{"id":1007244,"date":"2024-12-27T10:51:42","date_gmt":"2024-12-27T02:51:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1007244.html"},"modified":"2024-12-27T10:51:45","modified_gmt":"2024-12-27T02:51:45","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e7%94%bb%e5%9b%be%e5%83%8f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1007244.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u753b\u56fe\u50cf"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u753b\u56fe\u50cf\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
\u5229\u7528Python\u753b\u56fe\u50cf\u53ef\u4ee5\u4f7f\u7528matplotlib\u3001seaborn\u3001plotly\u7b49\u5e93\uff0c\u521b\u5efa\u4e8c\u7ef4\u56fe\u8868\u3001\u5904\u7406\u6570\u636e\u53ef\u89c6\u5316\u3001\u8fdb\u884c\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u3002<\/strong>\u5176\u4e2d\uff0cmatplotlib\u662f\u6700\u5e38\u7528\u7684Python\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684API\u6765\u521b\u5efa\u5404\u79cd\u9759\u6001\u3001\u52a8\u753b\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u901a\u8fc7matplotlib\uff0c\u7528\u6237\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u8fdb\u884c\u81ea\u5b9a\u4e49\u8bbe\u7f6e\uff0c\u5982\u5750\u6807\u8f74\u6807\u7b7e\u3001\u6807\u9898\u3001\u56fe\u4f8b\u7b49\u3002seaborn\u57fa\u4e8ematplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u7b80\u5316\u4e86\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u5f62\u7ed8\u5236\u3002plotly\u5219\u63d0\u4f9b\u4e86\u9ad8\u7ea7\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\u529f\u80fd\uff0c\u9002\u5408\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u53ef\u89c6\u5316\u9879\u76ee\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u5e93\u8fdb\u884c\u56fe\u50cf\u7ed8\u5236\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u7684\u4f7f\u7528<\/p>\n<\/p>\n

matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u4f7f\u7528\u8d77\u6765\u975e\u5e38\u7075\u6d3b\u3002\u5b83\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u56e0\u6b64\u9002\u5408\u4e8e\u9700\u8981\u7ec6\u7c92\u5ea6\u63a7\u5236\u7684\u56fe\u50cf\u7ed8\u5236\u3002<\/p>\n<\/p>\n

1.1\u3001\u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/p>\n<\/p>\n

\u8981\u4f7f\u7528matplotlib\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Line Plot')<\/p>\n
plt.xlabel('x-axis')<\/p>\n
plt.ylabel('y-axis')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u56fe\u5f62\u81ea\u5b9a\u4e49<\/p>\n<\/p>\n
matplotlib\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u4f8b\u5982\u7ebf\u6761\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u8bb0\u7b49\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff1a<\/p>\n<\/p>\n
    \n
  • \u7ebf\u6761\u6837\u5f0f<\/strong>\uff1a\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570linestyle<\/code>\u6765\u8bbe\u7f6e\uff0c\u5982'--'<\/code>\u8868\u793a\u865a\u7ebf\uff0c'-.'<\/code>\u8868\u793a\u70b9\u5212\u7ebf\u3002<\/li>\n
  • \u989c\u8272<\/strong>\uff1a\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570color<\/code>\u6765\u8bbe\u7f6e\u7ebf\u6761\u989c\u8272\uff0c\u4f8b\u5982'red'<\/code>\u3001'blue'<\/code>\u7b49\u3002<\/li>\n
  • \u6807\u8bb0<\/strong>\uff1a\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570marker<\/code>\u6765\u8bbe\u7f6e\u6570\u636e\u70b9\u7684\u6807\u8bb0\u6837\u5f0f\uff0c\u5982'o'<\/code>\u8868\u793a\u5706\u5708\uff0c's'<\/code>\u8868\u793a\u65b9\u5757\u3002<\/li>\n<\/ul>\n
    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u81ea\u5b9a\u4e49\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    plt.plot(x, y, linestyle='--', color='r', marker='o')<\/p>\n
    plt.title('Customized Line Plot')<\/p>\n
    plt.xlabel('x-axis')<\/p>\n
    plt.ylabel('y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3\u3001\u7ed8\u5236\u591a\u79cd\u56fe\u8868<\/p>\n<\/p>\n
    matplotlib\u4e0d\u4ec5\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u8fd8\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5982\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      \n
    • \u67f1\u72b6\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
      plt.bar(x, y, color='b')<\/p>\n
      plt.title('Bar Chart')<\/p>\n
      plt.xlabel('x-axis')<\/p>\n
      plt.ylabel('y-axis')<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      • \u6563\u70b9\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
        plt.scatter(x, y, color='g')<\/p>\n
        plt.title('Scatter Plot')<\/p>\n
        plt.xlabel('x-axis')<\/p>\n
        plt.ylabel('y-axis')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        • \u997c\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
          labels = ['A', 'B', 'C', 'D', 'E']<\/p>\n
          plt.pie(y, labels=labels, autopct='%1.1f%%')<\/p>\n
          plt.title('Pie Chart')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e8c\u3001SEABORN\u5e93\u7684\u4f7f\u7528<\/p>\n<\/p>\n
          seaborn\u662f\u57fa\u4e8ematplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u65e8\u5728\u4f7f\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u5f62\u66f4\u5bb9\u6613\u7ed8\u5236\u3002\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ed8\u8ba4\u7684\u56fe\u5f62\u6837\u5f0f\u548c\u989c\u8272\u8c03\u8272\u677f\uff0c\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u7f8e\u89c2\u3002<\/p>\n<\/p>\n
          2.1\u3001\u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/p>\n<\/p>\n
          \u540c\u6837\uff0c\u4f7f\u7528pip\u547d\u4ee4\u5b89\u88c5seaborn\uff1a<\/p>\n<\/p>\n
          pip install seaborn<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u5173\u7cfb\u56fe\uff1a<\/p>\n<\/p>\n
          import seaborn as sns<\/p>\n
          import numpy as np<\/p>\n
          \u6570\u636e<\/strong><\/h2>\n
          x = np.random.rand(100)<\/p>\n
          y = np.random.rand(100)<\/p>\n
          \u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
          sns.scatterplot(x=x, y=y)<\/p>\n
          \u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          2.2\u3001\u7ed8\u5236\u7edf\u8ba1\u56fe\u5f62<\/p>\n<\/p>\n
          seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\u7684\u56fe\u5f62\uff0c\u5982\u7bb1\u7ebf\u56fe\u3001\u70ed\u529b\u56fe\u3001\u56de\u5f52\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
            \n
          • \u7bb1\u7ebf\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
            data = np.random.normal(size=(20, 6)) + np.arange(6) \/ 2<\/p>\n
            sns.boxplot(data=data)<\/p>\n
            plt.title('Box Plot')<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            • \u70ed\u529b\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
              data = np.random.rand(10, 12)<\/p>\n
              sns.heatmap(data)<\/p>\n
              plt.title('Heatmap')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              • \u56de\u5f52\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n
                sns.regplot(x=x, y=y)<\/p>\n
                plt.title('Regression Plot')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                2.3\u3001\u56fe\u5f62\u98ce\u683c\u4e0e\u8c03\u8272\u677f<\/p>\n<\/p>\n
                seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ed8\u8ba4\u7684\u56fe\u5f62\u98ce\u683c\u548c\u989c\u8272\u8c03\u8272\u677f\uff0c\u53ef\u4ee5\u8f7b\u677e\u66f4\u6539\u56fe\u5f62\u7684\u5916\u89c2\u3002\u53ef\u4ee5\u4f7f\u7528set_style()<\/code>\u548cset_palette()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\uff1a<\/p>\n<\/p>\n
                sns.set_style('whitegrid')<\/p>\n
                sns.set_palette('pastel')<\/p>\n
                sns.scatterplot(x=x, y=y)<\/p>\n
                plt.title('Styled Scatter Plot')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4e09\u3001PLOTLY\u5e93\u7684\u4f7f\u7528<\/p>\n<\/p>\n
                plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u7684\u5e93\uff0c\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u4e14\u53ef\u4ee5\u8f7b\u677e\u5730\u5d4c\u5165\u5230\u7f51\u9875\u4e2d\u3002\u5b83\u7279\u522b\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u6570\u636e\u53ef\u89c6\u5316\u9879\u76ee\u3002<\/p>\n<\/p>\n
                3.1\u3001\u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/p>\n<\/p>\n
                \u540c\u6837\u4f7f\u7528pip\u547d\u4ee4\u5b89\u88c5plotly\uff1a<\/p>\n<\/p>\n
                pip install plotly<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4ee5\u4e0b\u662f\u4f7f\u7528plotly\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
                import plotly.graph_objects as go<\/p>\n
                \u6570\u636e<\/strong><\/h2>\n
                x = [1, 2, 3, 4, 5]<\/p>\n
                y = [2, 3, 5, 7, 11]<\/p>\n
                \u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
                fig = go.Figure()<\/p>\n
                \u6dfb\u52a0\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                fig.add_trace(go.Scatter(x=x, y=y, mode='lines+markers'))<\/p>\n
                \u8bbe\u7f6e\u6807\u9898<\/strong><\/h2>\n
                fig.update_layout(title='Interactive Line Plot')<\/p>\n
                \u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                3.2\u3001\u4ea4\u4e92\u5f0f\u56fe\u8868<\/p>\n<\/p>\n
                plotly\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5176\u4ea4\u4e92\u5f0f\u529f\u80fd\u3002\u7528\u6237\u53ef\u4ee5\u5728\u56fe\u8868\u4e2d\u8fdb\u884c\u7f29\u653e\u3001\u5e73\u79fb\u3001\u60ac\u505c\u67e5\u770b\u6570\u636e\u7b49\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5e26\u6709\u4ea4\u4e92\u529f\u80fd\u7684\u6563\u70b9\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
                fig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker=dict(size=12, color='rgba(152, 0, 0, .8)', line=dict(width=2, color='DarkSlateGrey'))))<\/p>\n
                fig.update_layout(title='Interactive Scatter Plot')<\/p>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                3.3\u3001\u7ed8\u5236\u590d\u6742\u56fe\u8868<\/p>\n<\/p>\n
                plotly\u652f\u6301\u8bb8\u591a\u590d\u6742\u7684\u56fe\u8868\u7c7b\u578b\uff0c\u59823D\u56fe\u8868\u3001\u5b50\u56fe\u3001\u5730\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u4e00\u4e2a3D\u6563\u70b9\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
                import plotly.express as px<\/p>\n
                import pandas as pd<\/p>\n
                \u6570\u636e<\/strong><\/h2>\n
                df = pd.DataFrame({<\/p>\n
                    'x': [1, 2, 3, 4, 5],<\/p>\n
                    'y': [2, 3, 5, 7, 11],<\/p>\n
                    'z': [5, 4, 6, 8, 9]<\/p>\n
                })<\/p>\n
                3D\u6563\u70b9\u56fe<\/strong><\/h2>\n
                fig = px.scatter_3d(df, x='x', y='y', z='z', color='z', size='z')<\/p>\n
                fig.update_layout(title='3D Scatter Plot')<\/p>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u6570\u636e\u53ef\u89c6\u5316\u7684\u6700\u4f73\u5b9e\u8df5<\/p>\n<\/p>\n
                \u65e0\u8bba\u4f7f\u7528\u54ea\u79cd\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u90fd\u9700\u8981\u9075\u5faa\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\uff0c\u4ee5\u786e\u4fdd\u56fe\u8868\u7684\u6709\u6548\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
                4.1\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b<\/p>\n<\/p>\n
                \u6839\u636e\u6570\u636e\u7684\u7279\u6027\u548c\u5206\u6790\u76ee\u7684\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u6298\u7ebf\u56fe\u9002\u5408\u4e8e\u663e\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\uff0c\u6563\u70b9\u56fe\u9002\u5408\u4e8e\u89c2\u5bdf\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u67f1\u72b6\u56fe\u9002\u5408\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
                4.2\u3001\u786e\u4fdd\u56fe\u8868\u7684\u53ef\u8bfb\u6027<\/p>\n<\/p>\n
                \u56fe\u8868\u5e94\u5f53\u6613\u4e8e\u9605\u8bfb\u548c\u7406\u89e3\u3002\u5e94\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
                  \n
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