{"id":1066825,"date":"2024-12-31T16:29:47","date_gmt":"2024-12-31T08:29:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1066825.html"},"modified":"2024-12-31T16:29:49","modified_gmt":"2024-12-31T08:29:49","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e6%89%87%e5%bd%a2%e7%bb%9f%e8%ae%a1%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1066825.html","title":{"rendered":"python\u5982\u4f55\u505a\u6247\u5f62\u7edf\u8ba1\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u6247\u5f62\u7edf\u8ba1\u56fe\"<\/p>\n

Python\u5236\u4f5c\u6247\u5f62\u7edf\u8ba1\u56fe\u7684\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u5236\u4f5c\u6247\u5f62\u7edf\u8ba1\u56fe\uff08\u4e5f\u79f0\u4e3a\u997c\u56fe\uff09\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3001Pandas\u5e93\u4ee5\u53caSeaborn\u5e93\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7075\u6d3b\u7684\u9009\u9879\u6765\u6ee1\u8db3\u4e0d\u540c\u9700\u6c42\u3002\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528Seaborn\u5e93<\/strong>\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u90fd\u53ef\u4ee5\u5b9e\u73b0\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528Matplotlib\u5e93\u6765\u5236\u4f5c\u6247\u5f62\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u5e93<\/p>\n<\/p>\n

Matplotlib\u662fPython\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u7684\u51fd\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7matplotlib.pyplot.pie<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5Matplotlib\u5e93<\/strong><\/li>\n<\/ol>\n

    \u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install matplotlib<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5bfc\u5165\u5e93\u5e76\u51c6\u5907\u6570\u636e<\/strong><\/li>\n<\/ol>\n
      \u5728\u4f7f\u7528Matplotlib\u5e93\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u76f8\u5173\u5e93\u5e76\u51c6\u5907\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u8981\u7ed8\u5236\u4e00\u4e2a\u5c55\u793a\u4e0d\u540c\u6c34\u679c\u9500\u91cf\u7684\u6247\u5f62\u7edf\u8ba1\u56fe\uff1a<\/p>\n<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
      labels = ['Apple', 'Banana', 'Cherry', 'Date']<\/p>\n
      sizes = [25, 35, 20, 20]<\/p>\n
      colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe<\/strong><\/li>\n<\/ol>\n
        \u4f7f\u7528plt.pie<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\uff1a<\/p>\n<\/p>\n
        plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)<\/p>\n
        \u8bbe\u7f6e\u7b49\u8f74\uff0c\u4ee5\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62<\/strong><\/h2>\n
        plt.axis('equal')<\/p>\n
        \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
        plt.title('Fruit Sales Distribution')<\/p>\n
        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0clabels<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u6247\u5f62\u7684\u6807\u7b7e\uff0ccolors<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u6247\u5f62\u7684\u989c\u8272\uff0cautopct<\/code>\u53c2\u6570\u7528\u4e8e\u663e\u793a\u6bcf\u4e2a\u6247\u5f62\u7684\u767e\u5206\u6bd4\uff0cstartangle<\/code>\u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u8d77\u59cb\u89d2\u5ea6\uff0cplt.axis('equal')<\/code>\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62\u3002<\/p>\n<\/p>\n
        \u4e8c\u3001\u4f7f\u7528Pandas\u5e93<\/p>\n<\/p>\n
        Pandas\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u3002Pandas\u5e93\u7684\u4f18\u52bf\u5728\u4e8e\u5b83\u53ef\u4ee5\u76f4\u63a5\u4eceDataFrame\u5bf9\u8c61\u4e2d\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n<\/p>\n
          \n
        1. \u5b89\u88c5Pandas\u5e93<\/strong><\/li>\n<\/ol>\n
          \u786e\u4fdd\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
          pip install pandas<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u5bfc\u5165\u5e93\u5e76\u51c6\u5907\u6570\u636e<\/strong><\/li>\n<\/ol>\n
            \u5bfc\u5165Pandas\u5e93\u5e76\u51c6\u5907\u6570\u636e\uff1a<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
            data = {'Fruit': ['Apple', 'Banana', 'Cherry', 'Date'],<\/p>\n
                    'Sales': [25, 35, 20, 20]}<\/p>\n
            df = pd.DataFrame(data)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe<\/strong><\/li>\n<\/ol>\n
              \u4f7f\u7528Pandas\u5e93\u7684plot.pie<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\uff1a<\/p>\n<\/p>\n
              df.set_index('Fruit').plot.pie(y='Sales', autopct='%1.1f%%', startangle=140, figsize=(8, 8))<\/p>\n
              \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
              plt.title('Fruit Sales Distribution')<\/p>\n
              \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5c06Fruit<\/code>\u5217\u8bbe\u7f6e\u4e3a\u7d22\u5f15\uff0c\u7136\u540e\u4f7f\u7528plot.pie<\/code>\u51fd\u6570\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n
              \u4e09\u3001\u4f7f\u7528Seaborn\u5e93<\/p>\n<\/p>\n
              Seaborn\u5e93\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n
                \n
              1. \u5b89\u88c5Seaborn\u5e93<\/strong><\/li>\n<\/ol>\n
                \u786e\u4fdd\u5b89\u88c5\u4e86Seaborn\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
                pip install seaborn<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u5bfc\u5165\u5e93\u5e76\u51c6\u5907\u6570\u636e<\/strong><\/li>\n<\/ol>\n
                  \u5bfc\u5165Seaborn\u5e93\u5e76\u51c6\u5907\u6570\u636e\uff1a<\/p>\n<\/p>\n
                  import seaborn as sns<\/p>\n
                  import matplotlib.pyplot as plt<\/p>\n
                  \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
                  data = {'Fruit': ['Apple', 'Banana', 'Cherry', 'Date'],<\/p>\n
                          'Sales': [25, 35, 20, 20]}<\/p>\n
                  df = pd.DataFrame(data)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe<\/strong><\/li>\n<\/ol>\n
                    Seaborn\u5e93\u6ca1\u6709\u76f4\u63a5\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u7684\u51fd\u6570\uff0c\u4f46\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408Matplotlib\u548cSeaborn\u7684\u6837\u5f0f\u6765\u7ed8\u5236\uff1a<\/p>\n<\/p>\n
                    # \u8bbe\u7f6eSeaborn\u6837\u5f0f<\/p>\n
                    sns.set(style="whitegrid")<\/p>\n
                    \u4f7f\u7528Matplotlib\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe<\/strong><\/h2>\n
                    plt.pie(df['Sales'], labels=df['Fruit'], autopct='%1.1f%%', startangle=140, colors=sns.color_palette("husl", len(df)))<\/p>\n
                    \u8bbe\u7f6e\u7b49\u8f74\uff0c\u4ee5\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62<\/strong><\/h2>\n
                    plt.axis('equal')<\/p>\n
                    \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
                    plt.title('Fruit Sales Distribution')<\/p>\n
                    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                    plt.show()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Seaborn\u7684set<\/code>\u51fd\u6570\u8bbe\u7f6e\u6837\u5f0f\uff0c\u7136\u540e\u4f7f\u7528Matplotlib\u7684plt.pie<\/code>\u51fd\u6570\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u3002<\/p>\n<\/p>\n
                    \u56db\u3001\u6247\u5f62\u7edf\u8ba1\u56fe\u7684\u9ad8\u7ea7\u8bbe\u7f6e<\/p>\n<\/p>\n
                    \u9664\u4e86\u57fa\u672c\u7684\u7ed8\u5236\u65b9\u6cd5\uff0cMatplotlib\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u8bbe\u7f6e\u9009\u9879\uff0c\u4f7f\u6211\u4eec\u80fd\u591f\u81ea\u5b9a\u4e49\u6247\u5f62\u7edf\u8ba1\u56fe\u7684\u5916\u89c2\u548c\u884c\u4e3a\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u7a81\u51fa\u663e\u793a\u67d0\u4e2a\u6247\u5f62<\/strong><\/li>\n<\/ol>\n
                      \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528explode<\/code>\u53c2\u6570\u6765\u7a81\u51fa\u663e\u793a\u67d0\u4e2a\u6247\u5f62\uff1a<\/p>\n<\/p>\n
                      explode = (0.1, 0, 0, 0)  # \u4ec5\u7a81\u51fa\u663e\u793a\u7b2c\u4e00\u4e2a\u6247\u5f62\uff08\u82f9\u679c\uff09<\/p>\n
                      plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)<\/p>\n
                      \u8bbe\u7f6e\u7b49\u8f74\uff0c\u4ee5\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62<\/strong><\/h2>\n
                      plt.axis('equal')<\/p>\n
                      \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
                      plt.title('Fruit Sales Distribution')<\/p>\n
                      \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                      plt.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u81ea\u5b9a\u4e49\u6247\u5f62\u7684\u989c\u8272<\/strong><\/li>\n<\/ol>\n
                        \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528colors<\/code>\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u6bcf\u4e2a\u6247\u5f62\u7684\u989c\u8272\uff1a<\/p>\n<\/p>\n
                        colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']<\/p>\n
                        plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)<\/p>\n
                        \u8bbe\u7f6e\u7b49\u8f74\uff0c\u4ee5\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62<\/strong><\/h2>\n
                        plt.axis('equal')<\/p>\n
                        \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
                        plt.title('Fruit Sales Distribution')<\/p>\n
                        \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                        plt.show()<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/li>\n<\/ol>\n
                          \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528plt.legend<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n
                          plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)<\/p>\n
                          \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
                          plt.legend(labels, loc="best")<\/p>\n
                          \u8bbe\u7f6e\u7b49\u8f74\uff0c\u4ee5\u786e\u4fdd\u997c\u56fe\u662f\u4e00\u4e2a\u5706\u5f62<\/strong><\/h2>\n
                          plt.axis('equal')<\/p>\n
                          \u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
                          plt.title('Fruit Sales Distribution')<\/p>\n
                          \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
                          plt.show()<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u5730\u7ed8\u5236\u548c\u5b9a\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\u3002\u65e0\u8bba\u662f\u4f7f\u7528Matplotlib\u3001Pandas\u8fd8\u662fSeaborn\u5e93\uff0c\u90fd\u80fd\u591f\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\uff0c\u521b\u5efa\u51fa\u7f8e\u89c2\u4e14\u529f\u80fd\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
                          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                           \u6247\u5f62\u7edf\u8ba1\u56fe\u662f\u4ec0\u4e48\uff1f\u5b83\u6709\u4ec0\u4e48\u5e94\u7528\u573a\u666f\uff1f<\/strong>
                          \u6247\u5f62\u7edf\u8ba1\u56fe\uff0c\u53c8\u79f0\u4e3a\u997c\u56fe\uff0c\u662f\u4e00\u79cd\u7528\u4e8e\u663e\u793a\u5404\u4e2a\u90e8\u5206\u5360\u6574\u4f53\u6bd4\u4f8b\u7684\u56fe\u5f62\u3002\u5b83\u901a\u8fc7\u5c06\u4e00\u4e2a\u5706\u5206\u6210\u82e5\u5e72\u6247\u5f62\u6765\u5c55\u793a\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\u5305\u62ec\u5e02\u573a\u4efd\u989d\u5206\u6790\u3001\u8c03\u67e5\u7ed3\u679c\u5c55\u793a\u548c\u9884\u7b97\u5206\u914d\u7b49\uff0c\u5e2e\u52a9\u8bfb\u8005\u5feb\u901f\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n
                          \u5728Python\u4e2d\uff0c\u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7ed8\u5236\u6247\u5f62\u7edf\u8ba1\u56fe\uff1f<\/strong>
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