数据集简介

Python 网络分析中级

Eric Ma

Data Carpentry instructor and author of nxviz package

数据集与案例介绍

大学论坛发帖数据集，6个月
节点分区：学生、论坛
本章活动：
- 从 pandas DataFrame 构建图
- 计算二分图的单分投影
- 可视化
- 时间序列过滤与分析
回顾已用函数

从 DataFrame 构建图

df

   customers    products
0  customerA    product1
1  customerB    product2
...

G = nx.Graph()

G.add_nodes_from(df['products'], bipartite='products')
G.add_nodes_from(df['customers'], bipartite='customers')

list(G.nodes())

['product1', 'customerC', 'product2', 'customerB', 'customerA']

list(G.edges())

[]

从 DataFrame 构建图

G.add_edges_from(zip(df['customers'], df['products']))

list(G.edges())

[('product1', 'customerC'), ('product1', 'customerA'), 
    ('customerC', 'product2'), ('product2', 'customerB')]

二分图投影

cust_nodes = [n for n in G.nodes() if G.nodes[n]
                 ['bipartite'] == 'customers']

prod_nodes = [n for n in G.nodes() if G.nodes[n]
                  ['bipartite'] == 'products']

prodG = nx.bipartite.projected_graph(G, nodes=prod_nodes)
custG = nx.bipartite.projected_graph(G, nodes=cust_nodes)

list(prodG.nodes())

['product1', 'product2']

list(custG.nodes())

['customerC', 'customerB', 'customerA']

开始练习！

Python 网络分析中级