文章目录

NetworkX实例1. 基础Basic2. 绘图Drawing3. 图标Graph

NetworkX实例

代码下载地址 NetworkX 2.4版本的通用示例性示例。本教程介绍了约定和基本的图形操作。具体章节内容如下：

基础Basic绘图Drawing图标Graph

本文参考：

https://networkx.github.io/documentation/stable/auto_examples/index.html

1. 基础Basic

读写图 Read and write graphs属性 Properties ## 读写图 Read and write graphs # Author: Aric Hagberg (hagberg@lanl.gov) # Copyright (C) 2004-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import sys import matplotlib.pyplot as plt import networkx as nx # 生成网格 G = nx.grid_2d_graph(5, 5) # 5x5 grid # print the adjacency list # 打印网络 for line in nx.generate_adjlist(G): print(line) # write edgelist to grid.edgelist # 写数据 #nx.write_edgelist(G, path="grid.edgelist", delimiter=":") # read edgelist from grid.edgelist # 读数据 #H = nx.read_edgelist(path="grid.edgelist", delimiter=":") nx.draw(G) plt.show() (0, 0) (1, 0) (0, 1) (0, 1) (1, 1) (0, 2) (0, 2) (1, 2) (0, 3) (0, 3) (1, 3) (0, 4) (0, 4) (1, 4) (1, 0) (2, 0) (1, 1) (1, 1) (2, 1) (1, 2) (1, 2) (2, 2) (1, 3) (1, 3) (2, 3) (1, 4) (1, 4) (2, 4) (2, 0) (3, 0) (2, 1) (2, 1) (3, 1) (2, 2) (2, 2) (3, 2) (2, 3) (2, 3) (3, 3) (2, 4) (2, 4) (3, 4) (3, 0) (4, 0) (3, 1) (3, 1) (4, 1) (3, 2) (3, 2) (4, 2) (3, 3) (3, 3) (4, 3) (3, 4) (3, 4) (4, 4) (4, 0) (4, 1) (4, 1) (4, 2) (4, 2) (4, 3) (4, 3) (4, 4) (4, 4)

## 属性 Properties # Copyright (C) 2004-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import matplotlib.pyplot as plt from networkx import nx G = nx.lollipop_graph(4, 6) pathlengths = [] print("source vertex {target:length, }") for v in G.nodes(): spl = dict(nx.single_source_shortest_path_length(G, v)) print('{} {} '.format(v, spl)) for p in spl: pathlengths.append(spl[p]) print('') print("average shortest path length %s" % (sum(pathlengths) / len(pathlengths))) # histogram of path lengths dist = {} for p in pathlengths: if p in dist: dist[p] += 1 else: dist[p] = 1 print('') print("length #paths") verts = dist.keys() for d in sorted(verts): print('%s %d' % (d, dist[d])) print("radius: %d" % nx.radius(G)) print("diameter: %d" % nx.diameter(G)) print("eccentricity: %s" % nx.eccentricity(G)) print("center: %s" % nx.center(G)) print("periphery: %s" % nx.periphery(G)) print("density: %s" % nx.density(G)) nx.draw(G, with_labels=True) plt.show() source vertex {target:length, } 0 {0: 0, 1: 1, 2: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 1 {1: 0, 0: 1, 2: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 2 {2: 0, 0: 1, 1: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 3 {3: 0, 0: 1, 1: 1, 2: 1, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6} 4 {4: 0, 5: 1, 3: 1, 6: 2, 0: 2, 1: 2, 2: 2, 7: 3, 8: 4, 9: 5} 5 {5: 0, 4: 1, 6: 1, 3: 2, 7: 2, 0: 3, 1: 3, 2: 3, 8: 3, 9: 4} 6 {6: 0, 5: 1, 7: 1, 4: 2, 8: 2, 3: 3, 9: 3, 0: 4, 1: 4, 2: 4} 7 {7: 0, 6: 1, 8: 1, 5: 2, 9: 2, 4: 3, 3: 4, 0: 5, 1: 5, 2: 5} 8 {8: 0, 7: 1, 9: 1, 6: 2, 5: 3, 4: 4, 3: 5, 0: 6, 1: 6, 2: 6} 9 {9: 0, 8: 1, 7: 2, 6: 3, 5: 4, 4: 5, 3: 6, 0: 7, 1: 7, 2: 7} average shortest path length 2.86 length #paths 0 10 1 24 2 16 3 14 4 12 5 10 6 8 7 6 radius: 4 diameter: 7 eccentricity: {0: 7, 1: 7, 2: 7, 3: 6, 4: 5, 5: 4, 6: 4, 7: 5, 8: 6, 9: 7} center: [5, 6] periphery: [0, 1, 2, 9] density: 0.26666666666666666

2. 绘图Drawing

简单路径Simple Path节点颜色Node Colormap边的颜色 Edge Colormap带颜色的房子 House With Colors环形树Circular Tree等级排列Degree Rank谱嵌入Spectral Embedding四宫格Four Grids自我中心网络Ego Graph度直方图Degree histogram随机几何图形Random Geometric Graph加权图Weighted Graph有向图Directed Graph标签和颜色Labels And Colors最大连通分支Giant Component地图集Atlas ## 简单路径Simple Path import matplotlib.pyplot as plt import networkx as nx G = nx.path_graph(8) nx.draw(G) plt.show()

## 节点颜色Node Colormap # Author: Aric Hagberg (hagberg@lanl.gov) import matplotlib.pyplot as plt import networkx as nx G = nx.cycle_graph(24) # 设置排列位置，iterations迭代次数 pos = nx.spring_layout(G, iterations=200) # node_color节点颜色 nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues) plt.show()

## 边的颜色 Edge Colormap # Author: Aric Hagberg (hagberg@lanl.gov) import matplotlib.pyplot as plt import networkx as nx G = nx.star_graph(20) pos = nx.spring_layout(G) colors = range(20) # edge_color边的颜色 nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors, width=4, edge_cmap=plt.cm.Blues, with_labels=False) plt.show()

## 带颜色的房子 House With Colors # Author: Aric Hagberg (hagberg@lanl.gov) import matplotlib.pyplot as plt import networkx as nx G = nx.house_graph() # explicitly set positions pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)} # 画节点 nx.draw_networkx_nodes(G, pos, node_size=2000, nodelist=[4]) nx.draw_networkx_nodes(G, pos, node_size=3000, nodelist=[0, 1, 2, 3], node_color='b') # 画线条 nx.draw_networkx_edges(G, pos, alpha=0.5, width=6) plt.axis('off') plt.show()

## 环形树Circular Tree # 管理员权限下 pip install pydot import matplotlib.pyplot as plt import networkx as nx import pydot from networkx.drawing.nx_pydot import graphviz_layout G = nx.balanced_tree(3, 5) # 设置环形布置 pos = graphviz_layout(G, prog='twopi') plt.figure(figsize=(8, 8)) nx.draw(G, pos, node_size=20, alpha=0.5, node_color="blue", with_labels=False) plt.axis('equal') plt.show()

## 等级排列Degree Rank # Author: Aric Hagberg <aric.hagberg@gmail.com> import networkx as nx import matplotlib.pyplot as plt G = nx.gnp_random_graph(100, 0.02) degree_sequence = sorted([d for n, d in G.degree()], reverse=True) # print "Degree sequence", degree_sequence dmax = max(degree_sequence) plt.loglog(degree_sequence, 'b-', marker='o') plt.title("Degree rank plot") plt.ylabel("degree") plt.xlabel("rank") # draw graph in inset plt.axes([0.45, 0.45, 0.45, 0.45]) Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0]) pos = nx.spring_layout(Gcc) plt.axis('off') nx.draw_networkx_nodes(Gcc, pos, node_size=20) nx.draw_networkx_edges(Gcc, pos, alpha=0.4) plt.show()

## 谱嵌入Spectral Embedding import matplotlib.pyplot as plt import networkx as nx options = { 'node_color': 'C0', 'node_size': 100, } G = nx.grid_2d_graph(6, 6) plt.subplot(332) nx.draw_spectral(G, **options) G.remove_edge((2, 2), (2, 3)) plt.subplot(334) nx.draw_spectral(G, **options) G.remove_edge((3, 2), (3, 3)) plt.subplot(335) nx.draw_spectral(G, **options) G.remove_edge((2, 2), (3, 2)) plt.subplot(336) nx.draw_spectral(G, **options) G.remove_edge((2, 3), (3, 3)) plt.subplot(337) nx.draw_spectral(G, **options) G.remove_edge((1, 2), (1, 3)) plt.subplot(338) nx.draw_spectral(G, **options) G.remove_edge((4, 2), (4, 3)) plt.subplot(339) nx.draw_spectral(G, **options) plt.show()

## 四宫格Four Grids # Author: Aric Hagberg (hagberg@lanl.gov) # Copyright (C) 2004-2019 # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import matplotlib.pyplot as plt import networkx as nx # 生成四宫格点 G = nx.grid_2d_graph(4, 4) # 4x4 grid # 点排列 pos = nx.spring_layout(G, iterations=100) plt.subplot(221) nx.draw(G, pos, font_size=8) plt.subplot(222) # node_color节点的颜色 nx.draw(G, pos, node_color='k', node_size=0, with_labels=False) plt.subplot(223) nx.draw(G, pos, node_color='g', node_size=250, with_labels=False, width=6) plt.subplot(224) # 设置为有向图 H = G.to_directed() nx.draw(H, pos, node_color='b', node_size=20, with_labels=False) plt.show()

## 自我中心网络Ego Graph # Author: Drew Conway (drew.conway@nyu.edu) from operator import itemgetter import matplotlib.pyplot as plt import networkx as nx if __name__ == '__main__': # Create a BA model graph n = 1000 m = 2 G = nx.generators.barabasi_albert_graph(n, m) # find node with largest degree node_and_degree = G.degree() (largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1] # Create ego graph of main hub hub_ego = nx.ego_graph(G, largest_hub) # Draw graph pos = nx.spring_layout(hub_ego) nx.draw(hub_ego, pos, node_color='b', node_size=50, with_labels=False) # Draw ego as large and red nx.draw_networkx_nodes(hub_ego, pos, nodelist=[largest_hub], node_size=300, node_color='r') plt.show()

## 度直方图Degree histogram import collections import matplotlib.pyplot as plt import networkx as nx G = nx.gnp_random_graph(100, 0.02) degree_sequence = sorted([d for n, d in G.degree()], reverse=True) # degree sequence # print "Degree sequence", degree_sequence degreeCount = collections.Counter(degree_sequence) deg, cnt = zip(*degreeCount.items()) fig, ax = plt.subplots() plt.bar(deg, cnt, width=0.80, color='b') plt.title("Degree Histogram") plt.ylabel("Count") plt.xlabel("Degree") ax.set_xticks([d + 0.4 for d in deg]) ax.set_xticklabels(deg) # draw graph in inset plt.axes([0.4, 0.4, 0.5, 0.5]) Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0]) pos = nx.spring_layout(G) plt.axis('off') nx.draw_networkx_nodes(G, pos, node_size=20) nx.draw_networkx_edges(G, pos, alpha=0.4) plt.show()

## 随机几何图形Random Geometric Graph import matplotlib.pyplot as plt import networkx as nx G = nx.random_geometric_graph(200, 0.125) # position is stored as node attribute data for random_geometric_graph pos = nx.get_node_attributes(G, 'pos') # find node near center (0.5,0.5) dmin = 1 ncenter = 0 for n in pos: x, y = pos[n] d = (x - 0.5)**2 + (y - 0.5)**2 if d < dmin: ncenter = n dmin = d # color by path length from node near center p = dict(nx.single_source_shortest_path_length(G, ncenter)) plt.figure(figsize=(8, 8)) nx.draw_networkx_edges(G, pos, nodelist=[ncenter], alpha=0.4) nx.draw_networkx_nodes(G, pos, nodelist=list(p.keys()), node_size=80, node_color=list(p.values()), cmap=plt.cm.Reds_r) plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) plt.axis('off') plt.show()

## 加权图Weighted Graph # Author: Aric Hagberg (hagberg@lanl.gov) import matplotlib.pyplot as plt import networkx as nx G = nx.Graph() # 确定边 G.add_edge('a', 'b', weight=0.6) G.add_edge('a', 'c', weight=0.2) G.add_edge('c', 'd', weight=0.1) G.add_edge('c', 'e', weight=0.7) G.add_edge('c', 'f', weight=0.9) G.add_edge('a', 'd', weight=0.3) # 长边 权重大于0.5 elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5] # 短边 权重小于0.5 esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5] # 设置位置 pos = nx.spring_layout(G) # positions for all nodes # nodes # 画节点 nx.draw_networkx_nodes(G, pos, node_size=700) # edges # 画边 nx.draw_networkx_edges(G, pos, edgelist=elarge,width=6) # style边的样式 nx.draw_networkx_edges(G, pos, edgelist=esmall,width=6, alpha=0.5, edge_color='b', style='dashed') # labels # 画标签 nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif') plt.axis('off') plt.show()

## 有向图Directed Graph # Author: Rodrigo Dorantes-Gilardi (rodgdor@gmail.com) import matplotlib as mpl import matplotlib.pyplot as plt import networkx as nx G = nx.generators.directed.random_k_out_graph(10, 3, 0.5) pos = nx.layout.spring_layout(G) node_sizes = [3 + 10 * i for i in range(len(G))] M = G.number_of_edges() edge_colors = range(2, M + 2) edge_alphas = [(5 + i) / (M + 4) for i in range(M)] nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='blue') edges = nx.draw_networkx_edges(G, pos, node_size=node_sizes, arrowstyle='->', arrowsize=10, edge_color=edge_colors, edge_cmap=plt.cm.Blues, width=2) # set alpha value for each edge for i in range(M): edges[i].set_alpha(edge_alphas[i]) pc = mpl.collections.PatchCollection(edges, cmap=plt.cm.Blues) pc.set_array(edge_colors) plt.colorbar(pc) ax = plt.gca() ax.set_axis_off() plt.show()

## 标签和颜色Labels And Colors # Author: Aric Hagberg (hagberg@lanl.gov) import matplotlib.pyplot as plt import networkx as nx # 生成立体图 G = nx.cubical_graph() # 确定位置 pos = nx.spring_layout(G) # positions for all nodes # nodes # 画节点 nx.draw_networkx_nodes(G, pos, nodelist=[0, 1, 2, 3], node_color='r', node_size=500, alpha=0.8) nx.draw_networkx_nodes(G, pos, nodelist=[4, 5, 6, 7], node_color='b', node_size=500, alpha=0.8) # edges nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5) nx.draw_networkx_edges(G, pos, edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)], width=8, alpha=0.5, edge_color='r') nx.draw_networkx_edges(G, pos, edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)], width=8, alpha=0.5, edge_color='b') # some math labels labels = {} labels[0] = r'$a$' labels[1] = r'$b$' labels[2] = r'$c$' labels[3] = r'$d$' labels[4] = r'$\alpha$' labels[5] = r'$\beta$' labels[6] = r'$\gamma$' labels[7] = r'$\delta$' # 填写标签 nx.draw_networkx_labels(G, pos, labels, font_size=16) plt.axis('off') plt.show()

## 最大连通分支Giant Component # Copyright (C) 2006-2019 # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import math import matplotlib.pyplot as plt import networkx as nx try: import pygraphviz from networkx.drawing.nx_agraph import graphviz_layout layout = graphviz_layout except ImportError: try: import pydot from networkx.drawing.nx_pydot import graphviz_layout layout = graphviz_layout except ImportError: print("PyGraphviz and pydot not found;\n" "drawing with spring layout;\n" "will be slow.") layout = nx.spring_layout n = 150 # 150 nodes # p value at which giant component (of size log(n) nodes) is expected p_giant = 1.0 / (n - 1) # p value at which graph is expected to become completely connected p_conn = math.log(n) / float(n) # the following range of p values should be close to the threshold pvals = [0.003, 0.006, 0.008, 0.015] region = 220 # for pylab 2x2 subplot layout plt.subplots_adjust(left=0, right=1, bottom=0, top=0.95, wspace=0.01, hspace=0.01) for p in pvals: G = nx.binomial_graph(n, p) pos = layout(G) region += 1 plt.subplot(region) plt.title("p = %6.3f" % (p)) nx.draw(G, pos, with_labels=False, node_size=10 ) # identify largest connected component Gcc = sorted(nx.connected_components(G), key=len, reverse=True) G0 = G.subgraph(Gcc[0]) nx.draw_networkx_edges(G0, pos, with_labels=False, edge_color='r', width=6.0 ) # show other connected components for Gi in Gcc[1:]: if len(Gi) > 1: nx.draw_networkx_edges(G.subgraph(Gi), pos, with_labels=False, edge_color='r', alpha=0.3, width=5.0 ) plt.show()

## 地图集Atlas # Author: Aric Hagberg (hagberg@lanl.gov) # Copyright (C) 2004-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import random try: import pygraphviz from networkx.drawing.nx_agraph import graphviz_layout except ImportError: try: import pydot from networkx.drawing.nx_pydot import graphviz_layout except ImportError: raise ImportError("This example needs Graphviz and either " "PyGraphviz or pydot.") import matplotlib.pyplot as plt import networkx as nx from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic from networkx.generators.atlas import graph_atlas_g def atlas6(): """ Return the atlas of all connected graphs of 6 nodes or less. Attempt to check for isomorphisms and remove. """ Atlas = graph_atlas_g()[0:208] # 208 # remove isolated nodes, only connected graphs are left U = nx.Graph() # graph for union of all graphs in atlas for G in Atlas: zerodegree = [n for n in G if G.degree(n) == 0] for n in zerodegree: G.remove_node(n) U = nx.disjoint_union(U, G) # iterator of graphs of all connected components C = (U.subgraph(c) for c in nx.connected_components(U)) UU = nx.Graph() # do quick isomorphic-like check, not a true isomorphism checker nlist = [] # list of nonisomorphic graphs for G in C: # check against all nonisomorphic graphs so far if not iso(G, nlist): nlist.append(G) UU = nx.disjoint_union(UU, G) # union the nonisomorphic graphs return UU def iso(G1, glist): """Quick and dirty nonisomorphism checker used to check isomorphisms.""" for G2 in glist: if isomorphic(G1, G2): return True return False if __name__ == '__main__': G = atlas6() print("graph has %d nodes with %d edges" % (nx.number_of_nodes(G), nx.number_of_edges(G))) print(nx.number_connected_components(G), "connected components") plt.figure(1, figsize=(8, 8)) # layout graphs with positions using graphviz neato pos = graphviz_layout(G, prog="neato") # color nodes the same in each connected subgraph C = (G.subgraph(c) for c in nx.connected_components(G)) for g in C: c = [random.random()] * nx.number_of_nodes(g) # random color... nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False ) plt.show() graph has 779 nodes with 1073 edges 137 connected components

3. 图标Graph

空手道俱乐部Karate ClubER随机图Erdos Renyi度序列Degree Sequence足球football ## 空手道俱乐部Karate Club import matplotlib.pyplot as plt import networkx as nx # 俱乐部数据 G = nx.karate_club_graph() print("Node Degree") for v in G: print('%s %s' % (v, G.degree(v))) # 画环形，其中节点表示会员 nx.draw_circular(G, with_labels=True) plt.show() Node Degree 0 16 1 9 2 10 3 6 4 3 5 4 6 4 7 4 8 5 9 2 10 3 11 1 12 2 13 5 14 2 15 2 16 2 17 2 18 2 19 3 20 2 21 2 22 2 23 5 24 3 25 3 26 2 27 4 28 3 29 4 30 4 31 6 32 12 33 17

## ER随机图Erdos Renyi # Author: Aric Hagberg (hagberg@lanl.gov) # Copyright (C) 2004-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import matplotlib.pyplot as plt from networkx import nx n = 10 # 10 nodes m = 20 # 20 edges G = nx.gnm_random_graph(n, m) # some properties print("node degree clustering") for v in nx.nodes(G): print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v))) # print the adjacency list for line in nx.generate_adjlist(G): print(line) nx.draw(G) plt.show() node degree clustering 0 2 1.000000 1 2 0.000000 2 3 0.333333 3 5 0.500000 4 5 0.500000 5 3 0.333333 6 4 0.500000 7 5 0.400000 8 5 0.300000 9 6 0.466667 0 9 8 1 7 6 2 4 5 8 3 7 4 9 5 6 4 6 9 7 5 8 6 9 7 9 8 8 9 9

## 度序列Degree Sequence # Author: Aric Hagberg (hagberg@lanl.gov) # Date: 2004-11-03 08:11:09 -0700 (Wed, 03 Nov 2004) # Revision: 503 # Copyright (C) 2004-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import matplotlib.pyplot as plt from networkx import nx z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] print(nx.is_graphical(z)) print("Configuration model") G = nx.configuration_model(z) # configuration model degree_sequence = [d for n, d in G.degree()] # degree sequence print("Degree sequence %s" % degree_sequence) print("Degree histogram") hist = {} for d in degree_sequence: if d in hist: hist[d] += 1 else: hist[d] = 1 print("degree #nodes") for d in hist: print('%d %d' % (d, hist[d])) nx.draw(G) plt.show() True Configuration model Degree sequence [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] Degree histogram degree #nodes 5 1 3 4 2 3 1 3

# 足球football # Author: Aric Hagberg (hagberg@lanl.gov) # Copyright (C) 2007-2019 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. try: # Python 3.x import urllib.request as urllib except ImportError: # Python 2.x import urllib import io import zipfile import matplotlib.pyplot as plt import networkx as nx url = "http://www-personal.umich.edu/~mejn/netdata/football.zip" sock = urllib.urlopen(url) # open URL s = io.BytesIO(sock.read()) # read into BytesIO "file" sock.close() zf = zipfile.ZipFile(s) # zipfile object txt = zf.read('football.txt').decode() # read info file gml = zf.read('football.gml').decode() # read gml data # throw away bogus first line with # from mejn files gml = gml.split('\n')[1:] G = nx.parse_gml(gml) # parse gml data #print(txt) # print degree for each team - number of games #for n, d in G.degree(): # print('%s %d' % (n, d)) options = { 'node_color': 'black', 'node_size': 50, 'line_color': 'grey', 'linewidths': 0, 'width': 0.1, } nx.draw(G, **options) plt.show()