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

csv, networkx, python

Solution

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`.

Problem

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.

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