Construct NetworkX graph from Pandas DataFrame

Question

I\'d like to create some NetworkX graphs from a simple Pandas DataFrame:

        Loc 1   Loc 2   Loc 3   Loc 4   Loc 5   Loc 6   Loc 7
Foo     0       0              


        

Answer 1

NetworkX expects a square matrix (of nodes and edges), perhaps* you want to pass it:

In [11]: df2 = pd.concat([df, df.T]).fillna(0)

Note: It's important that the index and columns are in the same order!

In [12]: df2 = df2.reindex(df2.columns)

In [13]: df2
Out[13]: 
       Bar  Bat  Baz  Foo  Loc 1  Loc 2  Loc 3  Loc 4  Loc 5  Loc 6  Loc 7  Quux
Bar      0    0    0    0      0      0      1      1      0      1      1     0
Bat      0    0    0    0      0      0      1      0      0      1      0     0
Baz      0    0    0    0      0      0      1      0      0      0      0     0
Foo      0    0    0    0      0      0      1      1      0      0      0     0
Loc 1    0    0    0    0      0      0      0      0      0      0      0     1
Loc 2    0    0    0    0      0      0      0      0      0      0      0     0
Loc 3    1    1    1    1      0      0      0      0      0      0      0     0
Loc 4    1    0    0    1      0      0      0      0      0      0      0     0
Loc 5    0    0    0    0      0      0      0      0      0      0      0     0
Loc 6    1    1    0    0      0      0      0      0      0      0      0     0
Loc 7    1    0    0    0      0      0      0      0      0      0      0     0
Quux     0    0    0    0      1      0      0      0      0      0      0     0

In[14]: graph = nx.from_numpy_matrix(df2.values)

This doesn't pass the column/index names to the graph, if you wanted to do that you could use relabel_nodes (you may have to be wary of duplicates, which are allowed in pandas' DataFrames):

In [15]: graph = nx.relabel_nodes(graph, dict(enumerate(df2.columns))) # is there nicer  way than dict . enumerate ?

*It's unclear exactly what the columns and index represent for the desired graph.