转换tsv文件，所以我可以使用它的节点和边缘在python [英] Convert tsv file so i can use it for nodes and edges in python

问题描述

我有这个tsv文件我想读取和以某种方式计数一个路径中的节点数量

I have this tsv file i would like to read and somehow count the numbers of nodes in a path

这是tsv文件的部分如何看起来像： / p>

This is how the parts of tsv file looks like:

  6a3701d319fc3754  1297740409  166  14th_century;15th_century;16th_century;Pacific_Ocean;Atlantic_Ocean;Accra;Africa;Atlantic_slave_trade;African_slave_trade  NULL
  3824310e536af032  1344753412  88  14th_century;Europe;Africa;Atlantic_slave_trade;African_slave_trade 3

路径只有这样：第14世纪分隔符';'

the paths is only the ones looking like this : 14th_century;15th_century; seperated by ';'

我的代码所以fare：

my code so fare :

import networkx as nx

fh = open("test.tsv", 'rb')
G = nx.read_edgelist("test.tsv", create_using=nx.DiGraph())
print G.nodes()
print G.edges()

问题是我如何计算路径所触及的节点数量？

So my question is how do i count the numbers of nodes touched by a path?

推荐答案

我在这里使用pandas库的速度，您可以使用 pip install pandas 进行安装，并在此处查看： http ：//pandas.pydata.org/

I'm using the pandas library here for speed, you can install using pip install pandas and also check here: http://pandas.pydata.org/

首先从您的示例代码构建我们的数据框架：

Firstly construct our dataframe from your sample code:

In [39]:

temp = """6a3701d319fc3754  1297740409  166  14th_century;15th_century;16th_century;Pacific_Ocean;Atlantic_Ocean;Accra;Africa;Atlantic_slave_trade;African_slave_trade  NULL

  3824310e536af032  1344753412  88  14th_century;Europe;Africa;Atlantic_slave_trade;African_slave_trade 3"""

# construct the dataframe
# in your case replace io.String() with the path to your tsv file
df = pd.read_csv(io.StringIO(temp), sep='\s+', header=None, names=['a','b','c','d','e'])

df
Out[39]:

                  a           b    c  \
0  6a3701d319fc3754  1297740409  166   
1  3824310e536af032  1344753412   88   

                                                   d   e  
0  14th_century;15th_century;16th_century;Pacific... NaN  
1  14th_century;Europe;Africa;Atlantic_slave_trad...   3  

[2 rows x 5 columns]

In [65]:

# use itertools to flatten our list of lists
import itertools

def to_edge_list(x):
    # split on semi-colon
    split_list = x.split(';')
    #print(split_list)
    # get our main node
    primary_node = split_list[0]
    # construct our edge list
    edge_list=[]
    # create a list comprehension from the split list
    edge_list = [(primary_node, x) for x in split_list[1:] ]
    #print(edge_list)
    return edge_list

# now use itertools to flatten the list of lists into a single list
combined_edge_list = list(itertools.chain.from_iterable(df['d'].apply(to_edge_list)))

print(combined_edge_list)

[('14th_century', '15th_century'), ('14th_century', '16th_century'), ('14th_century', 'Pacific_Ocean'), ('14th_century', 'Atlantic_Ocean'), ('14th_century', 'Accra'), ('14th_century', 'Africa'), ('14th_century', 'Atlantic_slave_trade'), ('14th_century', 'African_slave_trade'), ('14th_century', 'Europe'), ('14th_century', 'Africa'), ('14th_century', 'Atlantic_slave_trade'), ('14th_century', 'African_slave_trade')]

# Now construct our networkx graph from the edge list
In [66]:

import networkx as nx

G = nx.MultiDiGraph()
G.add_edges_from(combined_edge_list)
G.edges()


Out[66]:

[('14th_century', '15th_century'),
 ('14th_century', 'Africa'),
 ('14th_century', 'Africa'),
 ('14th_century', 'Atlantic_slave_trade'),
 ('14th_century', 'Atlantic_slave_trade'),
 ('14th_century', 'African_slave_trade'),
 ('14th_century', 'African_slave_trade'),
 ('14th_century', '16th_century'),
 ('14th_century', 'Accra'),
 ('14th_century', 'Europe'),
 ('14th_century', 'Atlantic_Ocean'),
 ('14th_century', 'Pacific_Ocean')]

绘制图形（看起来不太漂亮，但又是什么）：

draw the graph (doesn't look pretty but what the hell):

这篇关于转换tsv文件，所以我可以使用它的节点和边缘在python的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！