矩阵熊猫的邻接表

Question

I'm trying to get through a toy example of building an adjacency matrix from a list, but already I can't quite figure it out. I am thinking in terms of .loc() but I'm not sure how to index correctly.

{'nodes':['A', 'B', 'C', 'D', 'E'],
 'edges':[('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'), 
                      ('D', 'E'), ('E', 'A'),('E', 'B'), ('E', 'C')]}

I've started to build the matrix with:

n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns = graph['nodes'], index = graph['edges'])

but now I'm not sure how to fill it in. I think there's an easy one liner, maybe with a list comprehension?

Expected output:

   A  B  C  D  E
A  0  1  0  1  0
B  0  0  1  0  1
C  0  0  0  1  0
D  0  0  0  0  1
E  1  1  1  0  0

Answer 1

A simple way to obtain the adjacency matrix is by using NetworkX

d = {'nodes':['A', 'B', 'C', 'D', 'E'],
     'edges':[('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'), 
                      ('D', 'E'), ('E', 'A'),('E', 'B'), ('E', 'C')]}

It appears that from your adjacency matrix the graph is directed. You can create a directed graph as shown bellow and define its nodes and edges from the dictionary with:

import networkx as nx
g = nx.DiGraph()
g.add_nodes_from(d['nodes'])
g.add_edges_from(d['edges'])

And then you can obtain the adjacency matrix as a dataframe with nx.to_pandas_adjacency :

nx.to_pandas_adjacency(g)

    A    B    C    D    E
A  0.0  1.0  0.0  1.0  0.0
B  0.0  0.0  1.0  0.0  1.0
C  0.0  0.0  0.0  1.0  0.0
D  0.0  0.0  0.0  0.0  1.0
E  1.0  1.0  1.0  0.0  0.0
​

Answer 2

for undirected graph

graph = {'nodes': ['A', 'B', 'C', 'D', 'E'],
         'edges': [('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'),
                   ('D', 'E'), ('E', 'A'), ('E', 'B'), ('E', 'C')]}
n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns=graph['nodes'], index=graph['nodes'])
for i in graph['edges']:
    adj_matr.at[i[0], i[1]] = 1
    adj_matr.at[i[1], i[0]] = 1


print(adj_matr)

   A  B  C  D  E
A  0  1  0  1  1
B  1  0  1  0  1
C  0  1  0  1  1
D  1  0  1  0  1
E  1  1  1  1  0

for directed graph:

graph = {'nodes': ['A', 'B', 'C', 'D', 'E'],
         'edges': [('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'),
                   ('D', 'E'), ('E', 'A'), ('E', 'B'), ('E', 'C')]}
n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns=graph['nodes'], index=graph['nodes'])
print(adj_matr)
for i in graph['edges']:
    adj_matr.at[i[0], i[1]] = 1
    # adj_matr.at[i[1], i[0]] = 1

print(adj_matr)

   A  B  C  D  E
A  0  1  0  1  0
B  0  0  1  0  1
C  0  0  0  1  0
D  0  0  0  0  1
E  1  1  1  0  0

Answer 3

For a directed graph you can use:

df = pd.DataFrame(graph['edges'], columns=['From', 'To'])
df['Edge'] = 1
adj = df.pivot(index='From', columns='To', values='Edge').fillna(0)