[英]Reading a networkx graph from a csv file with row and column header
我有一个CSV文件,代表图表的邻接矩阵。 但是,该文件的第一行是节点的标签,第一列也是节点的标签。 如何将此文件读入networkx
图形对象? 有没有一个整洁的pythonic方式来做到这一点没有黑客攻击?
我到目前为止的审判:
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')
但当然这并没有解决问题,因为我需要图形创建中节点的标签。
数据示例:
Attribute,A,B,C
A,0,1,1
B,1,0,0
C,1,0,0
Tab是分隔符,而不是逗号。
您可以将数据读入结构化数组。 可以从x.dtype.names
获取标签,然后可以使用nx.from_numpy_matrix
生成networkx图:
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
有一个create_using
参数,您可以使用该参数指定要创建的networkx Graph的类型。 例如,
G = nx.from_numpy_matrix(y, create_using=nx.DiGraph())
使G
成为DiGraph
。
这可行,不确定它是最好的方法:
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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