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，缺少的属性只是从 csv 文件中丢失。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，但避免添加nan 的属性。

例如，这里是一个名为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]

它有效，但它很笨重。有没有更好的方法，例如在添加节点时避免nans，而不是之后删除nans？

Answer 1

将 csv 导入 Pandas 时使用 keep_default_na：

pd.read_csv('data.csv', keep_default_na=False)

Get pandas.read_csv to read empty values as empty string instead of nan

Answer 2

在这种情况下，您可以利用 Pandas 的力量进行竞标。所以，我创建了这个函数，它将你的带有两个键和值列的 DataFrame 转换为一个系列，然后使用 NaN 删除元素，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()

这是完整的代码

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')

使用代码链接到Google Colab Notebook。

另外，see this answer，了解有关当前所用方法的时间比较的更多信息。