Reading a networkx graph from a csv file with row and column header

Question

I have a CSV file that represents the adjacency matrix of a graph. However the file has as the first row the labels of the nodes and as the first column also the labels of the nodes. How can I read this file into a networkx graph object? Is there a neat pythonic way to do it without hacking around?

My trial so far:

x = np.loadtxt('file.mtx', delimiter='\t', dtype=np.str)
row_headers = x[0,:]
col_headers = x[:,0]
A = x[1:, 1:]
A = np.array(A, dtype='int')

But of course this doesn't solve the problem since I need the labels for the nodes in the graph creation.

Example of the data:

Attribute,A,B,C
A,0,1,1
B,1,0,0
C,1,0,0

A Tab is the delimiter, not a comma tho.

Answer 1

You could read the data into a structured array. The labels can be obtained from x.dtype.names, and then the networkx graph can be generated using nx.from_numpy_matrix:

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

# read the first line to determine the number of columns
with open('file.mtx', 'rb') as f:
    ncols = len(next(f).split('\t'))

x = np.genfromtxt('file.mtx', delimiter='\t', dtype=None, names=True,
                  usecols=range(1,ncols) # skip the first column
                  )
labels = x.dtype.names

# y is a view of x, so it will not require much additional memory
y = x.view(dtype=('int', len(x.dtype)))

G = nx.from_numpy_matrix(y)
G = nx.relabel_nodes(G, dict(zip(range(ncols-1), labels)))

print(G.edges(data=True))
# [('A', 'C', {'weight': 1}), ('A', 'B', {'weight': 1})]

The nx.from_numpy_matrix has a create_using parameter you can use to specify the type of networkx Graph you wish to create. For example,

G = nx.from_numpy_matrix(y, create_using=nx.DiGraph())

makes G a DiGraph.