BASICS OF NETWORKX

I place the data in a postgreSQL database. Use these code to create and insert data

create table edges ( fromnode int, tonode int, distance numeric );

insert into edges(fromnode, tonode, distance) values (1, 2, 1306), (1, 5, 2161), (1, 6, 2661), (2, 3, 919), (2, 4, 629), (3, 4, 435), (3, 5, 1225), (3, 7, 1983), (5, 6, 1483), (5, 7, 1258);



In [3]:

    
import psycopg2
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

conn = psycopg2.connect(database="postgres", user="postgres", password="***", host="127.0.0.1", port="5432")



In [5]:

    
query = """SELECT fromnode, tonode, distance from edges"""
df = pd.read_sql_query(query, conn)
df.shape









    Out[5]:





(10, 3)



In [11]:

    
# from dataframe to graph
# import as undirected graph
g=nx.from_pandas_dataframe(df, 'fromnode', 'tonode', 'distance')



In [12]:

    
# from graph to dataframe as a matrix
nx.to_pandas_dataframe(g, weight='distance')



In [13]:

    
# print nodes and edges
print 'list nodes: ', g.nodes(), '\n'
print 'no. nodes:', len(g) #no. nodes
print 'no. edges:', g.number_of_edges(), '\n'
print 'list edges: ', g.edges(), '\n'
print 'list all edge attributes: ', dict(((a,b,),c['distance']) for a,b,c in g.edges(data=True))









    



list nodes:  [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0] 

no. nodes: 7
no. edges: 10 

list edges:  [(1.0, 2.0), (1.0, 5.0), (1.0, 6.0), (2.0, 3.0), (2.0, 4.0), (3.0, 4.0), (3.0, 5.0), (3.0, 7.0), (5.0, 6.0), (5.0, 7.0)] 

list all edge attributes:  {(1.0, 2.0): 1306.0, (5.0, 6.0): 1483.0, (5.0, 7.0): 1258.0, (1.0, 5.0): 2161.0, (1.0, 6.0): 2661.0, (3.0, 7.0): 1983.0, (2.0, 3.0): 919.0, (3.0, 4.0): 435.0, (2.0, 4.0): 629.0, (3.0, 5.0): 1225.0}

PLOT GRAPH

NOTE TYPE OF LAYOUTS

'circular_layout', 'random_layout', 'shell_layout', 'spring_layout', 'spectral_layout', 'fruchterman_reingold_layout'



In [14]:

    
# choose layoutl pos=position for nodes
pos = nx.fruchterman_reingold_layout(g)

# draw network
nx.draw(g, pos, with_labels = True, node_size=800, node_color='pink', edge_color='grey')

# label edges
edge_labels = dict([((u,v,),d['distance']) for u,v,d in g.edges(data=True)])
nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)









    Out[14]:





{(1.0, 2.0): <matplotlib.text.Text at 0x11ae0e490>,
 (1.0, 5.0): <matplotlib.text.Text at 0x11ae32250>,
 (1.0, 6.0): <matplotlib.text.Text at 0x11ae32810>,
 (2.0, 3.0): <matplotlib.text.Text at 0x11ae3e3d0>,
 (2.0, 4.0): <matplotlib.text.Text at 0x11ae3ef50>,
 (3.0, 4.0): <matplotlib.text.Text at 0x11ae3e990>,
 (3.0, 5.0): <matplotlib.text.Text at 0x11ae4a550>,
 (3.0, 7.0): <matplotlib.text.Text at 0x11ae32dd0>,
 (5.0, 6.0): <matplotlib.text.Text at 0x11ae26690>,
 (5.0, 7.0): <matplotlib.text.Text at 0x11ae26c50>}

PLOT GRAPH WITH SHORTEST PATH



In [37]:

    
s=2
t=7

print nx.shortest_path(g, source=s,target=t, weight='distance')

plt.figure(figsize=(5, 5))
# choose layout
pos = nx.fruchterman_reingold_layout(g)

# draw network
nx.draw(g, pos, with_labels = True, node_size=800, node_color='pink', edge_color='grey')
# nx.draw_networkx_edges(g, pos, edge_color='grey',width=0.1, alpha=0.5)

# label edges
edge_labels = dict([((u,v,),d['distance']) for u,v,d in g.edges(data=True)])
nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)


# plot shortest path
path = nx.shortest_path(g, source=s,target=t, weight='distance')
path_edges = zip(path,path[1:])
# nx.draw_networkx_nodes(g,pos,nodelist=path,node_color='black', node_size=1000)
nx.draw_networkx_edges(g,pos,edgelist=path_edges,edge_color='r',width=15)









    



[2, 3.0, 7.0]






    Out[37]:





<matplotlib.collections.LineCollection at 0x12064f310>

SHORTEST PATHS



In [31]:

    
# shortest path
nx.shortest_path(graph, source=1, target=3, weight='distance')









    Out[31]:





[1, 2.0, 3.0]



In [32]:

    
# shortest path
paths = nx.all_shortest_paths(graph, source=1, target=7, weight='distance')
for path in paths:
    print path









    



[1, 5.0, 7]



In [33]:

    
# all simple paths without weight
path = nx.all_simple_paths(graph, source=1, target=7)
for i in path:
    print i









    



[1, 2.0, 3.0, 5.0, 7]
[1, 2.0, 3.0, 7]
[1, 2.0, 4.0, 3.0, 5.0, 7]
[1, 2.0, 4.0, 3.0, 7]
[1, 5.0, 3.0, 7]
[1, 5.0, 7]
[1, 6.0, 5.0, 3.0, 7]
[1, 6.0, 5.0, 7]

	1.0	2.0	3.0	4.0	5.0	6.0	7.0
1.0	0.0	1306.0	0.0	0.0	2161.0	2661.0	0.0
2.0	1306.0	0.0	919.0	629.0	0.0	0.0	0.0
3.0	0.0	919.0	0.0	435.0	1225.0	0.0	1983.0
4.0	0.0	629.0	435.0	0.0	0.0	0.0	0.0
5.0	2161.0	0.0	1225.0	0.0	0.0	1483.0	1258.0
6.0	2661.0	0.0	0.0	0.0	1483.0	0.0	0.0
7.0	0.0	0.0	1983.0	0.0	1258.0	0.0	0.0