使用Pandas数据框将边缘权重分配给networkx图 [英] Assign edge weights to a networkx graph using pandas dataframe

问题描述

我正在python 3中构建networkx图.我正在使用pandas数据框为图提供边缘和节点.这是我所做的:

I am contructing a networkx graph in python 3. I am using a pandas dataframe to supply the edges and nodes to the graph. Here is what I have done :

test = pd.read_csv("/home/Desktop/test_call1", delimiter = ';')

g_test = nx.from_pandas_edgelist(test, 'number', 'contactNumber', edge_attr='callDuration')

我想要的是，pandas数据帧的"callDuration"列充当networkx图的边缘权重，并且边缘的厚度也相应地变化.

What I want is that the "callDuration" column of the pandas dataframe act as the weight of the edges for the networkx graph and the thickness of the edges also change accordingly.

我也想获得'n'个最大加权边.

I also want to get the 'n' maximum weighted edges.

推荐答案

让我们尝试:

import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

df = pd.DataFrame({'number':['123','234','345'],'contactnumber':['234','345','123'],'callduration':[1,2,4]})

df

G = nx.from_pandas_edgelist(df,'number','contactnumber', edge_attr='callduration')
durations = [i['callduration'] for i in dict(G.edges).values()]
labels = [i for i in dict(G.nodes).keys()]
labels = {i:i for i in dict(G.nodes).keys()}

fig, ax = plt.subplots(figsize=(12,5))
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos, ax = ax, labels=True)
nx.draw_networkx_edges(G, pos, width=durations, ax=ax)
_ = nx.draw_networkx_labels(G, pos, labels, ax=ax)

输出:

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