csv to sparse matrix in python

I have a big csv file which lists connections between nodes in a graph. example:

0001,95784

0001,98743

0002,00082

0002,00091

So this means that开发者_开发知识库 node id 0001 is connected to node 95784 and 98743 and so on. I need to read this into a sparse matrix in numpy. How can i do this? I am new to python so tutorials on this would also help.

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Example using lil_matrix (list of list matrix) of scipy.

Row-based linked list matrix.

This contains a list (self.rows) of rows, each of which is a sorted list of column indices of non-zero elements. It also contains a list (self.data) of lists of these elements.

$ cat 1938894-simplified.csv
0,32
1,21
1,23
1,32
2,23
2,53
2,82
3,82
4,46
5,75
7,86
8,28

Code:

#!/usr/bin/env python

import csv
from scipy import sparse

rows, columns = 10, 100
matrix = sparse.lil_matrix( (rows, columns) )

csvreader = csv.reader(open('1938894-simplified.csv'))
for line in csvreader:
    row, column = map(int, line)
    matrix.data[row].append(column)

print matrix.data

Output:

[[32] [21, 23, 32] [23, 53, 82] [82] [46] [75] [] [86] [28] []]

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If you want an adjacency matrix, you can do something like:

from scipy.sparse import *
from scipy import *
from numpy import *
import csv
S = dok_matrix((10000,10000), dtype=bool)
f = open("your_file_name")
reader = csv.reader(f)
for line in reader:
    S[int(line[0]),int(line[1])] = True

---

You might also be interested in Networkx, a pure python network/graphing package.

From the website:

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

>>> import networkx as nx
>>> G=nx.Graph()
>>> G.add_edge(1,2)
>>> G.add_node("spam")
>>> print G.nodes()
[1, 2, 'spam']
>>> print G.edges()
[(1, 2)]