Python 根据dataframe的列值使用networkx创建图形

我有以下数据帧：

import pandas as pd
df = pd.DataFrame({'id_emp': [1,2,3,4,1], 
               'name_emp': ['x','y','z','w','x'], 
               'donnated_value':[1100,11000,500,300,1000],
               'refound_value':[22000,22000,50000,450,90]
            })
df['return_percentagem'] = 100 * 
df['refound_value']/df['donnated_value']
df['classification_roi'] = ''

def comunidade(i):

    if i < 50:
        return 'Bad Investment'
    elif i >=50 and i < 100:
        return 'Median Investment'
    elif i >= 100:
        return 'Good Investment'

df['classification_roi'] = df['return_percentagem'].map(comunidade)
df

---

欢迎提供任何帮助。

在这里，我没有使用来自edgelist的

。相反，列出理解和for循环：

import matplotlib.pyplot as plt
import networkx as nx
import itertools

G = nx.Graph()

# use index to name nodes, rather than id_emp, otherwise
# multiple nodes would end up having the same name
G.add_nodes_from([a for a in df.index])

#create edges:
#same employee edges
for ie in set(df['id_emp']):
    indices = df[df['id_emp']==ie].index
    G.add_edges_from(itertools.product(indices,indices))

# same classification edges
for cr in set(df['classification_roi']):
    indices = df[df['classification_roi']==cr].index
    G.add_edges_from(itertools.product(indices,indices))

nx.draw(G)
plt.show()


可选：着色，以区分节点

plt.subplot(121)
plt.title('coloured by id_emp')
nx.draw(G, node_color=df['id_emp'], cmap='viridis')

plt.subplot(122)

color_mapping = { 
    'Bad Investment': 0,
    'Median Investment': 1,
    'Good Investment':2}

plt.title('coloured by classification_roi')
nx.draw(G, node_color=df['classification_roi'].replace(color_mapping), cmap='RdYlBu')

在这里，我没有使用来自edgelist的
。相反，列出理解和for循环：

import matplotlib.pyplot as plt
import networkx as nx
import itertools

G = nx.Graph()

# use index to name nodes, rather than id_emp, otherwise
# multiple nodes would end up having the same name
G.add_nodes_from([a for a in df.index])

#create edges:
#same employee edges
for ie in set(df['id_emp']):
    indices = df[df['id_emp']==ie].index
    G.add_edges_from(itertools.product(indices,indices))

# same classification edges
for cr in set(df['classification_roi']):
    indices = df[df['classification_roi']==cr].index
    G.add_edges_from(itertools.product(indices,indices))

nx.draw(G)
plt.show()


可选：着色，以区分节点

plt.subplot(121)
plt.title('coloured by id_emp')
nx.draw(G, node_color=df['id_emp'], cmap='viridis')

plt.subplot(122)

color_mapping = { 
    'Bad Investment': 0,
    'Median Investment': 1,
    'Good Investment':2}

plt.title('coloured by classification_roi')
nx.draw(G, node_color=df['classification_roi'].replace(color_mapping), cmap='RdYlBu')

是数值​​“返回百分比”列的。我想要带值的“id\u emp”​​在“返回百分比”列中低于50，以相互连接，以及与其他组的连接。从这些数据来看，网络应该是什么样子？为什么有两个
id\u emp
s，其值
1
？它们是两个节点还是一个节点？它们是两个节点，并根据“结果百分比”值的范围进行分组。我想根据本专栏的价值创建社区。例如：“结果百分比”值大于100的社区，51到100之间的社区。@ScottBoston我认为最好用这些值来解释​​“返回百分比”列的。我想要带值的“id\u emp”​​在“返回百分比”列中低于50，以相互连接，以及与其他组的连接。从这些数据来看，网络应该是什么样子？为什么有两个
id\u emp
s，其值
1
？它们是两个节点还是一个节点？它们是两个节点，并根据“结果百分比”值的范围进行分组。我想根据本专栏的价值创建社区。例如：“结果百分比”值大于100的社区，51到100之间的社区。@ScottBoston我认为这是更好的解释