问题
I have a dataframe consisting of possible network connections in the format df = pd.DataFrame(["A", "B", "Count", "some_attribute"])
. This dataframe represents connections like this:
- A has a connection with B
- This connection occurred "Count" times
- This connection has a specific attribute (i.e. a specific type of contact)
I want to export this Dataframe to the graphml format. It works fine using the following code:
import networkx as nx
G = nx.Graph()
G.add_weighted_edges_from(df[["A", "B", "Count"]].values)
nx.write_graphml(G, "my_graph.graphml")
This code results in a graphml file with the correct graph, which I can use with Gephi. Now I want to add an attribute:
G = nx.Graph()
G.add_weighted_edges_from(df[["A", "B", "Count"]].values, attr=df["some_attribute"].values)
nx.write_graphml(G, "my_graph.graphml")
Whenever I try to add attributes in this code, it becomes impossible to write it to a graphml file. With this code, I get the following error message:
NetworkXError: GraphML writer does not support <class 'numpy.ndarray'> as data values.
I found related articles (like this one), but it didn't provide any solution for this problem. Does anyone have a solution for adding attributes to a graphml file using networkx so I can use them in Gephi?
回答1:
Assuming the random DataFrame:
import pandas as pd
df = pd.DataFrame({'A': [0,1,2,0,0],
'B': [1,2,3,2,3],
'Count': [1,2,5,1,1],
'some_attribute': ['red','blue','red','blue','red']})
A B Count some_attribute
0 0 1 1 red
1 1 2 2 blue
2 2 3 5 red
3 0 2 1 blue
4 0 3 1 red
Following the code from above to instantiate a Graph
:
import networkx as nx
G = nx.Graph()
G.add_weighted_edges_from(df[["A","B", "Count"]].values, attr=df["some_attribute"].values)
when inspecting an edge, it appears that the numpy
array, df['some_attribute'].values
, gets assigned as an attribute to each edge:
print (G.edge[0][1])
print (G.edge[2][3])
{'attr': array(['red', 'blue', 'red', 'blue', 'red'], dtype=object), 'weight': 1}
{'attr': array(['red', 'blue', 'red', 'blue', 'red'], dtype=object), 'weight': 5}
If I understand your intent correctly, I'm assuming you want each edge's attribute to correspond to the df['some_attribute']
column.
You may find it easier to create your Graph
using nx.from_pandas_dataframe(), especially since you already have data formatted in a DataFrame
object.
G = nx.from_pandas_dataframe(df, 'A', 'B', ['Count', 'some_attribute'])
print (G.edge[0][1])
print (G.edge[2][3])
{'Count': 1, 'some_attribute': 'red'}
{'Count': 5, 'some_attribute': 'red'}
writing to file was no problem:
nx.write_graphml(G,"my_graph.graphml")
except, I'm not a regular Gephi user so there may be another way to solve the following. When I loaded the file with 'Count'
as the edge attribute, the Gephi graph didn't recognize edge weights by default. So I changed the column name from 'Count'
to 'weight'
and saw the following when I loaded into Gephi:
df.columns=['A', 'B', 'weight', 'some_attribute']
G = nx.from_pandas_dataframe(df, 'A', 'B', ['weight', 'some_attribute'])
nx.write_graphml(G,"my_graph.graphml")
Hope this helps and that I understood your question correctly.
Edit
Per Corley's comment above, you can use the following if you choose to use add_edges_from
.
G.add_edges_from([(u,v,{'weight': w, 'attr': a}) for u,v,w,a in df[['A', 'B', 'Count', 'some_attribute']].values ])
There is no significant performance gain, however I find from_pandas_dataframe
more readable.
import numpy as np
df = pd.DataFrame({'A': np.arange(0,1000000),
'B': np.arange(1,1000001),
'Count': np.random.choice(range(10), 1000000, replace=True),
'some_attribute': np.random.choice(['red','blue'], 1000000, replace=True,)})
%%timeit
G = nx.Graph()
G.add_edges_from([(u,v,{'weight': w, 'attr': a}) for u,v,w,a in df[['A', 'B', 'Count', 'some_attribute']].values ])
1 loop, best of 3: 4.23 s per loop
%%timeit
G = nx.Graph()
G = nx.from_pandas_dataframe(df, 'A', 'B', ['Count', 'some_attribute'])
1 loop, best of 3: 3.93 s per loop
来源:https://stackoverflow.com/questions/40363046/add-attributes-in-networkx-write-graphml-before-opening-in-gephi