利用Python繪制關(guān)系網(wǎng)絡(luò)圖
一、NetworkX 概述
二、NetworkX 的安裝
pip install networkx -i http://pypi.douban.com/simple --trusted-host pypi.douban.com
三、NetworkX 基礎(chǔ)知識
1. 創(chuàng)建圖
import network as nx
G = nx.Graph()
G = nx.DiGraph()
G = nx.MultiGraph()
G = nx.MultiDiGraph()
import networkx as nx
import matplotlib.pyplot as plt
G = nx.DiGraph()
G.add_node('z') # 添加節(jié)點z
G.add_nodes_from([1, 2, 3]) # 添加節(jié)點 1 2 3
G.add_edge('x', 'y') # 添加邊 起點為x 終點為y
G.add_edges_from([(1, 2), (1, 3), (2, 3)]) # 添加多條邊
# 網(wǎng)絡(luò)圖繪制與顯示
nx.draw(G, with_labels=True)
plt.show()
nx.draw() 方法里的參數(shù)
nx.draw(G, pos=nx.random_layout(G), node_color = 'b', edge_color = 'r', with_labels = True, font_size =18, node_size =20)
G：待繪制的網(wǎng)絡(luò)圖G
node_size：指定節(jié)點的尺寸大小(默認(rèn)是300)
node_color: 指定節(jié)點的顏色 (可以用字符串簡單標(biāo)識顏色，例如'r'為紅色，'g'為綠色這樣)
node_shape: 節(jié)點的形狀（默認(rèn)是圓形，用字符串'o'標(biāo)識）
alpha: 透明度 (默認(rèn)是1.0，不透明，0為完全透明)
width: 邊的寬度 (默認(rèn)為1.0)
edge_color: 邊的顏色(默認(rèn)為黑色)
style: 邊的樣式(默認(rèn)為實現(xiàn)，可選：solid | dashed | dotted | dashdot
with_labels：節(jié)點是否帶標(biāo)簽
font_size: 節(jié)點標(biāo)簽字體大小
font_color: 節(jié)點標(biāo)簽字體顏色（默認(rèn)為黑色）
3. 運用布局
import networkx as nx
import matplotlib.pyplot as plt
# 初始化一個有向圖對象
DG = nx.DiGraph()
DG.add_node('X')
# 添加節(jié)點 傳入列表
DG.add_nodes_from(['A', 'B', 'C', 'D', 'E'])
print(f'輸出圖的全部節(jié)點：{DG.nodes}')
print(f'輸出節(jié)點的數(shù)量：{DG.number_of_nodes()}')
# 添加邊 傳入列表 列表里每個元素是一個元組 元組里表示一個點指向另一個點的邊
DG.add_edges_from([('A', 'B'), ('A', 'C'), ('A', 'D'), ('D', 'A'), ('E', 'A'), ('E', 'D')])
DG.add_edge('X', 'C')
print(f'輸出圖的全部邊:{DG.edges}')
print(f'輸出邊的數(shù)量：{DG.number_of_edges()}')
# 可自定義節(jié)點顏色
colors = ['pink', 'blue', 'green', 'yellow', 'red', 'brown']
# 運用布局
pos = nx.circular_layout(DG)
# 繪制網(wǎng)絡(luò)圖
nx.draw(DG, pos=pos, with_labels=True, node_size=200, width=0.6, node_color=colors)
# 展示圖片
plt.show()
輸出圖的全部節(jié)點：['X', 'A', 'B', 'C', 'D', 'E']
輸出節(jié)點的數(shù)量：6
輸出圖的全部邊:[('X', 'C'), ('A', 'B'), ('A', 'C'), ('A', 'D'), ('D', 'A'), ('E', 'A'), ('E', 'D')]
輸出邊的數(shù)量：7
1. 提取數(shù)據(jù)
import pandas as pd
df = pd.read_csv('soccer.csv', encoding='gbk')
data = df['Club'].value_counts()
# 球員人數(shù)最多的5個俱樂部
clubs = list(data.index[:5])
# 球員數(shù)量最多的俱樂部抽取30名
df1 = df[df['Club'] == clubs[0]].sample(30, axis=0)
# 剩下4個俱樂部各抽取5名
df2 = df[df['Club'] == clubs[1]].sample(5, axis=0)
df3 = df[df['Club'] == clubs[2]].sample(5, axis=0)
df4 = df[df['Club'] == clubs[3]].sample(5, axis=0)
df5 = df[df['Club'] == clubs[4]].sample(5, axis=0)
# 合并多個DataFrame
result = pd.concat([df1, df2, df3, df4, df5], axis=0, ignore_index=True)
# 打亂DataFrame順序
new_result = result.sample(frac=1).reset_index(drop=True)
# new_result.info()
# 抽樣的數(shù)據(jù)保存到excel
new_result.to_excel('samples.xlsx')
import pandas as pd
df = pd.read_excel('samples.xlsx')
df.info()
2. 畫網(wǎng)絡(luò)圖
import pandas as pd
df = pd.read_excel('samples.xlsx')
df = df.loc[::, ['Name', 'Club']]
print(df['Club'].value_counts())
datas = df.values.tolist()
name = [datas[i][0] for i in range(len(datas))]
nodes = [str(i) for i in range(len(datas))]
club = [datas[i][1] for i in range(len(datas))]
# print(nodes)
df = pd.DataFrame({'姓名': name, '節(jié)點編號': nodes, '所屬俱樂部': club})
df.to_csv('nodes_info.csv')
with open('record.txt', 'w') as f:
for i in range(len(nodes)):
for j in range(i, len(nodes) - 1):
if datas[i][1] == datas[j+1][1]: # 屬于同一俱樂部
f.write(f'{nodes[i]}-{nodes[j + 1]}-{datas[i][1]}'+ '\n')
(1) 隨機分布網(wǎng)絡(luò)圖
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
from collections importCounter
df = pd.read_csv('nodes_info.csv')['所屬俱樂部']
items = df.values
print(Counter(items))
node_colors = []
# 5個俱樂部 屬于同一個俱樂部的節(jié)點設(shè)置相同顏色
for item in items:
if item == 'Free Agents':
node_colors.append('red')
elif item == 'Real Madrid':
node_colors.append('yellow')
elif item == 'Chelsea':
node_colors.append('blue')
elif item == 'FC Barcelona':
node_colors.append('green')
elif item == 'Manchester Utd':
node_colors.append('pink')
DG = nx.MultiGraph()
DG.add_nodes_from([str(i) for i in range(0, 50)])
DG.nodes()
with open('record.txt', 'r') as f:
con = f.read().split('\n')
edges_list = []
for i in con[:-1]:
edges_list.append(tuple(i.split('-')[:2]))
print(edges_list)
DG.add_edges_from(edges_list)
# 運用布局
pos = nx.random_layout(DG) # 節(jié)點隨機分布
# 繪制網(wǎng)絡(luò)圖
nx.draw(DG, pos, with_labels=True, node_size=200, width=0.6, node_color=node_colors)
# 顯示圖片
plt.show()
(2) Fruchterman-Reingold 算法排列節(jié)點網(wǎng)絡(luò)圖
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
from collections importCounter
df = pd.read_csv('nodes_info.csv')['所屬俱樂部']
items = df.values
print(Counter(items))
node_colors = []
# 5個俱樂部 屬于同一個俱樂部的節(jié)點設(shè)置相同顏色
for item in items:
if item == 'Free Agents':
node_colors.append('red')
elif item == 'Real Madrid':
node_colors.append('yellow')
elif item == 'Chelsea':
node_colors.append('blue')
elif item == 'FC Barcelona':
node_colors.append('green')
elif item == 'Manchester Utd':
node_colors.append('pink')
DG = nx.MultiGraph()
DG.add_nodes_from([str(i) for i in range(0, 50)])
DG.nodes()
with open('record.txt', 'r') as f:
con = f.read().split('\n')
edges_list = []
for i in con[:-1]:
edges_list.append(tuple(i.split('-')[:2]))
print(edges_list)
DG.add_edges_from(edges_list)
# 運用布局
pos = nx.spring_layout(DG) # 用Fruchterman-Reingold算法排列節(jié)點（樣子類似多中心放射狀）
# 繪制網(wǎng)絡(luò)圖
nx.draw(DG, pos, node_size=10, width=0.6, node_color=node_colors)
# 顯示圖片
plt.show()
(3) 同心圓分布網(wǎng)絡(luò)圖
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
from collections importCounter
df = pd.read_csv('nodes_info.csv')['所屬俱樂部']
items = df.values
print(Counter(items))
node_colors = []
# 5個俱樂部 屬于同一個俱樂部的節(jié)點設(shè)置相同顏色
for item in items:
if item == 'Free Agents':
node_colors.append('red')
elif item == 'Real Madrid':
node_colors.append('yellow')
elif item == 'Chelsea':
node_colors.append('blue')
elif item == 'FC Barcelona':
node_colors.append('green')
elif item == 'Manchester Utd':
node_colors.append('pink')
DG = nx.MultiGraph()
DG.add_nodes_from([str(i) for i in range(0, 50)])
DG.nodes()
with open('record.txt', 'r') as f:
con = f.read().split('\n')
edges_list = []
for i in con[:-1]:
edges_list.append(tuple(i.split('-')[:2]))
print(edges_list)
DG.add_edges_from(edges_list)
# 運用布局
pos = nx.shell_layout(DG) # 節(jié)點在同心圓上分布
# 繪制網(wǎng)絡(luò)圖
nx.draw(DG, pos, with_labels=True, node_size=200, width=0.6, node_color=node_colors)
# 顯示圖片
plt.show()
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