Python 使用 NetworkX

python 使用 NetworkX

说明：本篇文章主要讲述 Python 使用 networkx 绘制有向图；

1. 介绍&安装

NetworkX 是一个用于创建、操作和研究复杂网络的 Python 库。它提供了丰富的功能，可以帮助你创建、分析和可视化各种类型的网络，例如社交网络、WEB图、生物网络等。NetworkX 可以用来创建各种类型的网络，包括有向图和无向图。它提供了各种方法来添加、删除和修改网络中的节点和边。你可以使用节点和边的属性来进一步描述网络的特性。

NetworkX 还提供了许多图的算法和分析工具。你可以使用这些工具来计算各种网络指标，比如节点的度、网络的直径、最短路径等。你还可以使用它来发现社区结构、进行图的聚类分析等。

除了功能强大的操作和分析工具，NetworkX还提供了多种方式来可视化网络。你可以使用它来绘制网络的节点和边，设置节点的颜色、尺寸和标签等。

总的来说，NetworkX是一个功能丰富、灵活易用的Python库，用于创建、操作和研究各种类型的复杂网络。无论你是在进行学术研究、数据分析还是网络可视化，NetworkX都是一个不错的选择。

安装

pip install networkx

2. 简单的有向图绘制

简单的类展示

import networkx as nx  # 导入 NetworkX 工具包# 创建 图G1 = nx.Graph()  # 创建：空的 无向图G2 = nx.DiGraph()  #创建：空的 有向图G3 = nx.MultiGraph()  #创建：空的 多图G4 = nx.MultiDiGraph()  #创建：空的 有向多图

三节点有向图的绘制

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 1. 创建有向图对象, 创建空的有向图的对象G = nx.DiGraph()# 2. 添加节点G.add_node('A')G.add_node('B')G.add_node('C')# 3. 添加有向边G.add_edge('A', 'B')G.add_edge('B', 'C')# 4. 进行图的绘制pos = nx.spring_layout(G)  # 选择布局算法;nx.draw(G, pos, with_labels=True)plt.show()

绘制分支节点的有向图

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 1. 创建有向图对象, 创建空的有向图的对象G = nx.DiGraph()# 2. 添加节点G.add_node('A')G.add_node('B')G.add_node('C')G.add_node('D')G.add_node('E')# 3. 添加有向边G.add_edge('A', 'B')G.add_edge('B', 'C')G.add_edge('B', 'D')G.add_edge('C', 'E')# 4. 进行图的绘制pos = nx.spring_layout(G)  # 选择布局算法;nx.draw(G, pos, with_labels=True)plt.show()

3.有向图的遍历

三节点有向图的遍历

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 1. 创建有向图对象, 创建空的有向图的对象G = nx.DiGraph()# 2. 添加节点G.add_node('A')G.add_node('B')G.add_node('C')# G.add_node('D')# G.add_node('E')# 3. 添加有向边G.add_edge('A', 'B')G.add_edge('B', 'C')# G.add_edge('B', 'D')# G.add_edge('C', 'E')# 4. 进行图的绘制pos = nx.spring_layout(G)  # 选择布局算法;nx.draw(G, pos, with_labels=True)plt.show()# 5. 有向图的遍历print(G.nodes)  # 节点列表, 输出节点 列表for node in G.nodes():    print("节点>>>", node)

绘制分支节点图形的输出

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 1. 创建有向图对象, 创建空的有向图的对象G = nx.DiGraph()# 2. 添加节点G.add_node('A')G.add_node('B')G.add_node('C')G.add_node('D')G.add_node('E')# 3. 添加有向边G.add_edge('A', 'B')G.add_edge('B', 'C')G.add_edge('B', 'D')G.add_edge('C', 'E')# 4. 进行图的绘制pos = nx.spring_layout(G)  # 选择布局算法;nx.draw(G, pos, with_labels=True)plt.show()# 5. 有向图的遍历print(G.nodes)  # 节点列表# print(G.edges)  # 边列表, [('A', 'B'), ('B', 'C'), ('B', 'D'), ('C', 'E')]for node in G.nodes():    print("节点>>>", node)    in_degree = G.in_degree(node)    print("入度:", in_degree)    # 获取节点的出度    out_degree = G.out_degree(node)    print("出度:", out_degree)    # 获取节点的邻居节点    neighbors = G.neighbors(node)    print("邻居节点:", list(neighbors))

4.带权重的边

本章节的内容主要展示带权重的边的绘制，并且求取出最大值和最小值；

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 创建有向图G = nx.DiGraph()# 直接创建边, 自动添加两个节点, 并且设置边的权重G.add_edge('A', 'B', weight=3)G.add_edge('B', 'C', weight=5)G.add_edge('C', 'D', weight=2)G.add_edge('C', 'E', weight=5)pos = nx.spring_layout(G)nx.draw(G, pos, with_labels=True)# 获取边的权重labels = nx.get_edge_attributes(G, 'weight')# 绘制带有权重的边nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)plt.show()# 获取边的权重值列表weights = nx.get_edge_attributes(G, 'weight').values()print("边的权重值列表>>>", weights)max_weight = max(weights)min_weight = min(weights)print("最大权重的边:", max_weight)print("最小权重的边:", min_weight)

求取图形中得最短路径

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as pltdef get_shortest_path(graph, source, target):    try:        shortest_path = nx.shortest_path(graph, source, target)        return shortest_path    except nx.exception.NetworkXNoPath:        return "不存在最短路径"def get_longest_path(graph, source, target):    all_paths = nx.all_simple_paths(graph, source, target)    longest_path = max(all_paths, key=len)    return longest_path# 创建有向图G = nx.DiGraph()G.add_edge('A', 'B')G.add_edge('A', 'C')G.add_edge('B', 'C')G.add_edge('B', 'D')G.add_edge('C', 'D')pos = nx.spring_layout(G)  # 选择布局算法;nx.draw(G, pos, with_labels=True)plt.show()# 求取最短路径shortest_path = get_shortest_path(G, 'A', 'D')print("最短路径:", shortest_path)# 求取最长路径longest_path = get_longest_path(G, 'A', 'D')print("最长路径:", longest_path)

按照权重求最短路径&最长路径

# -*- coding: utf-8 -*-import networkx as nximport matplotlib.pyplot as plt# 创建有向带权重图G = nx.DiGraph()G.add_edge('A', 'B', weight=3)G.add_edge('A', 'C', weight=5)G.add_edge('B', 'C', weight=2)G.add_edge('B', 'D', weight=4)G.add_edge('C', 'D', weight=1)pos = nx.spring_layout(G)nx.draw(G, pos, with_labels=True)# 获取边的权重labels = nx.get_edge_attributes(G, 'weight')# 绘制带有权重的边nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)plt.show()# 按照权重求取最短路径shortest_path = nx.dijkstra_path(G, 'A', 'D', weight='weight')shortest_distance = nx.dijkstra_path_length(G, 'A', 'D', weight='weight')print("最短路径:", shortest_path)print("最短距离:", shortest_distance)

import networkx as nx# 创建有向带权重图G = nx.DiGraph()G.add_edge('A', 'B', weight=3)G.add_edge('A', 'C', weight=5)G.add_edge('B', 'C', weight=2)G.add_edge('B', 'D', weight=4)G.add_edge('C', 'D', weight=1)# 将边的权重取相反数G_neg = nx.DiGraph()for u, v, data in G.edges(data=True):    G_neg.add_edge(u, v, weight=-data['weight'])# 按照权重取相反数的图中求取最短路径longest_path = nx.dijkstra_path(G_neg, 'A', 'D', weight='weight')longest_distance = -nx.dijkstra_path_length(G_neg, 'A', 'D', weight='weight')print("最长路径:", longest_path)print("最长距离:", longest_distance)

5.JSON 数据的转换

**说明：**前后端交互的时候通常传回的时候的 json 格式化后的数据，通常需要构建一下，因此最好创建一个统一的类进行封装。

# -*- coding: utf-8 -*-"""图的封装;"""import networkx as nximport matplotlib.pyplot as pltclass GraphDict(object):    """有向图的构造;    """    def __init__(self):        """初始化的封装;        """        self.graph = None  # 有向图的构建        self.graph_weight = None  # 有向图带权重    def dict_to_graph(self, data: list):        """        图的字典模式;        :param data:        :return:        example:            data=[{              source: 'Node 1',              target: 'Node 3'            },            {              source: 'Node 2',              target: 'Node 3'            },            {              source: 'Node 2',              target: 'Node 4'            },            {              source: 'Node 1',              target: 'Node 4'            }]        """        graph = nx.DiGraph()  # 创建有向图        # 循环添加边, 直接添加上节点        for item in data:            graph.add_edge(item.get('source'), item.get("target"))        # 赋值并返回        self.graph = graph        return graph    def dict_to_graph_weight(self, data: list):        """        图的字典模式, 带权重;        :param data:        :return:        example:            data = [                {                  source: 'Node 1',                  target: 'Node 3',                  weight: 5                },                {                  source: 'Node 2',                  target: 'Node 3',                  weight: 3,                },                {                  source: 'Node 2',                  target: 'Node 4',                  weight: 5,                },                {                  source: 'Node 1',                  target: 'Node 4',                  weight: 5                }            ]        """        graph = nx.DiGraph()  # 创建有向图        # 循环添加边, 直接添加上节点, 并且为边赋值上权重;        for item in data:            graph.add_edge(item.get('source'), item.get("target"), weight=item.get("weight"))        # 赋值并返回        self.graph_weight = graph        return graph    def graph_to_dict(self):        """        图的数据转换成为字典的数据;        :return:        """        assert self.graph is not None or self.graph_weight is not None, "必须首先通过该类创建一个有向图"        if self.graph is None:            edges = self.graph_weight.edges(data=True)            return [{"source": s, "target": t, "weight": w['weight']} for s, t, w in edges]        else:            edges = self.graph.edges()            return [{"source": s, "target": t} for s, t in edges]    def show(self):        """        有向图的显示;        :return:        """        assert self.graph is not None or self.graph_weight is not None, "必须首先通过该类创建一个有向图"        if self.graph is None:            pos = nx.spring_layout(self.graph_weight)            nx.draw(self.graph_weight, pos, with_labels=True)            labels = nx.get_edge_attributes(self.graph_weight, 'weight')            nx.draw_networkx_edge_labels(self.graph_weight, pos, edge_labels=labels)            plt.show()        else:            pos = nx.spring_layout(self.graph)            nx.draw(self.graph, pos, with_labels=True)            plt.show()    def to_png(self, name: str):        """        导出有向图的 png 图片;        :param name; str, 想要导出 png 图片的名称;        :return:        """        assert self.graph is not None or self.graph_weight is not None, "必须首先通过该类创建一个有向图"        if self.graph is None:            pos = nx.spring_layout(self.graph_weight)            nx.draw(self.graph_weight, pos, with_labels=True)            labels = nx.get_edge_attributes(self.graph_weight, 'weight')            nx.draw_networkx_edge_labels(self.graph_weight, pos, edge_labels=labels)            plt.savefig(name)        else:            pos = nx.spring_layout(self.graph)            nx.draw(self.graph, pos, with_labels=True)            plt.savefig(name)if __name__ == '__main__':    graph = GraphDict()    data = [        {            "source": 'Node 1',            "target": 'Node 3'        },        {            "source": 'Node 2',            "target": 'Node 3'        },        {            "source": 'Node 2',            "target": 'Node 4'        },        {            "source": 'Node 1',            "target": 'Node 4'        }]    data_weight = [        {            "source": 'Node 1',            "target": 'Node 3',            "weight": 3        },        {            "source": 'Node 2',            "target": 'Node 3',            "weight": 3        },        {            "source": 'Node 2',            "target": 'Node 4',            "weight": 3        },        {            "source": 'Node 1',            "target": 'Node 4',            "weight": 3        }]    # graph.dict_to_graph(data)    # graph.to_png("有向图的导出")    graph.dict_to_graph_weight(data_weight)    graph.to_png("权重")    v = graph.graph_to_dict()    print(v)

继续努力，终成大器；

来源地址：https://blog.csdn.net/m0_61970162/article/details/131498998