Python 用networkx模块解读人物关系 Python源码

import pandas as pd import networkx as nx import matplotlib.pyplot as plt # 读取文件 aa =r'../data/fl4.xls' df = pd.DataFrame(pd.read_excel(aa)) df1=df['label1'].drop_duplicates().values.tolist() df2=df[['label1','label2','weight']] #设置画布大小 plt.figure(figsize=(6, 5)) #颜色数据 colors = df['color'].drop_duplicates().values.tolist() G = nx.Graph() # 添加边 for i in df2.index: G.add_edge(df2.label1[i], df2.label2[i], weight=df2.weight[i]) # 定义两个边，并给边赋予权重，其中u是起点，v是终点，d是权重 edge1 = [(u, v) for (u, v, d) in G.edges(data=True) if (d['weight'] >=1)] edge2 = [(u, v) for (u, v, d) in G.edges(data=True) if (d['weight'] >=15)] # 图的布局 # 节点在一个圆环上均匀分布 pos = nx.circular_layout(G) #用Fruchterman-Reingold算法排列节点 #pos=nx.spring_layout(G) #节点随机分布 #pos=nx.spring_layout(G) # 点 nx.draw_networkx_nodes(G, pos, alpha=1, node_size=200,node_color=colors,node_shape='o') #nx.draw_networkx_nodes(G, pos, alpha=1, node_size=300,node_color=colors,node_shape='p') # 边 nx.draw_networkx_edges(G, pos, edgelist=edge1,width=1, alpha=0.3, edge_color='g', style='dashed') nx.draw_networkx_edges(G, pos, edgelist=edge2, width=1.5, alpha=0.5, edge_color='red') # 标签 nx.draw_networkx_labels(G, pos, font_size=9) # 生成结果 plt.axis('off') plt.title('《复仇者联盟4》人物关系图') plt.rcParams['font.size'] = 10 plt.rcParams['font.sans-serif']=['SimHei'] #解决中文乱码 plt.show()