node for each subgraph
# note that sets (unsorted) are used
# so there is no guarantee on any node being "first" item
nodes = [tuple(g)[0] for g in nx.connected_components(G) if g]
# [4, 9]

# filter DataFrame
df2 = df[df['Elemento_lista'].isin(nodes)]

output:

    Elemento_lista  Item_ID  Score  idx  ITEM_ID_Coincidencia
0                4   691776    100    5                691777
1                4   691776    100    6                691789
2                4   691776    100    7                691791
12               9  1407402    100   10               1407424

update: real data

You real data is hyperconnected, forming in fine only 2 groups.

You can change strategy here and use a directed graph and strongly_connected_components

import networkx as nx
#df = pd.read_csv('ADIDAS_CALZADO.csv', index_col=0)
G = nx.from_pandas_edgelist(df, source='Elemento_lista', target='idx', create_using=nx.DiGraph)

# len(list(nx.strongly_connected_components(G)))
# 150 subgraphs

nodes = [tuple(g)[0] for g in nx.strongly_connected_components(G) if g]

df2 = df[df['Elemento_lista'].isin(nodes)]

# len(df2)
# only 2,910 nodes left out of the 25,371 initial ones

new graph on the filtered df2: