You can read graphs in CSV format into igraph like this:

import csv, igraph
reader = csv.reader(open("input.csv"))
graph = igraph.Graph.TupleList(reader)

Or, if the CSV file contains headers:

reader = csv.DictReader(open("input.csv"))
graph = igraph.Graph.DictList(vertices=None, edges=reader)

Of course there is the possibility of wrapping these in a Graph.Read_CSV method, but there are too many possibilities with the interpretation of the columns in the CSV file (e.g., are there headers? which columns contain the endpoints of the edges? is the graph directed? which remaining columns should be stored as edge attributes? how can one provide attributes for the vertices? which vertex attribute should be used to store the names of the vertices?) that I believe it is hard to make a CSV reader that caters for all these use-cases, and it is yet unclear to me what is the "core" functionality that should be covered by the function. (The truth is, I don't really use CSV files to store my data). If you happen to have a good idea of what should be handled by Graph.Read_CSV and which of the above questions are not relevant for more than 20% of the use-cases, I'm happy to discuss this further.

Pandas interface for reading files #29

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