{"id":962976,"date":"2024-12-27T04:13:25","date_gmt":"2024-12-26T20:13:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/962976.html"},"modified":"2024-12-27T04:13:27","modified_gmt":"2024-12-26T20:13:27","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e6%b0%94%e6%b3%a1%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/962976.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u6c14\u6ce1\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u7ed8\u5236\u6c14\u6ce1\u56fe\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
Python\u7ed8\u5236\u6c14\u6ce1\u56fe\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u6216Plotly\u5e93\u6765\u5b9e\u73b0<\/strong>\u3002\u8fd9\u4e9b\u5e93\u5404\u6709\u7279\u70b9\uff0c\u5176\u4e2d\uff0cMatplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u7ed8\u5236\uff1bSeaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u56fe\u5f62\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff1bPlotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684\u56fe\u5f62\u5e93\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u79cd\u5e93\u7ed8\u5236\u6c14\u6ce1\u56fe\uff0c\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u548c\u4f7f\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7ed8\u5236\u6c14\u6ce1\u56fe<\/p>\n<\/p>\n

Matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5176\u7075\u6d3b\u6027\u548c\u53ef\u5b9a\u5236\u6027\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u53ef\u89c6\u5316\u7684\u70ed\u95e8\u9009\u62e9\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u6c14\u6ce1\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\u521b\u5efa\u6570\u636e\u3001\u8c03\u7528scatter<\/code>\u51fd\u6570\u5e76\u8bbe\u7f6e\u6c14\u6ce1\u7684\u5927\u5c0f\u548c\u989c\u8272\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u521b\u5efa\u6570\u636e<\/strong>
    \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u6570\u636e\u3002\u6c14\u6ce1\u56fe\u901a\u5e38\u9700\u8981\u4e09\u4e2a\u7ef4\u5ea6\u7684\u6570\u636e\uff1ax\u8f74\u3001y\u8f74\u548c\u6c14\u6ce1\u5927\u5c0f\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5217\u8868\u6216NumPy\u6570\u7ec4\u6765\u5b58\u50a8\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    x = np.random.rand(50)<\/p>\n
    y = np.random.rand(50)<\/p>\n
    sizes = np.random.rand(50) * 1000<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u7ed8\u5236\u6c14\u6ce1\u56fe<\/strong>
    \u4f7f\u7528Matplotlib\u7684scatter<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u6c14\u6ce1\u56fe\u3002scatter<\/code>\u51fd\u6570\u7684\u57fa\u672c\u53c2\u6570\u5305\u62ecx\u8f74\u6570\u636e\u3001y\u8f74\u6570\u636e\u548c\u6c14\u6ce1\u5927\u5c0f\u3002<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    plt.scatter(x, y, s=sizes, alpha=0.5)<\/p>\n
    plt.xlabel('X Label')<\/p>\n
    plt.ylabel('Y Label')<\/p>\n
    plt.title('Bubble Chart with Matplotlib')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  3. \n
    \u8bbe\u7f6e\u6c14\u6ce1\u989c\u8272\u548c\u900f\u660e\u5ea6<\/strong>
    scatter<\/code>\u51fd\u6570\u8fd8\u652f\u6301\u8bbe\u7f6e\u6c14\u6ce1\u7684\u989c\u8272\u548c\u900f\u660e\u5ea6\u3002\u53ef\u4ee5\u6839\u636e\u9700\u8981\u81ea\u5b9a\u4e49\u989c\u8272\uff0c\u4f7f\u7528c<\/code>\u53c2\u6570\u8bbe\u7f6e\u6bcf\u4e2a\u6c14\u6ce1\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n
    colors = np.random.rand(50)<\/p>\n
    plt.scatter(x, y, s=sizes, c=colors, alpha=0.5, cmap='viridis')<\/p>\n
    plt.colorbar()<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001SEABORN\u7ed8\u5236\u6c14\u6ce1\u56fe<\/p>\n<\/p>\n
    Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u867d\u7136Seaborn\u6ca1\u6709\u4e13\u95e8\u7684\u6c14\u6ce1\u56fe\u51fd\u6570\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u6563\u70b9\u56fe\u7684\u53c2\u6570\u6765\u5b9e\u73b0\u6c14\u6ce1\u56fe\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u521b\u5efa\u6570\u636e\u96c6<\/strong>
      \u4f7f\u7528Pandas DataFrame\u6765\u7ec4\u7ec7\u6570\u636e\u96c6\u662f\u4e00\u4e2a\u597d\u4e60\u60ef\uff0c\u7279\u522b\u662f\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u3002DataFrame\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      data = {<\/p>\n
          'x': np.random.rand(50),<\/p>\n
          'y': np.random.rand(50),<\/p>\n
          'size': np.random.rand(50) * 1000,<\/p>\n
          'color': np.random.rand(50)<\/p>\n
      }<\/p>\n
      df = pd.DataFrame(data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u4f7f\u7528Seaborn\u7ed8\u5236\u6c14\u6ce1\u56fe<\/strong>
      Seaborn\u7684scatterplot<\/code>\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u6c14\u6ce1\u56fe\uff0c\u8bbe\u7f6esize<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u6c14\u6ce1\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
      import seaborn as sns<\/p>\n
      sns.scatterplot(data=df, x='x', y='y', size='size', hue='color', sizes=(20, 200), palette='viridis', alpha=0.5)<\/p>\n
      plt.title('Bubble Chart with Seaborn')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u81ea\u5b9a\u4e49\u56fe\u5f62\u6837\u5f0f<\/strong>
      Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u6837\u5f0f\u9009\u9879\uff0c\u53ef\u4ee5\u901a\u8fc7set_style<\/code>\u548cset_context<\/code>\u7b49\u51fd\u6570\u8fdb\u884c\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n
      sns.set_style('whitegrid')<\/p>\n
      sns.set_context('talk')<\/p>\n
      sns.scatterplot(data=df, x='x', y='y', size='size', hue='color', sizes=(20, 200), palette='coolwarm', alpha=0.6)<\/p>\n
      plt.title('Customized Bubble Chart with Seaborn')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001PLOTLY\u7ed8\u5236\u6c14\u6ce1\u56fe<\/p>\n<\/p>\n
      Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u56fe\u5f62\u5e93\uff0c\u975e\u5e38\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u5b83\u7684scatter<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u4ea4\u4e92\u5f0f\u6c14\u6ce1\u56fe\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u51c6\u5907\u6570\u636e<\/strong>
        \u548c\u524d\u9762\u4e00\u6837\uff0c\u9700\u8981\u51c6\u5907x\u8f74\u3001y\u8f74\u548c\u6c14\u6ce1\u5927\u5c0f\u7684\u6570\u636e\u3002Plotly\u7684scatter<\/code>\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n
        import plotly.express as px<\/p>\n
        df = pd.DataFrame({<\/p>\n
            'x': np.random.rand(50),<\/p>\n
            'y': np.random.rand(50),<\/p>\n
            'size': np.random.rand(50) * 1000,<\/p>\n
            'color': np.random.rand(50)<\/p>\n
        })<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u7ed8\u5236\u4ea4\u4e92\u5f0f\u6c14\u6ce1\u56fe<\/strong>
        \u4f7f\u7528Plotly\u7684scatter<\/code>\u51fd\u6570\u521b\u5efa\u6c14\u6ce1\u56fe\uff0c\u5e76\u8bbe\u7f6e\u53c2\u6570size<\/code>\u548ccolor<\/code>\u3002<\/p>\n<\/p>\n
        fig = px.scatter(df, x='x', y='y', size='size', color='color', hover_name='size', size_max=60)<\/p>\n
        fig.update_layout(title='Interactive Bubble Chart with Plotly')<\/p>\n
        fig.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u6dfb\u52a0\u4ea4\u4e92\u529f\u80fd<\/strong>
        Plotly\u7684\u4ea4\u4e92\u529f\u80fd\u53ef\u4ee5\u901a\u8fc7hover_name<\/code>\u548chover_data<\/code>\u7b49\u53c2\u6570\u8fdb\u884c\u5b9a\u5236\uff0c\u4f7f\u7528\u6237\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u67e5\u770b\u8be6\u7ec6\u4fe1\u606f\u3002<\/p>\n<\/p>\n
        fig = px.scatter(df, x='x', y='y', size='size', color='color', hover_name='size', size_max=60, hover_data=['x', 'y'])<\/p>\n
        fig.update_layout(title='Enhanced Interactive Bubble Chart with Plotly')<\/p>\n
        fig.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u6c14\u6ce1\u56fe\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
        \u6c14\u6ce1\u56fe\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u5206\u6790\u591a\u7ef4\u6570\u636e\u65f6\u3002\u4ee5\u4e0b\u662f\u6c14\u6ce1\u56fe\u7684\u4e00\u4e9b\u5e38\u89c1\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
          \n
        1. \n
          \u5e02\u573a\u5206\u6790<\/strong>
          \u5728\u5e02\u573a\u5206\u6790\u4e2d\uff0c\u6c14\u6ce1\u56fe\u53ef\u4ee5\u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u4ea7\u54c1\u7684\u5e02\u573a\u4efd\u989d\u3001\u589e\u957f\u7387\u548c\u5176\u4ed6\u91cd\u8981\u6307\u6807\u3002\u6bcf\u4e2a\u6c14\u6ce1\u4ee3\u8868\u4e00\u4e2a\u4ea7\u54c1\uff0c\u6c14\u6ce1\u7684\u5927\u5c0f\u548c\u989c\u8272\u53ef\u4ee5\u8868\u793a\u4e0d\u540c\u7684\u5e02\u573a\u53d8\u91cf\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u793e\u4f1a\u7ecf\u6d4e\u6570\u636e\u5206\u6790<\/strong>
          \u6c14\u6ce1\u56fe\u5728\u793e\u4f1a\u7ecf\u6d4e\u6570\u636e\u5206\u6790\u4e2d\u4e5f\u5f88\u5e38\u7528\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u7528\u6765\u5c55\u793a\u5404\u4e2a\u56fd\u5bb6\u7684GDP\u3001\u4eba\u5747\u6536\u5165\u548c\u4eba\u53e3\u6570\u91cf\u3002\u901a\u8fc7\u6c14\u6ce1\u7684\u5927\u5c0f\u548c\u989c\u8272\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u6bd4\u8f83\u4e0d\u540c\u56fd\u5bb6\u7684\u7ecf\u6d4e\u72b6\u51b5\u3002<\/p>\n<\/p>\n<\/li>\n
        3. \n
          \u79d1\u5b66\u7814\u7a76<\/strong>
          \u5728\u79d1\u5b66\u7814\u7a76\u4e2d\uff0c\u6c14\u6ce1\u56fe\u53ef\u4ee5\u7528\u4e8e\u5c55\u793a\u5b9e\u9a8c\u7ed3\u679c\u6216\u89c2\u6d4b\u6570\u636e\u3002\u4f8b\u5982\uff0c\u6c14\u6ce1\u56fe\u53ef\u4ee5\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u7684\u53cd\u5e94\u901f\u7387\u548c\u4ea7\u7269\u6570\u91cf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
          \u5728Python\u4e2d\u7ed8\u5236\u6c14\u6ce1\u56fe\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u4e0d\u540c\u7684\u5e93\u63d0\u4f9b\u4e86\u4e0d\u540c\u7684\u529f\u80fd\u548c\u7279\u6027\u3002Matplotlib\u9002\u5408\u9700\u8981\u9ad8\u5ea6\u81ea\u5b9a\u4e49\u7684\u9759\u6001\u56fe\u5f62\uff0cSeaborn\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684\u63a5\u53e3\u548c\u7f8e\u89c2\u7684\u6837\u5f0f\uff0c\u800cPlotly\u5219\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u5f62<\/strong>\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u548c\u5206\u6790\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u60a8\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u7ed8\u5236\u6c14\u6ce1\u56fe\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u6c14\u6ce1\u56fe\u9002\u5408\u7528\u6765\u5c55\u793a\u54ea\u4e9b\u7c7b\u578b\u7684\u6570\u636e\uff1f<\/strong>
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