python - 构建 NetworkX 图时避免使用 NaN 属性

Question

我想使用 pandas 读取包含节点及其属性的 csv 文件。并非所有节点都具有每个属性，缺少的属性只是 csv 文件中缺少的。当 pandas 读取 csv 文件时，缺失值显示为 nan。我想从数据框中批量添加节点，但要避免添加 nan 的属性。

例如，这里有一个名为 mwe.csv 的示例 csv 文件:

Name,Cost,Depth,Class,Mean,SD,CST,SL,Time
Manuf_0001,39.00,1,Manuf,,,12,,10.00
Manuf_0002,36.00,1,Manuf,,,8,,10.00
Part_0001,12.00,2,Part,,,,,28.00
Part_0002,5.00,2,Part,,,,,15.00
Part_0003,9.00,2,Part,,,,,10.00
Retail_0001,0.00,0,Retail,253,36.62,0,0.95,0.00
Retail_0002,0.00,0,Retail,45,1,0,0.95,0.00
Retail_0003,0.00,0,Retail,75,2,0,0.95,0.00

这是我目前的处理方式:

import pandas as pd
import numpy as np
import networkx as nx

node_df = pd.read_csv('mwe.csv')

graph = nx.DiGraph()
graph.add_nodes_from(node_df['Name'])
nx.set_node_attributes(graph, dict(zip(node_df['Name'], node_df['Cost'])), 'nodeCost')
nx.set_node_attributes(graph, dict(zip(node_df['Name'], node_df['Mean'])), 'avgDemand')
nx.set_node_attributes(graph, dict(zip(node_df['Name'], node_df['SD'])), 'sdDemand')
nx.set_node_attributes(graph, dict(zip(node_df['Name'], node_df['CST'])), 'servTime')
nx.set_node_attributes(graph, dict(zip(node_df['Name'], node_df['SL'])), 'servLevel')

# Loop through all nodes and all attributes and remove NaNs.
for i in graph.nodes:
    for k, v in list(graph.nodes[i].items()):
        if np.isnan(v):
            del graph.nodes[i][k]

它可以工作，但是很笨重。有没有更好的方法，例如在添加节点时避免 nan，而不是之后删除 nan？

Answer 1

在这种情况下，您可以利用 Pandas 的力量来执行您的命令。因此，我创建了这个函数，它将具有两个键和值列的 DataFrame 转换为一个系列，然后删除具有 NaN 的元素，最后将其更改为字典

def create_node_attribs(key_col, val_col):
    # Upto you if you want to pass the dataframe as argument
    # In your case, since this was the only df, I only passed the columns
    global node_df
    return Series(node_df[val_col].values,
                  index=node_df[key_col]).dropna().to_dict()

完整代码如下

import pandas as pd
import networkx as nx
from pandas import Series

node_df = pd.read_csv('mwe.csv')

graph = nx.DiGraph()

def create_node_attribs(key_col, val_col):
    # Upto you if you want to pass the dataframe as argument
    # In your case, since this was the only df, I only passed the columns
    global node_df
    return Series(node_df[val_col].values,
                  index=node_df[key_col]).dropna().to_dict()

graph.add_nodes_from(node_df['Name'])
nx.set_node_attributes(graph, create_node_attribs('Name', 'Cost'), 'nodeCost')
nx.set_node_attributes(graph, create_node_attribs('Name', 'Mean'), 'avgDemand')
nx.set_node_attributes(graph, create_node_attribs('Name', 'SD'), 'sdDemand')
nx.set_node_attributes(graph, create_node_attribs('Name', 'CST'), 'servTime')
nx.set_node_attributes(graph, create_node_attribs('Name', 'SL'), 'servLevel')

链接到 Google Colab Notebook用代码。

此外，see this answer , 有关当前使用的方法的时间比较的更多信息。