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create hierarchy using two columns in pandas

Data I am working with is below:

Name RefSecondary     RefMain
test  2               3   
bet   3               4   
get   1               2   
set   null            1   
net   3               5

I have done a very simple query which looks up the presence of values in dataframe and build hierarchy

sys_role = 'sample.xlsx'
df = pd.read_excel(sys_role,na_filter = False).apply(lambda x: x.astype(str).str.strip())
for i in range(count):
    for j in range(count):
        if df.iloc[i]['RefMain'] == df.iloc[j]['RefSecondary']:
            df.iloc[j, df.columns.get_loc('Name')] = "/".join([df.iloc[i]['Name'],df.iloc[j]['Name']])
    j = j+1
i = i+1

The results I am getting is below:

   Result          RefMain
0  get/test           3
1  test/bet           4
2  set/get            2
3  set                1
4  test/net           5

This is really slow and the logic doesn't work perfectly as well. Is there a way I can get this done faster?

Logic needs to be as below:

 1)Take a value from column RefMain,and find its correspoding RefSecondary value.  
 2)Look up the RefSecondary value  in RefMain, 
 3)If found Back to Step 1 and repeat.
 4)This continues recursively till no value/null is found in RefSecondary column.

Resultant dataframe should look like below:

   Result            RefMain
0  set/get/test          3
1  set/get/test/bet      4
2  set/get               2
3  set                   1
4  set/get/test/net      5
like image 914
misguided Avatar asked Dec 31 '22 15:12

misguided


1 Answers

This sounds like a graph problem. You can try networkx as follows:

df = df.fillna(-1)

# create a graph
G = nx.DiGraph()

# add reference as edges
G.add_edges_from(zip(df['RefMain'],df['RefSecondary'] ))

# rename the nodes accordingly
G = nx.relabel_nodes(G, mapping=df.set_index('RefMain')['Name'].to_dict())


# merge the path list to the dataframe
df = df.merge(pd.DataFrame(nx.shortest_path(G)).T['null'], 
              left_on='Name', 
              right_index=True)

# new column:
df['Path'] = df['null'].apply(lambda x: '/'.join(x[-2::-1]) )

Output:

   Name RefSecondary RefMain                         null              Path
0  test            2       3       [test, get, set, null]      set/get/test
1   bet            3       4  [bet, test, get, set, null]  set/get/test/bet
2   get            1       2             [get, set, null]           set/get
3   set         null       1                  [set, null]               set
4   net            3       5  [net, test, get, set, null]  set/get/test/net
like image 186
Quang Hoang Avatar answered Jan 21 '23 05:01

Quang Hoang