{"id":1010655,"date":"2024-12-27T11:23:05","date_gmt":"2024-12-27T03:23:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1010655.html"},"modified":"2024-12-27T11:23:07","modified_gmt":"2024-12-27T03:23:07","slug":"python%e7%94%bb%e5%9b%be%e5%a6%82%e4%bd%95%e6%98%be%e7%a4%ba%e9%a1%b6%e7%82%b9","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1010655.html","title":{"rendered":"python\u753b\u56fe\u5982\u4f55\u663e\u793a\u9876\u70b9"},"content":{"rendered":"

\"python\u753b\u56fe\u5982\u4f55\u663e\u793a\u9876\u70b9\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u56fe\u5f62\u5e76\u663e\u793a\u9876\u70b9\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4f8b\u5982\u4f7f\u7528Matplotlib\u3001NetworkX\u3001Plotly\u7b49\u5e93\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u5229\u7528Matplotlib\u4e0eNetworkX\u7ed3\u5408\u7ed8\u5236\u56fe\u5f62\uff0c\u5e76\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u663e\u793a\u9876\u70b9\u3001\u8bbe\u7f6e\u9876\u70b9\u6807\u7b7e\u3001\u989c\u8272\u3001\u5927\u5c0f\u7b49\u3002<\/strong>\u8fd9\u91cc\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528Matplotlib\u548cNetworkX\u7ed8\u5236\u56fe\u5f62\u5e76\u663e\u793a\u9876\u70b9\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528MATPLOTLIB\u548cNETWORKX\u7ed8\u5236\u56fe\u5f62<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0cNetworkX\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u590d\u6742\u7f51\u7edc\u7684\u5e93\u3002\u7ed3\u5408\u8fd9\u4e24\u4e2a\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u56fe\u5f62\u5e76\u663e\u793a\u9876\u70b9\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u548cNetworkX\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib networkx<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u5e76\u7ed8\u5236\u7b80\u5355\u56fe\u5f62<\/h4>\n<\/p>\n
\u4f7f\u7528NetworkX\u521b\u5efa\u56fe\u5f62\uff0c\u7136\u540e\u4f7f\u7528Matplotlib\u7ed8\u5236\u8be5\u56fe\u5f62\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import networkx as nx<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u5f62<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4)])<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
pos = nx.spring_layout(G)<\/p>\n
nx.draw(G, pos, with_labels=True, node_color='lightblue', edge_color='gray')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0cnx.spring_layout(G)<\/code>\u7528\u4e8e\u751f\u6210\u9876\u70b9\u7684\u5e03\u5c40\uff0cnx.draw<\/code>\u7528\u4e8e\u7ed8\u5236\u56fe\u5f62\uff0c\u5176\u4e2dwith_labels=True<\/code>\u8868\u793a\u663e\u793a\u9876\u70b9\u6807\u7b7e\u3002<\/p>\n<\/p>\n
3. \u81ea\u5b9a\u4e49\u9876\u70b9\u663e\u793a<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u9876\u70b9\u7684\u663e\u793a\uff0c\u4f8b\u5982\u66f4\u6539\u9876\u70b9\u989c\u8272\u3001\u5927\u5c0f\u3001\u5f62\u72b6\u7b49\u3002<\/p>\n<\/p>\n
nx.draw(G, pos, with_labels=True, node_size=500, node_color='lightblue', font_size=10, font_color='black')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u66f4\u6539node_size<\/code>\u3001node_color<\/code>\u3001font_size<\/code>\u7b49\u53c2\u6570\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u9876\u70b9\u663e\u793a\u7684\u81ea\u5b9a\u4e49\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5728\u56fe\u5f62\u4e2d\u663e\u793a\u9876\u70b9\u7684\u5177\u4f53\u4fe1\u606f<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u6211\u4eec\u4e0d\u4ec5\u9700\u8981\u5728\u56fe\u5f62\u4e2d\u663e\u793a\u9876\u70b9\u7684\u6807\u7b7e\uff0c\u8fd8\u9700\u8981\u663e\u793a\u9876\u70b9\u7684\u5176\u4ed6\u4fe1\u606f\uff0c\u6bd4\u5982\u6743\u91cd\u3001\u5c5e\u6027\u7b49\u3002<\/p>\n<\/p>\n
1. \u6dfb\u52a0\u9876\u70b9\u5c5e\u6027<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4e3a\u9876\u70b9\u6dfb\u52a0\u5c5e\u6027\uff0c\u5e76\u5728\u56fe\u5f62\u4e2d\u663e\u793a\u8fd9\u4e9b\u5c5e\u6027\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u9876\u70b9\u5c5e\u6027<\/p>\n
G.nodes[1]['weight'] = 0.5<\/p>\n
G.nodes[2]['weight'] = 0.8<\/p>\n
G.nodes[3]['weight'] = 0.3<\/p>\n
G.nodes[4]['weight'] = 0.9<\/p>\n
\u5728\u9876\u70b9\u4e0a\u663e\u793a\u5c5e\u6027<\/strong><\/h2>\n
labels = nx.get_node_attributes(G, 'weight')<\/p>\n
nx.draw(G, pos, with_labels=True, node_size=500, node_color='lightblue', font_size=10, font_color='black')<\/p>\n
nx.draw_networkx_labels(G, pos, labels=labels)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0cnx.get_node_attributes(G, 'weight')<\/code>\u7528\u4e8e\u83b7\u53d6\u9876\u70b9\u7684\u5c5e\u6027\uff0cnx.draw_networkx_labels<\/code>\u7528\u4e8e\u5728\u9876\u70b9\u4e0a\u663e\u793a\u8fd9\u4e9b\u5c5e\u6027\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528\u4e0d\u540c\u7684\u56fe\u5f62\u5e93<\/h4>\n<\/p>\n
\u9664\u4e86\u4f7f\u7528Matplotlib\u548cNetworkX\uff0cPlotly\u4e5f\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
\u4f7f\u7528Plotly\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
edge_trace = go.Scatter(<\/p>\n
    x=[],<\/p>\n
    y=[],<\/p>\n
    line=dict(width=0.5, color='#888'),<\/p>\n
    hoverinfo='none',<\/p>\n
    mode='lines')<\/p>\n
for edge in G.edges():<\/p>\n
    x0, y0 = pos[edge[0]]<\/p>\n
    x1, y1 = pos[edge[1]]<\/p>\n
    edge_trace['x'] += [x0, x1, None]<\/p>\n
    edge_trace['y'] += [y0, y1, None]<\/p>\n
node_trace = go.Scatter(<\/p>\n
    x=[],<\/p>\n
    y=[],<\/p>\n
    text=[],<\/p>\n
    mode='markers+text',<\/p>\n
    hoverinfo='text',<\/p>\n
    marker=dict(<\/p>\n
        showscale=True,<\/p>\n
        colorscale='YlGnBu',<\/p>\n
        reversescale=True,<\/p>\n
        color=[],<\/p>\n
        size=10,<\/p>\n
        colorbar=dict(<\/p>\n
            thickness=15,<\/p>\n
            title='Node Connections',<\/p>\n
            xanchor='left',<\/p>\n
            titleside='right'<\/p>\n
        ),<\/p>\n
        line=dict(width=2)))<\/p>\n
for node in G.nodes():<\/p>\n
    x, y = pos[node]<\/p>\n
    node_trace['x'].append(x)<\/p>\n
    node_trace['y'].append(y)<\/p>\n
fig = go.Figure(data=[edge_trace, node_trace],<\/p>\n
             layout=go.Layout(<\/p>\n
                title='<br>Network graph made with Python',<\/p>\n
                titlefont=dict(size=16),<\/p>\n
                showlegend=False,<\/p>\n
                hovermode='closest',<\/p>\n
                margin=dict(b=20,l=5,r=5,t=40),<\/p>\n
                annotations=[ dict(<\/p>\n
                    text="Python code for interactive graph visualization",<\/p>\n
                    showarrow=False,<\/p>\n
                    xref="paper", yref="paper",<\/p>\n
                    x=0.005, y=-0.002 ) ],<\/p>\n
                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),<\/p>\n
                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))<\/p>\n
                )<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
Plotly\u5141\u8bb8\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u5e76\u80fd\u591f\u663e\u793a\u66f4\u4e30\u5bcc\u7684\u9876\u70b9\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f18\u5316\u56fe\u5f62\u7684\u9876\u70b9\u663e\u793a<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u9876\u70b9\u8fdb\u884c\u4f18\u5316\u663e\u793a\uff0c\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528\u4e0d\u540c\u7684\u5e03\u5c40\u7b97\u6cd5<\/h4>\n<\/p>\n
NetworkX\u63d0\u4f9b\u4e86\u591a\u79cd\u5e03\u5c40\u7b97\u6cd5\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u5e03\u5c40\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u4e0d\u540c\u7684\u5e03\u5c40\u7b97\u6cd5<\/p>\n
pos_shell = nx.shell_layout(G)<\/p>\n
nx.draw(G, pos_shell, with_labels=True, node_color='lightblue', edge_color='gray')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5e38\u7528\u7684\u5e03\u5c40\u7b97\u6cd5\u5305\u62ecspring_layout<\/code>\u3001shell_layout<\/code>\u3001circular_layout<\/code>\u7b49\u3002<\/p>\n<\/p>\n
2. \u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6837\u5f0f<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u81ea\u5b9a\u4e49\u989c\u8272\u548c\u6837\u5f0f\u6765\u63d0\u9ad8\u56fe\u5f62\u7684\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n
colors = ['red' if node == 1 else 'blue' for node in G.nodes()]<\/p>\n
nx.draw(G, pos, with_labels=True, node_size=500, node_color=colors, font_size=10, font_color='black')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0c\u901a\u8fc7\u5217\u8868\u751f\u6210\u5668\u4e3a\u9876\u70b9\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\uff0c\u4ece\u800c\u7a81\u51fa\u663e\u793a\u7279\u5b9a\u7684\u9876\u70b9\u3002<\/p>\n<\/p>\n
3. \u6dfb\u52a0\u56fe\u4f8b<\/h4>\n<\/p>\n
\u5728\u590d\u6742\u7684\u56fe\u5f62\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u6dfb\u52a0\u56fe\u4f8b\u6765\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u56fe\u4f8b<\/p>\n
legend_labels = {'Type 1': 'red', 'Type 2': 'blue'}<\/p>\n
for label, color in legend_labels.items():<\/p>\n
    plt.scatter([], [], c=color, label=label)<\/p>\n
plt.legend()<\/p>\n
nx.draw(G, pos, with_labels=True, node_size=500, node_color=colors, font_size=10, font_color='black')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u5728\u56fe\u5f62\u4e0a\u6dfb\u52a0\u56fe\u4f8b\uff0c\u53ef\u4ee5\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u56fe\u5f62\u4e2d\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Python\u7684Matplotlib\u548cNetworkX\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u56fe\u5f62\u5e76\u663e\u793a\u9876\u70b9\u3002\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u548c\u4f7f\u7528\u4e0d\u540c\u7684\u5e03\u5c40\u7b97\u6cd5\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u9876\u70b9\u663e\u793a\u7684\u81ea\u5b9a\u4e49\u3002\u8fd8\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Plotly\u7b49\u5e93\u521b\u5efa\u66f4\u4e3a\u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u4f18\u5316\u9876\u70b9\u7684\u663e\u793a\u53ef\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002\u8fd9\u4e9b\u6280\u5de7\u5728\u6570\u636e\u5206\u6790\u3001\u7f51\u7edc\u7814\u7a76\u548c\u4fe1\u606f\u53ef\u89c6\u5316\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u7ed8\u56fe\u4e2d\u6dfb\u52a0\u9876\u70b9\u6807\u8bb0\uff1f<\/strong>
\u5728Python\u4e2d\u7ed8\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u6765\u663e\u793a\u9876\u70b9\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528plt.scatter()<\/code>\u51fd\u6570\u5728\u56fe\u5f62\u7684\u7279\u5b9a\u5750\u6807\u4e0a\u6dfb\u52a0\u70b9\uff0c\u540c\u65f6\u4f7f\u7528plt.annotate()<\/code>\u51fd\u6570\u4e3a\u8fd9\u4e9b\u70b9\u6dfb\u52a0\u6807\u7b7e\u3002\u8fd9\u6837\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u6e05\u695a\u5730\u770b\u5230\u9876\u70b9\uff0c\u8fd8\u80fd\u63d0\u4f9b\u66f4\u591a\u4fe1\u606f\u3002<\/p>\n
\u4f7f\u7528\u54ea\u4e2aPython\u5e93\u6700\u9002\u5408\u7ed8\u5236\u5e26\u9876\u70b9\u7684\u56fe\u5f62\uff1f<\/strong>
Matplotlib\u662f\u6700\u6d41\u884c\u7684Python\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u9002\u5408\u7528\u4e8e\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u5305\u62ec\u5e26\u9876\u70b9\u7684\u56fe\u3002Seaborn\u548cPlotly\u7b49\u5e93\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u7c7b\u4f3c\u529f\u80fd\uff0c\u4f46Matplotlib\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7075\u6d3b\u7684\u63a7\u5236\u548c\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u9002\u5408\u9700\u8981\u7ec6\u81f4\u8c03\u6574\u7684\u573a\u5408\u3002<\/p>\n
\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u5982\u4f55\u786e\u4fdd\u9876\u70b9\u663e\u793a\u5f97\u66f4\u52a0\u6e05\u6670\uff1f<\/strong>
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