python pandas recursive lookup between manager and employee ID's - python-3.x

Question

I have a dataframe like below
import pandas as pd
import numpy as np
raw_data = {'Emp_ID':[144,220,155,200],
'Mgr_ID': [200, 144,200,500],
'Type': ['O','I','I','I'],
'Location' : ['India','UK','UK','US']
}
df2 = pd.DataFrame(raw_data, columns = ['Emp_ID','Mgr_ID', 'Type','Location'])
print(df2)
i want to get manager id and ultimate employee ID which he directly/indirectly reporting...suppose manager id 200 directly reporting 144 and 155 and indirectly reporting employee 220. so i want to have separate 3 records for manager 200 like below output..like this for other all manager id's
Wanted output like below

Answer 1

Finding parents/childs or relations between IDs relate to graph theory, so you are better using Networkx package. You need to install it through pip and import it. Create a graph g using networkx from_pandas_edgelist. For each manager, there are multiple employees directly under him/her. However, each employee is supposed having only one direct manager. So, we start from Emp_ID. Call nx.ancestors for each employee using genex (or listcomp if you prefer) and passing it to create dataframe df3. Finally, explode the series of lists of Mgr_ID and join back to df2 to get final output.
import pandas as pd
import networkx as nx
g = nx.from_pandas_edgelist(df2, source='Mgr_ID', target='Emp_ID', create_using=nx.DiGraph)
df3 = pd.DataFrame(([list(nx.ancestors(g, x)), x] for x in df2.Emp_ID),
index=df2.index, columns=['Mgr_ID', 'Emp_ID'])
df_final = df3.explode('Mgr_ID').join(df2[['Type', 'Location']])
Out[23]:
Mgr_ID Emp_ID Type Location
0 200 144 O India
0 500 144 O India
1 144 220 I UK
1 500 220 I UK
1 200 220 I UK
2 200 155 I UK
2 500 155 I UK
3 500 200 I US