{"id":1062758,"date":"2024-12-31T15:54:42","date_gmt":"2024-12-31T07:54:42","guid":{"rendered":""},"modified":"2024-12-31T15:54:44","modified_gmt":"2024-12-31T07:54:44","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e6%95%b0%e6%8d%ae%e6%88%90%e5%9b%be%e5%bd%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1062758.html","title":{"rendered":"python\u5982\u4f55\u7edf\u8ba1\u6570\u636e\u6210\u56fe\u5f62"},"content":{"rendered":"

\"python\u5982\u4f55\u7edf\u8ba1\u6570\u636e\u6210\u56fe\u5f62\"<\/p>\n

Python\u7edf\u8ba1\u6570\u636e\u6210\u56fe\u5f62\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Pandas\u3001Plotly\u7b49\u5e93\uff0c\u53ef\u4ee5\u5b9e\u73b0\u67f1\u72b6\u56fe\u3001\u6298\u7ebf\u56fe\u3001\u997c\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u591a\u79cd\u56fe\u5f62\u7684\u7ed8\u5236\u3002<\/strong> \u5176\u4e2d\uff0cMatplotlib\u662f\u6700\u57fa\u7840\u548c\u5e7f\u6cdb\u4f7f\u7528\u7684\u5e93\uff0c\u9002\u5408\u521d\u5b66\u8005\u638c\u63e1\u3002\u901a\u8fc7Matplotlib\u53ef\u4ee5\u5feb\u901f\u521b\u5efa\u7b80\u5355\u7684\u56fe\u5f62\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u63d0\u9ad8\u7ed8\u56fe\u7684\u6548\u7387\u548c\u7f8e\u89c2\u5ea6\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7ed8\u5236\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n

\u67f1\u72b6\u56fe\uff08Bar Chart\uff09\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u636e\u53ef\u89c6\u5316\u65b9\u5f0f\uff0c\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5927\u5c0f\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5bfc\u5165Matplotlib\u5e93<\/li>\n
  2. \u51c6\u5907\u6570\u636e<\/li>\n
  3. \u4f7f\u7528plt.bar()<\/code>\u51fd\u6570\u7ed8\u5236\u67f1\u72b6\u56fe<\/li>\n
  4. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/li>\n
  5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n

    import matplotlib.pyplot as plt<\/p>\n
    \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
    categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
    values = [10, 24, 36, 40, 5]<\/p>\n
    \u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
    plt.bar(categories, values, color='blue')<\/p>\n
    \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
    plt.title('Sample Bar Chart')<\/p>\n
    plt.xlabel('Categories')<\/p>\n
    plt.ylabel('Values')<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165Matplotlib\u5e93\uff0c\u7136\u540e\u5b9a\u4e49\u7c7b\u522b\u548c\u5bf9\u5e94\u7684\u503c\u3002\u4f7f\u7528plt.bar()<\/code>\u51fd\u6570\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u5e76\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
    \u4e8c\u3001MATPLOTLIB\u7ed8\u5236\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
    \u6298\u7ebf\u56fe\uff08Line Chart\uff09\u7528\u4e8e\u663e\u793a\u6570\u636e\u968f\u65f6\u95f4\u53d8\u5316\u7684\u8d8b\u52bf\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
      \n
    1. \u5bfc\u5165Matplotlib\u5e93<\/li>\n
    2. \u51c6\u5907\u6570\u636e<\/li>\n
    3. \u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u7ed8\u5236\u6298\u7ebf\u56fe<\/li>\n
    4. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/li>\n
    5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
      months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']<\/p>\n
      sales = [123, 150, 170, 145, 180, 200, 210, 195, 185, 220, 240, 250]<\/p>\n
      \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
      plt.plot(months, sales, marker='o', linestyle='-', color='green')<\/p>\n
      \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
      plt.title('Monthly Sales')<\/p>\n
      plt.xlabel('Months')<\/p>\n
      plt.ylabel('Sales')<\/p>\n
      \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u6708\u4efd\u548c\u5bf9\u5e94\u7684\u9500\u552e\u6570\u636e\u3002\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u6dfb\u52a0\u6570\u636e\u70b9\u7684\u6807\u8bb0\u3002\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
      \u4e09\u3001MATPLOTLIB\u7ed8\u5236\u997c\u56fe<\/h3>\n<\/p>\n
      \u997c\u56fe\uff08Pie Chart\uff09\u7528\u4e8e\u663e\u793a\u5404\u90e8\u5206\u5728\u603b\u4f53\u4e2d\u7684\u5360\u6bd4\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u997c\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        \n
      1. \u5bfc\u5165Matplotlib\u5e93<\/li>\n
      2. \u51c6\u5907\u6570\u636e<\/li>\n
      3. \u4f7f\u7528plt.pie()<\/code>\u51fd\u6570\u7ed8\u5236\u997c\u56fe<\/li>\n
      4. \u6dfb\u52a0\u6807\u9898<\/li>\n
      5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
        labels = ['A', 'B', 'C', 'D']<\/p>\n
        sizes = [15, 30, 45, 10]<\/p>\n
        colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']<\/p>\n
        explode = (0.1, 0, 0, 0)  # \u7a81\u51fa\u663e\u793a\u7b2c\u4e00\u90e8\u5206<\/p>\n
        \u7ed8\u5236\u997c\u56fe<\/strong><\/h2>\n
        plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)<\/p>\n
        \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
        plt.title('Sample Pie Chart')<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u6807\u7b7e\u3001\u5927\u5c0f\u3001\u989c\u8272\u548c\u7a81\u51fa\u663e\u793a\u7684\u90e8\u5206\u3002\u4f7f\u7528plt.pie()<\/code>\u51fd\u6570\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u901a\u8fc7autopct<\/code>\u53c2\u6570\u663e\u793a\u767e\u5206\u6bd4\u3002\u901a\u8fc7plt.title()<\/code>\u6dfb\u52a0\u6807\u9898\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \u56db\u3001MATPLOTLIB\u7ed8\u5236\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
        \u6563\u70b9\u56fe\uff08Scatter Plot\uff09\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528Matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
          \n
        1. \u5bfc\u5165Matplotlib\u5e93<\/li>\n
        2. \u51c6\u5907\u6570\u636e<\/li>\n
        3. \u4f7f\u7528plt.scatter()<\/code>\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe<\/li>\n
        4. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/li>\n
        5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
          import matplotlib.pyplot as plt<\/p>\n
          \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
          x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6]<\/p>\n
          y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78, 77, 85, 86]<\/p>\n
          \u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
          plt.scatter(x, y, color='red')<\/p>\n
          \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
          plt.title('Sample Scatter Plot')<\/p>\n
          plt.xlabel('X values')<\/p>\n
          plt.ylabel('Y values')<\/p>\n
          \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86x\u548cy\u7684\u503c\u3002\u4f7f\u7528plt.scatter()<\/code>\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u5e76\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
          \u4e94\u3001SEABORN\u7ed8\u5236\u70ed\u529b\u56fe<\/h3>\n<\/p>\n
          \u70ed\u529b\u56fe\uff08Heatmap\uff09\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u503c\uff0c\u901a\u8fc7\u989c\u8272\u6765\u8868\u793a\u6570\u636e\u7684\u5927\u5c0f\u3002\u4f7f\u7528Seaborn\u7ed8\u5236\u70ed\u529b\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
            \n
          1. \u5bfc\u5165Seaborn\u548cMatplotlib\u5e93<\/li>\n
          2. \u51c6\u5907\u6570\u636e<\/li>\n
          3. \u4f7f\u7528sns.heatmap()<\/code>\u51fd\u6570\u7ed8\u5236\u70ed\u529b\u56fe<\/li>\n
          4. \u6dfb\u52a0\u6807\u9898<\/li>\n
          5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
            import seaborn as sns<\/p>\n
            import matplotlib.pyplot as plt<\/p>\n
            import numpy as np<\/p>\n
            \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
            data = np.random.rand(10, 12)<\/p>\n
            \u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
            sns.heatmap(data, annot=True, fmt=".1f", cmap='coolwarm')<\/p>\n
            \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
            plt.title('Sample Heatmap')<\/p>\n
            \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u751f\u6210\u968f\u673a\u6570\u636e\u3002\u4f7f\u7528sns.heatmap()<\/code>\u51fd\u6570\u7ed8\u5236\u70ed\u529b\u56fe\uff0c\u5e76\u901a\u8fc7annot<\/code>\u53c2\u6570\u663e\u793a\u6570\u636e\u503c\u3002\u901a\u8fc7plt.title()<\/code>\u6dfb\u52a0\u6807\u9898\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
            \u516d\u3001PANDAS\u7ed8\u5236\u6570\u636e\u6846\u56fe\u5f62<\/h3>\n<\/p>\n
            Pandas\u5e93\u9664\u4e86\u7528\u4e8e\u6570\u636e\u5904\u7406\u5916\uff0c\u8fd8\u53ef\u4ee5\u76f4\u63a5\u7ed8\u5236\u56fe\u5f62\u3002\u4f7f\u7528Pandas\u7ed8\u5236\u56fe\u5f62\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
              \n
            1. \u5bfc\u5165Pandas\u548cMatplotlib\u5e93<\/li>\n
            2. \u51c6\u5907\u6570\u636e<\/li>\n
            3. \u4f7f\u7528Pandas\u7684plot<\/code>\u65b9\u6cd5\u7ed8\u5236\u56fe\u5f62<\/li>\n
            4. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/li>\n
            5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
              import pandas as pd<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
              data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],<\/p>\n
                      'Sales': [200, 220, 250, 210, 300]}<\/p>\n
              df = pd.DataFrame(data)<\/p>\n
              \u7ed8\u5236\u6570\u636e\u6846\u56fe\u5f62<\/strong><\/h2>\n
              df.plot(x='Month', y='Sales', kind='bar', color='purple')<\/p>\n
              \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
              plt.title('Monthly Sales')<\/p>\n
              plt.xlabel('Month')<\/p>\n
              plt.ylabel('Sales')<\/p>\n
              \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u521b\u5efa\u6570\u636e\u6846\uff0c\u5e76\u4f7f\u7528plot<\/code>\u65b9\u6cd5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002\u901a\u8fc7plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528plt.show()<\/code>\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
              \u4e03\u3001PLOTLY\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/h3>\n<\/p>\n
              Plotly\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u4f7f\u7528Plotly\u7ed8\u5236\u56fe\u5f62\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
                \n
              1. \u5bfc\u5165Plotly\u5e93<\/li>\n
              2. \u51c6\u5907\u6570\u636e<\/li>\n
              3. \u4f7f\u7528Plotly\u7684graph_objects<\/code>\u6a21\u5757\u521b\u5efa\u56fe\u5f62<\/li>\n
              4. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/li>\n
              5. \u663e\u793a\u56fe\u5f62<\/li>\n<\/ol>\n
                import plotly.graph_objects as go<\/p>\n
                \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
                categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
                values = [10, 24, 36, 40, 5]<\/p>\n
                \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
                fig = go.Figure(data=[go.Bar(x=categories, y=values)])<\/p>\n
                \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
                fig.update_layout(title='Sample Bar Chart',<\/p>\n
                                  xaxis_title='Categories',<\/p>\n
                                  yaxis_title='Values')<\/p>\n
                \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Plotly\u7684graph_objects<\/code>\u6a21\u5757\u521b\u5efa\u67f1\u72b6\u56fe\u3002\u901a\u8fc7update_layout<\/code>\u65b9\u6cd5\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528show<\/code>\u65b9\u6cd5\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
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