{"id":1100933,"date":"2025-01-08T15:44:32","date_gmt":"2025-01-08T07:44:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1100933.html"},"modified":"2025-01-08T15:44:35","modified_gmt":"2025-01-08T07:44:35","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8python%e9%87%8c%e7%94%bb%e8%9c%98%e8%9b%9b%e7%bd%91-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1100933.html","title":{"rendered":"\u5982\u4f55\u5728python\u91cc\u753b\u8718\u86db\u7f51"},"content":{"rendered":"

\"\u5982\u4f55\u5728python\u91cc\u753b\u8718\u86db\u7f51\"<\/p>\n

\u5728Python\u91cc\u753b\u8718\u86db\u7f51\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001NetworkX\u3001Plotly\u7b49\u5e93\u6765\u5b9e\u73b0\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u521b\u5efa\u5404\u79cd2D\u56fe\u5f62\uff0cNetworkX\u4e13\u6ce8\u4e8e\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\uff0c\u800cPlotly\u662f\u4e00\u4e2a\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u5f62\u7684\u5e93\u3002\u4e0b\u9762\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u8718\u86db\u7f51\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u8718\u86db\u7f51\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\u5b83\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u8718\u86db\u7f51<\/p>\n<\/p>\n
Matplotlib\u63d0\u4f9b\u4e86\u6781\u5750\u6807\u56fe\uff08polar plot\uff09\uff0c\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8718\u86db\u7f51\u7684\u56fe\u5f62\u7c7b\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e00\u4e2a\u57fa\u672c\u7684\u8718\u86db\u7f51\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u5b9a\u4e49\u8718\u86db\u7f51\u7684\u8f74\u6570<\/strong><\/h2>\n
labels = np.array(['A', 'B', 'C', 'D', 'E', 'F'])<\/p>\n
num_vars = len(labels)<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
values = [4, 3, 2, 5, 4, 3]<\/p>\n
values += values[:1]  # \u4f7f\u56fe\u5f62\u95ed\u5408<\/p>\n
\u8ba1\u7b97\u89d2\u5ea6<\/strong><\/h2>\n
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()<\/p>\n
angles += angles[:1]<\/p>\n
\u521b\u5efa\u7ed8\u56fe<\/strong><\/h2>\n
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))<\/p>\n
\u7ed8\u5236\u8718\u86db\u7f51<\/strong><\/h2>\n
ax.fill(angles, values, color='blue', alpha=0.25)<\/p>\n
ax.plot(angles, values, color='blue', linewidth=2)<\/p>\n
\u6dfb\u52a0\u6807\u7b7e<\/strong><\/h2>\n
ax.set_yticklabels([])<\/p>\n
ax.set_xticks(angles[:-1])<\/p>\n
ax.set_xticklabels(labels)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u8bf4\u660e\uff1a<\/strong><\/p>\n<\/p>\n
    \n
  • \u5b9a\u4e49\u8f74\u6570<\/strong>\uff1a\u8fd9\u91cc\u6211\u4eec\u5b9a\u4e49\u4e86\u516d\u4e2a\u8f74\uff0c\u5206\u522b\u5bf9\u5e94\u6807\u7b7e'A', 'B', 'C', 'D', 'E', 'F'\u3002<\/li>\n
  • \u521b\u5efa\u6570\u636e<\/strong>\uff1a\u6570\u636evalues<\/code>\u8868\u793a\u6bcf\u4e2a\u8f74\u4e0a\u7684\u503c\uff0c\u6700\u540e\u901a\u8fc7values += values[:1]<\/code>\u4f7f\u56fe\u5f62\u95ed\u5408\u3002<\/li>\n
  • \u8ba1\u7b97\u89d2\u5ea6<\/strong>\uff1a\u6bcf\u4e2a\u8f74\u7684\u89d2\u5ea6\u901a\u8fc7np.linspace<\/code>\u8ba1\u7b97\uff0c\u4f7f\u5404\u70b9\u5747\u5300\u5206\u5e03\u5728\u5706\u5468\u4e0a\u3002<\/li>\n
  • \u521b\u5efa\u7ed8\u56fe<\/strong>\uff1a\u4f7f\u7528subplot_kw=dict(polar=True)<\/code>\u521b\u5efa\u4e00\u4e2a\u6781\u5750\u6807\u5b50\u56fe\u3002<\/li>\n
  • \u7ed8\u5236\u8718\u86db\u7f51<\/strong>\uff1a\u901a\u8fc7ax.fill<\/code>\u548cax.plot<\/code>\u5206\u522b\u586b\u5145\u548c\u7ed8\u5236\u8718\u86db\u7f51\u7684\u533a\u57df\u3002<\/li>\n
  • \u6dfb\u52a0\u6807\u7b7e<\/strong>\uff1a\u901a\u8fc7set_xticks<\/code>\u548cset_xticklabels<\/code>\u6dfb\u52a0\u6807\u7b7e\u3002<\/li>\n<\/ul>\n
    \u4e09\u3001\u6269\u5c55\u8718\u86db\u7f51\u56fe<\/p>\n<\/p>\n
    \u4f60\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55\u8718\u86db\u7f51\u56fe\uff0c\u4f8b\u5982\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u96c6\u3001\u8c03\u6574\u56fe\u5f62\u6837\u5f0f\u3001\u6dfb\u52a0\u6807\u9898\u548c\u6ce8\u91ca\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u66f4\u590d\u6742\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u96c6\u5e76\u81ea\u5b9a\u4e49\u6837\u5f0f\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    import numpy as np<\/p>\n
    \u5b9a\u4e49\u8718\u86db\u7f51\u7684\u8f74\u6570<\/strong><\/h2>\n
    labels = np.array(['A', 'B', 'C', 'D', 'E', 'F'])<\/p>\n
    num_vars = len(labels)<\/p>\n
    \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
    values1 = [4, 3, 2, 5, 4, 3]<\/p>\n
    values2 = [3, 4, 5, 2, 3, 4]<\/p>\n
    values1 += values1[:1]  # \u4f7f\u56fe\u5f62\u95ed\u5408<\/p>\n
    values2 += values2[:1]  # \u4f7f\u56fe\u5f62\u95ed\u5408<\/p>\n
    \u8ba1\u7b97\u89d2\u5ea6<\/strong><\/h2>\n
    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()<\/p>\n
    angles += angles[:1]<\/p>\n
    \u521b\u5efa\u7ed8\u56fe<\/strong><\/h2>\n
    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))<\/p>\n
    \u7ed8\u5236\u7b2c\u4e00\u4e2a\u6570\u636e\u96c6<\/strong><\/h2>\n
    ax.fill(angles, values1, color='blue', alpha=0.25)<\/p>\n
    ax.plot(angles, values1, color='blue', linewidth=2, label='Group 1')<\/p>\n
    \u7ed8\u5236\u7b2c\u4e8c\u4e2a\u6570\u636e\u96c6<\/strong><\/h2>\n
    ax.fill(angles, values2, color='red', alpha=0.25)<\/p>\n
    ax.plot(angles, values2, color='red', linewidth=2, label='Group 2')<\/p>\n
    \u6dfb\u52a0\u6807\u7b7e<\/strong><\/h2>\n
    ax.set_yticklabels([])<\/p>\n
    ax.set_xticks(angles[:-1])<\/p>\n
    ax.set_xticklabels(labels)<\/p>\n
    \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
    plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))<\/p>\n
    \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
    plt.title('Spider Web Plot Example')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u603b\u7ed3\uff1a<\/strong><\/p>\n<\/p>\n
      \n
    • \u901a\u8fc7\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u96c6<\/strong>\uff0c\u53ef\u4ee5\u6bd4\u8f83\u4e0d\u540c\u7ec4\u7684\u6570\u636e\u3002<\/li>\n
    • \u81ea\u5b9a\u4e49\u6837\u5f0f<\/strong>\uff0c\u5982\u989c\u8272\u3001\u7ebf\u6761\u5bbd\u5ea6\u548c\u900f\u660e\u5ea6\u3002<\/li>\n
    • \u6dfb\u52a0\u56fe\u4f8b\u548c\u6807\u9898<\/strong>\uff0c\u4f7f\u56fe\u5f62\u66f4\u5177\u4fe1\u606f\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/li>\n<\/ul>\n
      \u56db\u3001\u4f7f\u7528NetworkX\u7ed8\u5236\u590d\u6742\u8718\u86db\u7f51<\/p>\n<\/p>\n
      \u5982\u679c\u4f60\u9700\u8981\u7ed8\u5236\u66f4\u590d\u6742\u7684\u8718\u86db\u7f51\u56fe\uff08\u5982\u7f51\u7edc\u56fe\uff09\uff0c\u53ef\u4ee5\u4f7f\u7528NetworkX\u5e93\u3002NetworkX\u4e13\u6ce8\u4e8e\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\uff0c\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u7f51\u7edc\u7ed3\u6784\u3002<\/p>\n<\/p>\n
      \u5b89\u88c5NetworkX\u5e93\uff1a<\/p>\n<\/p>\n
      pip install networkx<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528NetworkX\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u8718\u86db\u7f51\uff1a<\/p>\n<\/p>\n
      import networkx as nx<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u7a7a\u7684\u65e0\u5411\u56fe<\/strong><\/h2>\n
      G = nx.Graph()<\/p>\n
      \u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n
      nodes = range(1, 7)<\/p>\n
      G.add_nodes_from(nodes)<\/p>\n
      \u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
      edges = [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 1), (1, 4), (2, 5), (3, 6)]<\/p>\n
      G.add_edges_from(edges)<\/p>\n
      \u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
      pos = nx.circular_layout(G)  # \u8bbe\u7f6e\u5e03\u5c40\u4e3a\u5706\u5f62<\/p>\n
      nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=500, edge_color='gray')<\/p>\n
      plt.title('NetworkX Spider Web Plot')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8be6\u7ec6\u8bf4\u660e\uff1a<\/strong><\/p>\n<\/p>\n
        \n
      • \u521b\u5efa\u56fe<\/strong>\uff1a\u4f7f\u7528nx.Graph()<\/code>\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684\u65e0\u5411\u56fe\u3002<\/li>\n
      • \u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/strong>\uff1a\u901a\u8fc7G.add_nodes_from<\/code>\u548cG.add_edges_from<\/code>\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u3002<\/li>\n
      • \u8bbe\u7f6e\u5e03\u5c40<\/strong>\uff1anx.circular_layout<\/code>\u5c06\u8282\u70b9\u6309\u5706\u5f62\u5e03\u5c40\u3002<\/li>\n
      • \u7ed8\u5236\u56fe\u5f62<\/strong>\uff1a\u4f7f\u7528nx.draw<\/code>\u7ed8\u5236\u56fe\u5f62\uff0c\u5e76\u901a\u8fc7\u53c2\u6570\u81ea\u5b9a\u4e49\u8282\u70b9\u548c\u8fb9\u7684\u6837\u5f0f\u3002<\/li>\n<\/ul>\n
        \u4e94\u3001\u4f7f\u7528Plotly\u521b\u5efa\u4ea4\u4e92\u5f0f\u8718\u86db\u7f51\u56fe<\/p>\n<\/p>\n
        \u5982\u679c\u4f60\u9700\u8981\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u53ef\u4ee5\u4f7f\u7528Plotly\u5e93\u3002Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \u5b89\u88c5Plotly\u5e93\uff1a<\/p>\n<\/p>\n
        pip install plotly<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Plotly\u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u8718\u86db\u7f51\u56fe\uff1a<\/p>\n<\/p>\n
        import plotly.graph_objects as go<\/p>\n
        \u5b9a\u4e49\u8f74\u6570<\/strong><\/h2>\n
        categories = ['A', 'B', 'C', 'D', 'E', 'F']<\/p>\n
        \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
        values1 = [4, 3, 2, 5, 4, 3]<\/p>\n
        values2 = [3, 4, 5, 2, 3, 4]<\/p>\n
        \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
        fig = go.Figure()<\/p>\n
        \u6dfb\u52a0\u7b2c\u4e00\u4e2a\u6570\u636e\u96c6<\/strong><\/h2>\n
        fig.add_trace(go.Scatterpolar(<\/p>\n
            r=values1,<\/p>\n
            theta=categories,<\/p>\n
            fill='toself',<\/p>\n
            name='Group 1'<\/p>\n
        ))<\/p>\n
        \u6dfb\u52a0\u7b2c\u4e8c\u4e2a\u6570\u636e\u96c6<\/strong><\/h2>\n
        fig.add_trace(go.Scatterpolar(<\/p>\n
            r=values2,<\/p>\n
            theta=categories,<\/p>\n
            fill='toself',<\/p>\n
            name='Group 2'<\/p>\n
        ))<\/p>\n
        \u66f4\u65b0\u5e03\u5c40<\/strong><\/h2>\n
        fig.update_layout(<\/p>\n
            polar=dict(<\/p>\n
                radialaxis=dict(visible=True)<\/p>\n
            ),<\/p>\n
            showlegend=True,<\/p>\n
            title='Interactive Spider Web Plot Example'<\/p>\n
        )<\/p>\n
        fig.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u603b\u7ed3\uff1a<\/strong><\/p>\n<\/p>\n
          \n
        • Plotly<\/strong>\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u7528\u6237\u53ef\u4ee5\u653e\u5927\u3001\u7f29\u5c0f\u548c\u60ac\u505c\u67e5\u770b\u8be6\u7ec6\u4fe1\u606f\u3002<\/li>\n
        • \u901a\u8fc7go.Figure<\/code><\/strong>\u548cgo.Scatterpolar<\/code>\u521b\u5efa\u6781\u5750\u6807\u56fe\uff0c\u5e76\u6dfb\u52a0\u6570\u636e\u96c6\u3002<\/li>\n
        • \u66f4\u65b0\u5e03\u5c40<\/strong>\uff0c\u5982\u663e\u793a\u56fe\u4f8b\u548c\u6807\u9898\u3002<\/li>\n<\/ul>\n
          \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
          \u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u3001NetworkX\u548cPlotly\u5e93\u7ed8\u5236\u8718\u86db\u7f51\u56fe\u3002Matplotlib\u9002\u5408\u9759\u6001\u56fe\u5f62\uff0cNetworkX\u9002\u5408\u590d\u6742\u7684\u7f51\u7edc\u56fe\uff0c\u800cPlotly\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u548c\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n
          \u65e0\u8bba\u4f60\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u7814\u7a76\u4eba\u5458\u8fd8\u662f\u5de5\u7a0b\u5e08\uff0c\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\u90fd\u80fd\u63d0\u5347\u4f60\u7684\u6570\u636e\u53ef\u89c6\u5316\u80fd\u529b\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u5e76\u6fc0\u53d1\u4f60\u5728\u6570\u636e\u53ef\u89c6\u5316\u9886\u57df\u7684\u66f4\u591a\u521b\u610f\u548c\u63a2\u7d22\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           1. \u5728Python\u4e2d\u7ed8\u5236\u8718\u86db\u7f51\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
          \u8981\u5728Python\u4e2d\u7ed8\u5236\u8718\u86db\u7f51\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u548cNumPy\u3002Matplotlib\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u800cNumPy\u5219\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u6570\u5b66\u8ba1\u7b97\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u6570\u636e\u548c\u5750\u6807\u65f6\u3002\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86\u8fd9\u4e24\u4e2a\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n
          2. \u5982\u4f55\u8bbe\u7f6e\u8718\u86db\u7f51\u7684\u5c3a\u5bf8\u548c\u6837\u5f0f\uff1f<\/strong>
          \u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u8718\u86db\u7f51\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u5f62\u7684\u5927\u5c0f\u548c\u6837\u5f0f\u6765\u6ee1\u8db3\u9700\u6c42\u3002\u4f7f\u7528plt.figure(figsize=(width, height))<\/code>\u53ef\u4ee5\u8bbe\u7f6e\u56fe\u5f62\u7684\u5927\u5c0f\u3002\u6837\u5f0f\u65b9\u9762\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u7ebf\u6761\u7684\u989c\u8272\u3001\u5bbd\u5ea6\u548c\u7c7b\u578b\u6765\u6539\u53d8\u8718\u86db\u7f51\u7684\u5916\u89c2\uff0c\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u4e2d\u7684\u53c2\u6570\u8fdb\u884c\u8bbe\u7f6e\u3002<\/p>\n
          3. \u5982\u4f55\u5728\u8718\u86db\u7f51\u4e2d\u6dfb\u52a0\u6570\u636e\u5e76\u8fdb\u884c\u53ef\u89c6\u5316\uff1f<\/strong>
          \u8981\u5728\u8718\u86db\u7f51\u4e2d\u6dfb\u52a0\u6570\u636e\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u70b9\u8f6c\u6362\u4e3a\u6781\u5750\u6807\u5f62\u5f0f\u3002\u901a\u5e38\u4f7f\u7528numpy<\/code>\u5e93\u7684linspace<\/code>\u51fd\u6570\u751f\u6210\u6570\u636e\u70b9\u7684\u89d2\u5ea6\uff0c\u7136\u540e\u4f7f\u7528plt.polar<\/code>\u6216ax.plot<\/code>\u6765\u7ed8\u5236\u8fd9\u4e9b\u6570\u636e\u3002\u786e\u4fdd\u6570\u636e\u7684\u7ef4\u5ea6\u4e0e\u8718\u86db\u7f51\u7684\u8f74\u6570\u91cf\u76f8\u5339\u914d\uff0c\u4ee5\u4fbf\u80fd\u591f\u6b63\u786e\u663e\u793a\u5404\u4e2a\u6570\u636e\u70b9\u5728\u8718\u86db\u7f51\u4e2d\u7684\u4f4d\u7f6e\u548c\u5173\u7cfb\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u91cc\u753b\u8718\u86db\u7f51\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001NetworkX\u3001Plotly\u7b49\u5e93\u6765\u5b9e\u73b0\u3002Matpl […]","protected":false},"author":3,"featured_media":1100941,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100933"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1100933"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100933\/revisions"}],"predecessor-version":[{"id":1100944,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100933\/revisions\/1100944"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1100941"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1100933"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1100933"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1100933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}