{"id":1003944,"date":"2024-12-27T10:21:47","date_gmt":"2024-12-27T02:21:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1003944.html"},"modified":"2024-12-27T10:21:53","modified_gmt":"2024-12-27T02:21:53","slug":"python-%e6%95%a3%e7%82%b9%e5%9b%be%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e5%9b%be%e4%be%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1003944.html","title":{"rendered":"python \u6563\u70b9\u56fe\u5982\u4f55\u6dfb\u52a0\u56fe\u4f8b"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u7684\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684legend()\u51fd\u6570\u3001\u901a\u8fc7\u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6\u5206\u914d\u6807\u7b7e\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u6807\u8bb0\u548c\u989c\u8272\u8fdb\u884c\u533a\u5206\u3002<\/strong>\u5728\u8fd9\u4e09\u79cd\u65b9\u6cd5\u4e2d\uff0c\u5408\u7406\u8bbe\u7f6e\u6807\u7b7e\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\u3002\u901a\u8fc7\u5728\u7ed8\u56fe\u65f6\u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6\u5206\u914d\u72ec\u7279\u7684\u6807\u7b7e\uff0c\u53ef\u4ee5\u786e\u4fdd\u56fe\u4f8b\u51c6\u786e\u5730\u53cd\u6620\u51fa\u56fe\u4e2d\u6bcf\u4e2a\u6570\u636e\u96c6\u7684\u542b\u4e49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u5e76\u5b9a\u5236\u6563\u70b9\u56fe\u7684\u56fe\u4f8b\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u6982\u8ff0<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u56e0\u5176\u5f3a\u5927\u7684\u529f\u80fd\u548c\u7075\u6d3b\u6027\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u901a\u8fc7Matplotlib\uff0c\u7528\u6237\u53ef\u4ee5\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\uff0c\u5e76\u53ef\u4ee5\u5bf9\u56fe\u8868\u8fdb\u884c\u9ad8\u5ea6\u81ea\u5b9a\u4e49\u3002\u8981\u5728\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\uff0c\u9996\u5148\u9700\u8981\u7406\u89e3Matplotlib\u7684\u57fa\u672c\u7528\u6cd5\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5\u4e0e\u5bfc\u5165<\/li>\n<\/ol>\n

    Matplotlib\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff0c\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

    pip install matplotlib<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u57fa\u672c\u7ed8\u56fe<\/li>\n<\/ol>\n
      \u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u9996\u5148\u9700\u8981\u521b\u5efa\u4e00\u4e2aFigure\u5bf9\u8c61\uff0c\u7136\u540e\u5728\u5176\u4e0a\u6dfb\u52a0Axes\u5bf9\u8c61\u3002Figure\u662f\u6574\u4e2a\u7ed8\u56fe\u7684\u5bb9\u5668\uff0c\u800cAxes\u662f\u56fe\u5f62\u7684\u5177\u4f53\u533a\u57df\u3002<\/p>\n<\/p>\n
      fig, ax = plt.subplots()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u6dfb\u52a0\u56fe\u4f8b\u7684\u57fa\u672c\u65b9\u6cd5<\/p>\n<\/p>\n
      \u5728\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\u7684\u5173\u952e\u5728\u4e8e\u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6\u5206\u914d\u6807\u7b7e\uff0c\u7136\u540e\u4f7f\u7528legend()\u51fd\u6570\u663e\u793a\u8fd9\u4e9b\u6807\u7b7e\u3002<\/p>\n<\/p>\n
        \n
      1. \u4e3a\u6570\u636e\u96c6\u5206\u914d\u6807\u7b7e<\/li>\n<\/ol>\n
        \u5728\u7ed8\u5236\u6563\u70b9\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528label\u53c2\u6570\u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6\u5206\u914d\u4e00\u4e2a\u6807\u7b7e\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
        ax.scatter(x_values, y_values, label='Data Set 1')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4f7f\u7528legend()\u51fd\u6570<\/li>\n<\/ol>\n
          \u5728\u5206\u914d\u5b8c\u6807\u7b7e\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528legend()\u51fd\u6570\u6765\u663e\u793a\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
          ax.legend()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u793a\u4f8b\u4ee3\u7801<\/li>\n<\/ol>\n
            \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u5728\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
            import matplotlib.pyplot as plt<\/p>\n
            \u6570\u636e<\/strong><\/h2>\n
            x1 = [1, 2, 3, 4, 5]<\/p>\n
            y1 = [2, 3, 5, 7, 11]<\/p>\n
            x2 = [1, 2, 3, 4, 5]<\/p>\n
            y2 = [1, 4, 6, 8, 9]<\/p>\n
            \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
            fig, ax = plt.subplots()<\/p>\n
            ax.scatter(x1, y1, label='Prime Numbers', color='r')<\/p>\n
            ax.scatter(x2, y2, label='Random Numbers', color='b')<\/p>\n
            \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
            ax.legend()<\/p>\n
            \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u56fe\u4f8b\u7684\u9ad8\u7ea7\u5b9a\u5236<\/p>\n<\/p>\n
            Matplotlib\u63d0\u4f9b\u4e86\u591a\u79cd\u9009\u9879\u6765\u5b9a\u5236\u56fe\u4f8b\u7684\u5916\u89c2\uff0c\u5305\u62ec\u4f4d\u7f6e\u3001\u5b57\u4f53\u3001\u8fb9\u6846\u7b49\u3002<\/p>\n<\/p>\n
              \n
            1. \u56fe\u4f8b\u4f4d\u7f6e<\/li>\n<\/ol>\n
              \u53ef\u4ee5\u901a\u8fc7\u5728legend()\u51fd\u6570\u4e2d\u4f20\u9012loc\u53c2\u6570\u6765\u6307\u5b9a\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3002\u5e38\u7528\u7684\u4f4d\u7f6e\u6709\uff1a<\/p>\n<\/p>\n
                \n
              • 'upper right'<\/li>\n
              • 'upper left'<\/li>\n
              • 'lower left'<\/li>\n
              • 'lower right'<\/li>\n
              • 'right'<\/li>\n
              • 'center left'<\/li>\n
              • 'center right'<\/li>\n
              • 'lower center'<\/li>\n
              • 'upper center'<\/li>\n
              • 'center'<\/li>\n<\/ul>\n
                \u4f8b\u5982\uff0c\u5c06\u56fe\u4f8b\u653e\u5728\u5de6\u4e0a\u89d2\uff1a<\/p>\n<\/p>\n
                ax.legend(loc='upper left')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u56fe\u4f8b\u5b57\u4f53\u548c\u5927\u5c0f<\/li>\n<\/ol>\n
                  \u53ef\u4ee5\u4f7f\u7528prop\u53c2\u6570\u8bbe\u7f6e\u56fe\u4f8b\u7684\u5b57\u4f53\u548c\u5927\u5c0f\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
                  ax.legend(prop={'size': 10, 'family': 'serif'})<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u56fe\u4f8b\u8fb9\u6846<\/li>\n<\/ol>\n
                    \u901a\u8fc7\u8bbe\u7f6eframeon\u53c2\u6570\uff0c\u53ef\u4ee5\u63a7\u5236\u662f\u5426\u663e\u793a\u56fe\u4f8b\u7684\u8fb9\u6846\uff1a<\/p>\n<\/p>\n
                    ax.legend(frameon=False)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u56db\u3001\u7ed3\u5408NumPy\u548cPandas\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
                    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u901a\u5e38\u4ee5NumPy\u6570\u7ec4\u6216Pandas\u6570\u636e\u6846\u7684\u5f62\u5f0f\u5b58\u5728\u3002\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\u8fdb\u884c\u66f4\u590d\u6742\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u4f7f\u7528NumPy\u751f\u6210\u6570\u636e<\/li>\n<\/ol>\n
                      NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u751f\u6210\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u4f8b\u5982\uff0c\u751f\u6210\u4e24\u4e2a\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u96c6\uff1a<\/p>\n<\/p>\n
                      import numpy as np<\/p>\n
                      x = np.random.normal(0, 1, 100)<\/p>\n
                      y1 = np.random.normal(0, 1, 100)<\/p>\n
                      y2 = np.random.normal(1, 1, 100)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u6570\u636e\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u6dfb\u52a0\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
                      fig, ax = plt.subplots()<\/p>\n
                      ax.scatter(x, y1, label='Dataset 1', color='g', alpha=0.5)<\/p>\n
                      ax.scatter(x, y2, label='Dataset 2', color='m', alpha=0.5)<\/p>\n
                      ax.legend()<\/p>\n
                      plt.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u4f7f\u7528Pandas\u5904\u7406\u6570\u636e<\/li>\n<\/ol>\n
                        Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u5e93\uff0c\u901a\u5e38\u7528\u4e8e\u8bfb\u53d6\u548c\u5904\u7406CSV\u6587\u4ef6\u7b49\u6570\u636e\u6e90\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6\u6570\u636e\u5e76\u7ed8\u5236\u6563\u70b9\u56fe\uff1a<\/p>\n<\/p>\n
                        import pandas as pd<\/p>\n
                        \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5305\u542bx\u3001y1\u3001y2\u4e09\u5217\u6570\u636e<\/strong><\/h2>\n
                        data = pd.read_csv('data.csv')<\/p>\n
                        fig, ax = plt.subplots()<\/p>\n
                        ax.scatter(data['x'], data['y1'], label='Group 1', color='c')<\/p>\n
                        ax.scatter(data['x'], data['y2'], label='Group 2', color='y')<\/p>\n
                        ax.legend()<\/p>\n
                        plt.show()<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u4e94\u3001\u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u53ef\u89c6\u5316<\/p>\n<\/p>\n
                        Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u4e00\u4e2a\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u4fbf\u7684\u7ed8\u56fe\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u5b83\u7279\u522b\u9002\u5408\u7528\u4e8e\u7edf\u8ba1\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
                          \n
                        1. \u5b89\u88c5\u4e0e\u5bfc\u5165<\/li>\n<\/ol>\n
                          \u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Seaborn\uff1a<\/p>\n<\/p>\n
                          pip install seaborn<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
                          import seaborn as sns<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          1. \u4f7f\u7528Seaborn\u7ed8\u5236\u6563\u70b9\u56fe<\/li>\n<\/ol>\n
                            Seaborn\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u5e76\u81ea\u52a8\u5904\u7406\u56fe\u4f8b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Seaborn\u7684scatterplot()\u51fd\u6570\uff1a<\/p>\n<\/p>\n
                            import seaborn as sns<\/p>\n
                            \u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                            tips = sns.load_dataset('tips')<\/p>\n
                            \u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
                            sns.scatterplot(data=tips, x='total_bill', y='tip', hue='day', style='time')<\/p>\n
                            plt.show()<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cSeaborn\u81ea\u52a8\u4e3a\u4e0d\u540c\u7684\u201cday\u201d\u548c\u201ctime\u201d\u5206\u914d\u4e86\u4e0d\u540c\u7684\u989c\u8272\u548c\u5f62\u72b6\uff0c\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
                              \n
                            1. \u5b9a\u5236Seaborn\u56fe\u4f8b<\/li>\n<\/ol>\n
                              Seaborn\u5141\u8bb8\u7528\u6237\u901a\u8fc7legend()\u51fd\u6570\u5b9a\u5236\u56fe\u4f8b\u7684\u5916\u89c2\uff0c\u4e0eMatplotlib\u7c7b\u4f3c\uff1a<\/p>\n<\/p>\n
                              sns.scatterplot(data=tips, x='total_bill', y='tip', hue='day', style='time')<\/p>\n
                              plt.legend(loc='upper left', title='Legend')<\/p>\n
                              plt.show()<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u516d\u3001\u5728\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u4f7f\u7528Plotly<\/p>\n<\/p>\n
                              Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u652f\u6301\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\uff0c\u5305\u62ecPython\u3002\u5728\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\uff0cPlotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u4e3a\u6570\u636e\u53ef\u89c6\u5316\u589e\u6dfb\u66f4\u591a\u52a8\u6001\u5143\u7d20\u3002<\/p>\n<\/p>\n
                                \n
                              1. \u5b89\u88c5\u4e0e\u5bfc\u5165<\/li>\n<\/ol>\n
                                \u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Plotly\uff1a<\/p>\n<\/p>\n
                                pip install plotly<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                \u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
                                import plotly.express as px<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                  \n
                                1. \u4f7f\u7528Plotly\u7ed8\u5236\u6563\u70b9\u56fe<\/li>\n<\/ol>\n
                                  Plotly\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u7ed8\u5236\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe\uff0c\u5e76\u81ea\u52a8\u5904\u7406\u56fe\u4f8b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
                                  import plotly.express as px<\/p>\n
                                  \u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                                  df = px.data.iris()<\/p>\n
                                  \u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
                                  fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', symbol='species')<\/p>\n
                                  fig.show()<\/p>\n
                                  <\/code><\/pre>\n<\/p>\n
                                  \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cPlotly\u81ea\u52a8\u4e3a\u4e0d\u540c\u7684\u201cspecies\u201d\u5206\u914d\u4e86\u4e0d\u540c\u7684\u989c\u8272\u548c\u5f62\u72b6\uff0c\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
                                    \n
                                  1. \u5b9a\u5236Plotly\u56fe\u4f8b<\/li>\n<\/ol>\n
                                    Plotly\u5141\u8bb8\u7528\u6237\u901a\u8fc7update_layout()\u51fd\u6570\u5b9a\u5236\u56fe\u4f8b\u7684\u5916\u89c2\uff1a<\/p>\n<\/p>\n
                                    fig.update_layout(legend_title_text='Species')<\/p>\n
                                    fig.show()<\/p>\n
                                    <\/code><\/pre>\n<\/p>\n
                                    \u4e03\u3001\u603b\u7ed3\u4e0e\u6700\u4f73\u5b9e\u8df5<\/p>\n<\/p>\n
                                    \u5728Python\u4e2d\u4e3a\u6563\u70b9\u56fe\u6dfb\u52a0\u56fe\u4f8b\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u53ef\u89c6\u5316\u6280\u5de7\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528Matplotlib\u3001Seaborn\u6216Plotly\u6765\u521b\u5efa\u548c\u5b9a\u5236\u56fe\u4f8b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
                                      \n
                                    • \u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6\u5206\u914d\u6e05\u6670\u7684\u6807\u7b7e<\/strong>\uff1a\u786e\u4fdd\u56fe\u4f8b\u4e2d\u7684\u6bcf\u4e2a\u6807\u7b7e\u90fd\u51c6\u786e\u63cf\u8ff0\u6570\u636e\u96c6\u7684\u542b\u4e49\u3002<\/li>\n
                                    • \u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u548c\u6807\u8bb0<\/strong>\uff1a\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u548c\u6807\u8bb0\u6765\u533a\u5206\u6570\u636e\u96c6\uff0c\u4f7f\u56fe\u4f8b\u66f4\u5177\u53ef\u8bfb\u6027\u3002<\/li>\n
                                    • \u5b9a\u5236\u56fe\u4f8b\u7684\u4f4d\u7f6e\u548c\u5916\u89c2<\/strong>\uff1a\u6839\u636e\u56fe\u8868\u7684\u5e03\u5c40\u548c\u8bbe\u8ba1\u9700\u6c42\uff0c\u5b9a\u5236\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3001\u5b57\u4f53\u548c\u8fb9\u6846\u3002<\/li>\n
                                    • \u4f7f\u7528\u9ad8\u7ea7\u5de5\u5177\u8fdb\u884c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316<\/strong>\uff1a\u5728\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u60c5\u51b5\u4e0b\uff0c\u8003\u8651\u4f7f\u7528Plotly\u7b49\u5de5\u5177\u6765\u589e\u5f3a\u7528\u6237\u4f53\u9a8c\u3002<\/li>\n<\/ul>\n
                                      \u901a\u8fc7\u5408\u7406\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u548c\u6280\u672f\uff0c\u53ef\u4ee5\u521b\u5efa\u51fa\u66f4\u52a0\u4e13\u4e1a\u548c\u6613\u4e8e\u7406\u89e3\u7684\u6570\u636e\u53ef\u89c6\u5316\u4f5c\u54c1\u3002<\/p>\n<\/p>\n
                                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                                       \u5982\u4f55\u5728Python\u7684\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\uff1f<\/strong>
                                      \u5728Python\u4e2d\u4f7f\u7528Matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528plt.legend()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u56fe\u4f8b\u3002\u5728\u7ed8\u5236\u6563\u70b9\u56fe\u65f6\uff0c\u4e3a\u6bcf\u4e00\u7ec4\u6570\u636e\u6307\u5b9a\u6807\u7b7e\uff0c\u7136\u540e\u5728\u8c03\u7528legend()<\/code>\u51fd\u6570\u65f6\uff0c\u56fe\u4f8b\u5c06\u81ea\u52a8\u663e\u793a\u8fd9\u4e9b\u6807\u7b7e\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                                      import matplotlib.pyplot as plt\n\n# \u793a\u4f8b\u6570\u636e\nx1 = [1, 2, 3, 4]\ny1 = [10, 20, 25, 30]\nx2 = [1, 2, 3, 4]\ny2 = [5, 15, 20, 25]\n\n# \u7ed8\u5236\u6563\u70b9\u56fe\nplt.scatter(x1, y1, label='\u6570\u636e\u7ec41', color='blue')\nplt.scatter(x2, y2, label='\u6570\u636e\u7ec42', color='red')\n\n# \u6dfb\u52a0\u56fe\u4f8b\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
                                      \u6563\u70b9\u56fe\u4e2d\u7684\u56fe\u4f8b\u53ef\u4ee5\u81ea\u5b9a\u4e49\u54ea\u4e9b\u5c5e\u6027\uff1f<\/strong>
                                      \u5728\u521b\u5efa\u56fe\u4f8b\u65f6\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49\u591a\u4e2a\u5c5e\u6027\uff0c\u5305\u62ec\u56fe\u4f8b\u7684\u4f4d\u7f6e\u3001\u5b57\u4f53\u5927\u5c0f\u3001\u8fb9\u6846\u6837\u5f0f\u7b49\u3002\u901a\u8fc7\u4f20\u5165\u53c2\u6570loc<\/code>\u53ef\u4ee5\u8bbe\u7f6e\u56fe\u4f8b\u7684\u4f4d\u7f6e\uff0cfontsize<\/code>\u53ef\u4ee5\u8c03\u6574\u5b57\u4f53\u5927\u5c0f\uff0cframeon<\/code>\u53ef\u4ee5\u63a7\u5236\u662f\u5426\u663e\u793a\u8fb9\u6846\u3002\u4f8b\u5982\uff1a  <\/p>\n
                                      plt.legend(loc='upper left', fontsize='large', frameon=False)\n<\/code><\/pre>\n
                                      \u5982\u679c\u6211\u60f3\u4e3a\u6563\u70b9\u56fe\u4e2d\u7684\u4e0d\u540c\u70b9\u4f7f\u7528\u4e0d\u540c\u7684\u6807\u8bb0\u6837\u5f0f\uff0c\u8be5\u5982\u4f55\u64cd\u4f5c\uff1f<\/strong>
                                      \u53ef\u4ee5\u4e3a\u6bcf\u7ec4\u6570\u636e\u4f7f\u7528\u4e0d\u540c\u7684\u6807\u8bb0\u6837\u5f0f\uff08\u5982\u5706\u5f62\u3001\u4e09\u89d2\u5f62\u3001\u6b63\u65b9\u5f62\u7b49\uff09\uff0c\u5728\u8c03\u7528scatter()<\/code>\u51fd\u6570\u65f6\uff0c\u901a\u8fc7marker<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528marker='o'<\/code>\u8868\u793a\u5706\u5f62\uff0cmarker='^'<\/code>\u8868\u793a\u4e09\u89d2\u5f62\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
                                      plt.scatter(x1, y1, label='\u5706\u5f62\u6807\u8bb0', marker='o', color='blue')\nplt.scatter(x2, y2, label='\u4e09\u89d2\u5f62\u6807\u8bb0', marker='^', color='red')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
                                      \u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u8ba9\u56fe\u4f8b\u66f4\u5177\u53ef\u8bfb\u6027\u548c\u89c6\u89c9\u51b2\u51fb\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u7684\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684legend()\u51fd\u6570\u3001\u901a\u8fc7\u4e3a\u6bcf\u4e2a\u6570\u636e\u96c6 […]","protected":false},"author":3,"featured_media":1003960,"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\/1003944"}],"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=1003944"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1003944\/revisions"}],"predecessor-version":[{"id":1003966,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1003944\/revisions\/1003966"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1003960"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1003944"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1003944"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1003944"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}