Networkx Multigraph from_pandas_dataframe [英] Networkx Multigraph from_pandas_dataframe

问题描述

更新:
书面问题与Networkx版本< 2.0. from_pandas_dataframe方法已被删除. >
要在Networkx> = 2.0中完成相同的任务，请参见已接受答案的更新.

Update:
The question, as written, is relevant to Networkx version < 2.0. The from_pandas_dataframe method has been dropped.
To accomplish the same task in Networkx >= 2.0, see the update to the accepted answer.

尝试创建 MultiGraph() 使用networkx的 from_pandas_dataframe .在下面的示例中我在做什么错了?

Trying to create a MultiGraph() instance from a pandas DataFrame using networkx's from_pandas_dataframe. What am I doing wrong in the example below?

In [1]: import pandas as pd
        import networkx as nx

        df = pd.DataFrame([['geneA', 'geneB', 0.05, 'method1'],
                           ['geneA', 'geneC', 0.45, 'method1'],
                           ['geneA', 'geneD', 0.35, 'method1'],
                           ['geneA', 'geneB', 0.45, 'method2']], 
                           columns = ['gene1','gene2','conf','type'])

首先尝试使用默认的nx.Graph():

First try with the default nx.Graph():

In [2]: G= nx.from_pandas_dataframe(df, 'gene1', 'gene2', edge_attr=['conf','type'], 
                                    create_using=nx.Graph())

作为非MultiGraph()，我缺少重复的边缘之一:

As a non-MultiGraph(), I'm missing one of the duplicate edges:

In [3]: G.edges(data=True)
Out[3]: [('geneA', 'geneB', {'conf': 0.45, 'type': 'method2'}),
         ('geneA', 'geneC', {'conf': 0.45, 'type': 'method1'}),
         ('geneA', 'geneD', {'conf': 0.35, 'type': 'method1'})]

使用MultiGraph():

In [4]: MG= nx.from_pandas_dataframe(df, 'gene1', 'gene2', edge_attr=['conf','type'], 
                             create_using=nx.MultiGraph())

此:

TypeError                                 Traceback (most recent call last)
<ipython-input-49-d2c7b8312ea7> in <module>()
----> 1 MG= nx.from_pandas_dataframe(df, 'gene1', 'gene2', ['conf','type'], create_using=nx.MultiGraph())

/usr/lib/python2.7/site-packages/networkx-1.10-py2.7.egg/networkx/convert_matrix.pyc in from_pandas_dataframe(df, source, target, edge_attr, create_using)
    209         # Iteration on values returns the rows as Numpy arrays
    210         for row in df.values:
--> 211             g.add_edge(row[src_i], row[tar_i], {i:row[j] for i, j in edge_i})
    212 
    213     # If no column names are given, then just return the edges.

/usr/lib/python2.7/site-packages/networkx-1.10-py2.7.egg/networkx/classes/multigraph.pyc in add_edge(self, u, v, key, attr_dict, **attr)
    340             datadict.update(attr_dict)
    341             keydict = self.edge_key_dict_factory()
--> 342             keydict[key] = datadict
    343             self.adj[u][v] = keydict
    344             self.adj[v][u] = keydict

TypeError: unhashable type: 'dict'

问题 如何从熊猫数据帧实例化MultiGraph()?

Question How do I instantiate a MultiGraph() from a pandas dataframe?

推荐答案

Networkx< 2.0:
这是一个错误，一旦完成建议的编辑，我就在GitHub上打开了一个问题:

Networkx < 2.0:
It's was a bug, I opened an issue on GitHub, once I made the suggested edit:

将convert_matrix.py的第211行更改为:

It changed line 211 of convert_matrix.py to to read:

g.add_edge(row[src_i], row[tar_i], attr_dict={i:row[j] for i, j in edge_i})

此更改的结果:(此后已合并)

MG= nx.from_pandas_dataframe(df, 'gene1', 'gene2', edge_attr=['conf','type'], 
                                 create_using=nx.MultiGraph())

MG.edges(data=True)
[('geneA', 'geneB', {'conf': 0.05, 'type': 'method1'}),
         ('geneA', 'geneB', {'conf': 0.45, 'type': 'method2'}),
         ('geneA', 'geneC', {'conf': 0.45, 'type': 'method1'}),
         ('geneA', 'geneD', {'conf': 0.35, 'type': 'method1'})]

Networkx> = 2.0:
在具有这种格式(边缘列表)的DataFrames中，使用 from_pandas_edgelist

Networkx >= 2.0:
In DataFrames with this format (edge list), use from_pandas_edgelist

MG= nx.from_pandas_edgelist(df, 'gene1', 'gene2', edge_attr=['conf','type'], 
                             create_using=nx.MultiGraph())

MG.edges(data=True)
MultiEdgeDataView([('geneA', 'geneB', {'conf': 0.05, 'type': 'method1'}),
                   ('geneA', 'geneB', {'conf': 0.45, 'type': 'method2'}),
                   ('geneA', 'geneC', {'conf': 0.45, 'type': 'method1'}), 
                   ('geneA', 'geneD', {'conf': 0.35, 'type': 'method1'})])

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