I have data of countries trade with one another. I have split the main file according to months and got 12 csv files for the year 2019. A sample of the data of January csv is provided below:
reporter partner year month trade
0 Albania Argentina 2019 01 515256
1 Albania Australia 2019 01 398336
2 Albania Austria 2019 01 7664503
3 Albania Bahrain 2019 01 400
4 Albania Bangladesh 2019 01 653907
5 Zimbabwe Zambia 2019 01 79569855
I want to make complex network for every month and print the number of nodes of every network. Now I can do it the hard (stupid) way like so.
df01 = pd.read_csv('012019.csv')
df02 = pd.read_csv('022019.csv')
df03 = pd.read_csv('032019.csv')
df1= df01[['reporter','partner', 'trade']]
df2= df02[['reporter','partner', 'trade']]
df3= df03[['reporter','partner', 'trade']]
G1 = nx.Graph()
G1 = nx.from_pandas_edgelist(df1, 'reporter', 'partner', edge_attr='trade')
G1.number_of_nodes()
and so on for the next networks.
My question is how can I use a "for loop" to read the files, convert them to networks from dataframe and report the number of nodes of each node.
I tried this but nothing is reported.
for f in glob.glob('.csv'):
df = pd.read_csv(f)
df1 = df[['reporter','partner', 'trade']]
G = nx.from_pandas_edgelist(df1, 'reporter', 'partner', edge_attr='trade')
G.number_of_nodes()
Thanks.
Edit:
Ok. So I managed to do the above using similar codes like below:
for files in glob.glob('/home/user/VMShared/network/2nd/*.csv'):
df = pd.read_csv(files)
df1=df[['reporter','partner', 'import']]
G = nx.Graph()
G = nx.from_pandas_edgelist(df1, 'reporter', 'partner', edge_attr='import')
nx.write_graphml_lxml(G, "/home/user/VMShared/network/2nd/*.graphml")
The problem that I now face is how to write separate files. All I get from this is one file titled *.graphml. How can I get graphml files for every input file? Also if I can get the same graphml output name as the input file would be a plus.
