Reading network graph from csv file with row and column header
I have a CSV file that represents the adjacency matrix of a graph. However, the file has node labels as the first row and node labels as the first column. How can I read this file into a graphics object networkx
? Is there a neat pythonic way to do this without hacking?
My trial version:
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 does not solve the problem as I need the labels for the nodes in the plot creation.
Sample data:
Attribute,A,B,C A,0,1,1 B,1,0,0 C,1,0,0
A Tab is a delimiter, not a comma.
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You can read data in 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})]
nx.from_numpy_matrix
has a parameter create_using
you can use to specify the type of networkx diagram you want to create. For example,
G = nx.from_numpy_matrix(y, create_using=nx.DiGraph())
does G
a DiGraph
.
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This will work, but not sure if this is the best way:
In [23]:
import pandas as pd
import io
import networkx as nx
temp = """Attribute,A,B,C
A,0,1,1
B,1,0,0
C,1,0,0"""
# for your case just load the csv like you would do, use sep='\t'
df = pd.read_csv(io.StringIO(temp))
df
Out[23]:
Attribute A B C
0 A 0 1 1
1 B 1 0 0
2 C 1 0 0
In [39]:
G = nx.DiGraph()
for col in df:
for x in list(df.loc[df[col] == 1,'Attribute']):
G.add_edge(col,x)
G.edges()
Out[39]:
[('C', 'A'), ('B', 'A'), ('A', 'C'), ('A', 'B')]
In [40]:
nx.draw(G)
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