add_edges_from three tuples networkx - python-2.7

Question

I am trying to use networkx to create a DiGraph. I want to use add_edges_from(), and I want the edges and their data to be generated from three tuples.
I am importing the data from a CSV file. I have three columns: one for ids (first set of nodes), one for a set of names (second set of nodes), and another for capacities (no headers in the file). So, I created a dictionary for the ids and capacities.
dictionary = dict(zip(id, capacity))
then I zipped the tuples containing the edges data:
List = zip(id, name, capacity)
but when I execute the next line, it gives me an assertion error.
G.add_edges_from(List, 'weight': 1)
Can someone help me with this problem? I have been trying for a week with no luck.
P.S. I'm a newbie in programming.
EDIT:
so, i found the following solution. I am honestly not sure how it works, but it did the job!
Here is the code:
import networkx as nx
import csv
G = nx.DiGraph()
capacity_dict = dict(zip(zip(id, name),capacity))
List = zip(id, name, capacity)
G.add_edges_from(capacity_dict, weight=1)
for u,v,d in List:
G[u][v]['capacity']=d
Now when I run:
G.edges(data=True)
The result will be:
[(2.0, 'First', {'capacity': 1.0, 'weight': 1}), (3.0, 'Second', {'capacity': 2.0, 'weight': 1})]
I am using the network simplex. Now, I am trying to find a way to make the output of the flowDict more understandable, because it is only showing the ids of the flow. (Maybe i'll try to input them in a database and return the whole row of data instead of using the ids only).

Answer 1

A few improvements on your version. (1) NetworkX algorithms assume that weight is 1 unless you specifically set it differently. Hence there is no need to set it explicitly in your case. (2) Using the generator allows the capacity attribute to be set explicitly and other attributes to also be set once per record. (3) The use of a generator to process each record as it comes through saves you having to iterate through the whole list twice. The performance improvement is probably negligible on small datasets but still it feels more elegant. Having said that -- your method clearly works!
import networkx as nx
import csv
# simulate a csv file.
# This makes a multi-line string behave as a file.
from StringIO import StringIO
filehandle = StringIO('''a,b,30
b,c,40
d,a,20
''')
# process each row in the file
# and generate an edge from each
def edge_generator(fh):
reader = csv.reader(fh)
for row in reader:
row[-1] = float(row[-1]) # convert capacity to float
# add other attributes to the dict() below as needed...
# e.g. you might add weights here as well.
yield (row[0],
row[1],
dict(capacity=row[2]))
# create the graph
G = nx.DiGraph()
G.add_edges_from(edge_generator(filehandle))
print G.edges(data=True)
Returns this:
[('a', 'b', {'capacity': 30.0}),
('b', 'c', {'capacity': 40.0}),
('d', 'a', {'capacity': 20.0})]