python - NetworkX/Pandas - 无法将节点的度数输出到 .txt 文件中(错误消息)

Question

我需要将网络节点的度(以及其他中心性指标)输出到 .txt 文件中。我在以前的 NetworkX/Pandas 版本中能够做到这一点，但现在我收到错误。

我正在使用 NetworkX 版本。 2.1 和 Pandas 版本。 0.23.4:

import pandas as pd
import networkx as nx

G = nx.Graph()
G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(4,6)])

df = pd.DataFrame(dict(
    DEGREE = nx.degree(G),
    DEGREE_CENTRALITY = nx.degree_centrality(G),
    EIGENVECTOR = nx.eigenvector_centrality(G),
    KATZ = nx.katz_centrality_numpy(G),
    CLOSENESS_CENTRALITY = nx.closeness_centrality(G),
    BETWEENNESS_CENTRALITY = nx.betweenness_centrality(G),
    CLUSTCOEF = nx.clustering(G),
)) 
#df.index += 1 
#df.to_csv('centrality-metrics.csv')

错误信息是:

Traceback (most recent call last):
  File "<stdin>", line 8, in <module>
  File "/home/arthur/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 348, in __init__
    mgr = self._init_dict(data, index, columns, dtype=dtype)
  File "/home/arthur/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 459, in _init_dict
    return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
  File "/home/arthur/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 7356, in _arrays_to_mgr
    index = extract_index(arrays)
  File "/home/arthur/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py", line 7405, in extract_index
    raise ValueError('Mixing dicts with non-Series may lead to '
ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.

更新:如果我注释(#) DEGREE 行，则不会发生错误。输出 .txt 与此类似(我的原始数据来自基因网络):

GENE    BETWEENNESS CLOSENESS   CLUSTCOEF   DEGREE  DEGREE_CENTRALITY   EIGENVECTOR KATZ
A1BG    0.000142303010695361    0.318553702985653   0.0350877192982456  19  0.00115635080031647 7.26316435442522e-05    0.00556190357079266
A1CF    5.71271005407417e-05    0.33571705861921    0.375324675324675   56  0.00340819183251172 0.000225887126821305    -0.00251062164644857
A2M 0.000886299232394493    0.370534908894101   0.109265734265734   146 0.00888564299190554 0.00294316042120819 0.0163918804690203
A4GALT  7.38971769935498e-07    0.286369102602088   0   4   0.000243442273750837    1.35050378606586e-05    -0.000361261465931375
A4GNT   1.03038198147882e-05    0.297404430929626   0   15  0.000912908526565638    2.45985150882602e-05    0.00562955611859571

Answer 1

好吧，在阅读了上面的评论(@Joel)并执行了little research之后，我们可以使用dict(G. Degree)而不是nx. Degree:

import networkx as nx

G = nx.Graph()
G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(4,6)])

df = pd.DataFrame(dict(
    DEGREE = dict(G.degree),
    DEGREE_CENTRALITY = nx.degree_centrality(G),
    EIGENVECTOR = nx.eigenvector_centrality(G),
    KATZ = nx.katz_centrality_numpy(G),
    CLOSENESS_CENTRALITY = nx.closeness_centrality(G),
    BETWEENNESS_CENTRALITY = nx.betweenness_centrality(G),
    CLUSTCOEF = nx.clustering(G),
)) 

输出:

   DEGREE  DEGREE_CENTRALITY  EIGENVECTOR      KATZ  CLOSENESS_CENTRALITY  BETWEENNESS_CENTRALITY  CLUSTCOEF
1       2                0.4     0.456984  0.408764              0.500000                     0.0   1.000000
2       2                0.4     0.456984  0.408764              0.500000                     0.0   1.000000
3       3                0.6     0.584217  0.448883              0.714286                     0.6   0.333333
4       3                0.6     0.417120  0.441314              0.714286                     0.7   0.000000
5       1                0.2     0.183076  0.367131              0.454545                     0.0   0.000000
6       1                0.2     0.183076  0.367131              0.454545                     0.0   0.000000