Construct bipartite graph from columns of python dataframe

Question

I have a dataframe with three columns.

data[\'subdomain\'],  data[\'domain\'], data [\'IP\']

I want to build one bipartite graph for every

Answer 1

You could use

B.add_weighted_edges_from(
    [(row['domain'], row['subdomain'], 1) for idx, row in df.iterrows()], 
    weight='weight')

to add weighted edges, or you could use

B.add_edges_from(
    [(row['domain'], row['subdomain']) for idx, row in df.iterrows()])

to add edges without weights.

You may not need weights since the node degree is the number of edges adjacent to that node. For example,

>>> B.degree('example.org')
3

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import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt

df = pd.DataFrame(
    {'IP': ['10.20.30.40',
      '30.50.70.90',
      '10.20.30.41',
      '10.20.30.42',
      '90.80.70.10'],
     'domain': ['example.org',
      'site.com',
      'example.org',
      'example.org',
      'website.com'],
     'subdomain': ['test1', 'something', 'test2', 'test3', 'else']})

B = nx.Graph()
B.add_nodes_from(df['subdomain'], bipartite=0)
B.add_nodes_from(df['domain'], bipartite=1)
B.add_weighted_edges_from(
    [(row['domain'], row['subdomain'], 1) for idx, row in df.iterrows()], 
    weight='weight')

print(B.edges(data=True))
# [('test1', 'example.org', {'weight': 1}), ('test3', 'example.org', {'weight': 1}), ('test2', 'example.org', {'weight': 1}), ('website.com', 'else', {'weight': 1}), ('site.com', 'something', {'weight': 1})]

pos = {node:[0, i] for i,node in enumerate(df['domain'])}
pos.update({node:[1, i] for i,node in enumerate(df['subdomain'])})
nx.draw(B, pos, with_labels=False)
for p in pos:  # raise text positions
    pos[p][1] += 0.25
nx.draw_networkx_labels(B, pos)

plt.show()

yields

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