{"id":966311,"date":"2024-12-27T04:46:29","date_gmt":"2024-12-26T20:46:29","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/966311.html"},"modified":"2024-12-27T04:46:31","modified_gmt":"2024-12-26T20:46:31","slug":"python%e5%a6%82%e4%bd%95%e5%85%b3%e9%97%ad%e7%bb%98%e5%9b%be%e7%aa%97%e5%8f%a3","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/966311.html","title":{"rendered":"python\u5982\u4f55\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3"},"content":{"rendered":"

\"python\u5982\u4f55\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u4f9d\u8d56\u4e8e\u6240\u4f7f\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5982matplotlib<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7plt.close()<\/code>\u3001plt.close('all')<\/code>\u3001\u76f4\u63a5\u5173\u95ed\u7a97\u53e3\u6765\u5b9e\u73b0\u3002\u5728\u4f7f\u7528matplotlib<\/code>\u65f6\uff0c\u6700\u5e38\u7528\u7684\u662fplt.close()<\/code>\uff0c\u56e0\u4e3a\u5b83\u5141\u8bb8\u5173\u95ed\u7279\u5b9a\u7684\u56fe\u5f62\u7a97\u53e3\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u4e2d\u7684\u7ed8\u56fe\u7a97\u53e3\u5173\u95ed\u65b9\u6cd5<\/p>\n<\/p>\n

matplotlib<\/code>\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

    \n
  1. \n

    \u4f7f\u7528plt.close()<\/code>\u51fd\u6570<\/strong><\/p>\n<\/p>\n

    plt.close()<\/code>\u53ef\u4ee5\u5173\u95ed\u5f53\u524d\u6d3b\u52a8\u7684\u7ed8\u56fe\u7a97\u53e3\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4f20\u9012\u4e0d\u540c\u7684\u53c2\u6570\u6765\u63a7\u5236\u5173\u95ed\u7684\u5177\u4f53\u7a97\u53e3\u3002\u4f8b\u5982\uff0cplt.close(fig)<\/code>\u5173\u95ed\u7279\u5b9a\u7684\u56fe\u5f62\u5bf9\u8c61\uff0c\u800cplt.close('all')<\/code>\u5173\u95ed\u6240\u6709\u56fe\u5f62\u7a97\u53e3\u3002<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    \u521b\u5efa\u5e76\u663e\u793a\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u5f62<\/strong><\/h2>\n
    plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
    plt.show()<\/p>\n
    \u5173\u95ed\u5f53\u524d\u6d3b\u52a8\u7684\u56fe\u5f62\u7a97\u53e3<\/strong><\/h2>\n
    plt.close()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u4f7f\u7528plt.close('all')<\/code><\/strong><\/p>\n<\/p>\n
    \u5982\u679c\u6709\u591a\u4e2a\u56fe\u5f62\u7a97\u53e3\u6253\u5f00\uff0c\u800c\u4f60\u5e0c\u671b\u4e00\u6b21\u6027\u5173\u95ed\u6240\u6709\u7a97\u53e3\uff0c\u53ef\u4ee5\u4f7f\u7528plt.close('all')<\/code>\u3002<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u521b\u5efa\u591a\u4e2a\u56fe\u5f62<\/strong><\/h2>\n
    plt.figure()<\/p>\n
    plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
    plt.figure()<\/p>\n
    plt.plot([1, 2, 3], [6, 5, 4])<\/p>\n
    plt.show()<\/p>\n
    \u5173\u95ed\u6240\u6709\u56fe\u5f62\u7a97\u53e3<\/strong><\/h2>\n
    plt.close('all')<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  3. \n
    \u901a\u8fc7\u7a97\u53e3\u63a7\u4ef6\u5173\u95ed<\/strong><\/p>\n<\/p>\n
    \u5728\u663e\u793a\u56fe\u5f62\u65f6\uff0c\u901a\u5e38\u4f1a\u5f39\u51fa\u4e00\u4e2a\u7a97\u53e3\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u70b9\u51fb\u7a97\u53e3\u53f3\u4e0a\u89d2\u7684\u201c\u5173\u95ed\u201d\u6309\u94ae\u6765\u624b\u52a8\u5173\u95ed\u7a97\u53e3\u3002<\/p>\n<\/p>\n
    \u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u76f4\u89c2\uff0c\u4f46\u5728\u81ea\u52a8\u5316\u811a\u672c\u6216\u4e0d\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u7a0b\u5e8f\u4e2d\u5e76\u4e0d\u5b9e\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u4f7f\u7528SEABORN\u548cPANDAS\u4e2d\u7684\u7ed8\u56fe<\/p>\n<\/p>\n
    seaborn<\/code>\u548cpandas<\/code>\u662f\u6784\u5efa\u5728matplotlib<\/code>\u4e4b\u4e0a\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u56e0\u6b64\u5b83\u4eec\u7684\u56fe\u5f62\u5173\u95ed\u65b9\u6cd5\u4e0ematplotlib<\/code>\u76f8\u540c\u3002\u7531\u4e8eseaborn<\/code>\u548cpandas<\/code>\u4f9d\u8d56\u4e8ematplotlib<\/code>\uff0c\u6240\u4ee5\u4f7f\u7528plt.close()<\/code>\u7cfb\u5217\u51fd\u6570\u540c\u6837\u9002\u7528\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u5173\u95edseaborn<\/code>\u751f\u6210\u7684\u56fe\u5f62<\/strong><\/p>\n<\/p>\n
      seaborn<\/code>\u901a\u5e38\u7528\u4e8e\u521b\u5efa\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u5f62\u3002\u7531\u4e8e\u5b83\u4f9d\u8d56\u4e8ematplotlib<\/code>\uff0c\u5173\u95ed\u65b9\u6cd5\u4e0e\u524d\u8ff0\u76f8\u540c\u3002<\/p>\n<\/p>\n
      import seaborn as sns<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u7bb1\u578b\u56fe<\/strong><\/h2>\n
      sns.boxplot(x=[1, 2, 3, 4, 5])<\/p>\n
      plt.show()<\/p>\n
      plt.close()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u5173\u95edpandas<\/code>\u751f\u6210\u7684\u56fe\u5f62<\/strong><\/p>\n<\/p>\n
      pandas<\/code>\u7684\u7ed8\u56fe\u529f\u80fd\u901a\u5e38\u7528\u4e8e\u6570\u636e\u6846\u7684\u5feb\u901f\u53ef\u89c6\u5316\u3002\u4f7f\u7528pandas<\/code>\u751f\u6210\u7684\u56fe\u5f62\uff0c\u5173\u95ed\u65b9\u6cd5\u4e5f\u4e0ematplotlib<\/code>\u76f8\u540c\u3002<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u521b\u5efa\u7b80\u5355\u6570\u636e\u6846\u5e76\u7ed8\u56fe<\/strong><\/h2>\n
      df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})<\/p>\n
      df.plot()<\/p>\n
      plt.show()<\/p>\n
      plt.close()<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u7684\u7ed8\u56fe\u7a97\u53e3\u5173\u95ed<\/p>\n<\/p>\n
      \u5728Jupyter Notebook\u6216IPython\u7b49\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\uff0c\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u65b9\u5f0f\u53ef\u80fd\u4f1a\u6709\u6240\u4e0d\u540c\u3002\u901a\u5e38\u5728\u8fd9\u4e9b\u73af\u5883\u4e2d\uff0c\u56fe\u5f62\u662f\u5d4c\u5165\u5728\u754c\u9762\u4e2d\u7684\uff0c\u800c\u4e0d\u662f\u5f39\u51fa\u4e3a\u72ec\u7acb\u7684\u7a97\u53e3\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u5728Jupyter Notebook\u4e2d\u5173\u95ed\u56fe\u5f62<\/strong><\/p>\n<\/p>\n
        \u5728Jupyter Notebook\u4e2d\uff0c\u56fe\u5f62\u901a\u5e38\u4f1a\u5d4c\u5165\u5728\u7b14\u8bb0\u672c\u5355\u5143\u683c\u4e2d\u3002\u8981\u63a7\u5236\u56fe\u5f62\u663e\u793a\uff0c\u53ef\u4ee5\u4f7f\u7528%matplotlib inline<\/code>\u6216%matplotlib notebook<\/code>\u7b49\u9b54\u6cd5\u547d\u4ee4\u6765\u63a7\u5236\u663e\u793a\u6a21\u5f0f\u3002\u5173\u95ed\u56fe\u5f62\u5219\u4f9d\u7136\u4f7f\u7528plt.close()<\/code>\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u4f7f\u7528inline\u6a21\u5f0f<\/strong><\/h2>\n
        %matplotlib inline<\/p>\n
        \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
        plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
        \u5173\u95ed\u56fe\u5f62<\/strong><\/h2>\n
        plt.close()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u5728IPython\u4e2d\u5173\u95ed\u56fe\u5f62<\/strong><\/p>\n<\/p>\n
        \u7c7b\u4f3c\u4e8eJupyter Notebook\uff0cIPython\u4e2d\u7684\u56fe\u5f62\u901a\u5e38\u4e5f\u5d4c\u5165\u5728\u754c\u9762\u4e2d\u3002\u5173\u95ed\u65b9\u6cd5\u4e0e\u4f7f\u7528matplotlib<\/code>\u65f6\u76f8\u540c\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
        plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
        \u5173\u95ed\u56fe\u5f62<\/strong><\/h2>\n
        plt.close()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u6700\u4f73\u5b9e\u8df5<\/p>\n<\/p>\n
        \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u64cd\u4f5c\u5e94\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u9009\u62e9\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
          \n
        1. \n
          \u4f7f\u7528\u5408\u9002\u7684\u5173\u95ed\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
          \u5982\u679c\u7a0b\u5e8f\u4e2d\u9700\u8981\u9891\u7e41\u6253\u5f00\u548c\u5173\u95ed\u56fe\u5f62\u7a97\u53e3\uff0c\u5efa\u8bae\u4f7f\u7528plt.close()<\/code>\u6216plt.close('all')<\/code>\u6765\u7ba1\u7406\u56fe\u5f62\u7a97\u53e3\u7684\u8d44\u6e90\u91ca\u653e\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u8003\u8651\u811a\u672c\u8fd0\u884c\u73af\u5883<\/strong><\/p>\n<\/p>\n
          \u9488\u5bf9\u4e0d\u540c\u7684\u8fd0\u884c\u73af\u5883\uff08\u5982\u547d\u4ee4\u884c\u3001Jupyter Notebook\u3001IPython\u7b49\uff09\uff0c\u9009\u62e9\u5408\u9002\u7684\u5173\u95ed\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u5728Jupyter Notebook\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528\u9b54\u6cd5\u547d\u4ee4\u63a7\u5236\u56fe\u5f62\u663e\u793a\u3002<\/p>\n<\/p>\n<\/li>\n
        3. \n
          \u4f18\u5316\u8d44\u6e90\u7ba1\u7406<\/a><\/strong><\/p>\n<\/p>\n
          \u5728\u5904\u7406\u5927\u91cf\u6570\u636e\u6216\u751f\u6210\u5927\u91cf\u56fe\u5f62\u65f6\uff0c\u5408\u7406\u5173\u95ed\u4e0d\u5fc5\u8981\u7684\u56fe\u5f62\u7a97\u53e3\u53ef\u4ee5\u8282\u7701\u5185\u5b58\u8d44\u6e90\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u8fd0\u884c\u6548\u7387\u3002<\/p>\n<\/p>\n<\/li>\n
        4. \n
          \u8c03\u8bd5\u548c\u6d4b\u8bd5<\/strong><\/p>\n<\/p>\n
          \u5728\u8c03\u8bd5\u548c\u6d4b\u8bd5\u9636\u6bb5\uff0c\u9002\u5f53\u4fdd\u7559\u56fe\u5f62\u7a97\u53e3\u4ee5\u89c2\u5bdf\u7a0b\u5e8f\u7684\u8fd0\u884c\u7ed3\u679c\uff0c\u4e4b\u540e\u518d\u8fdb\u884c\u5173\u95ed\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u901a\u8fc7\u638c\u63e1\u4ee5\u4e0a\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u7684\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u66f4\u6709\u6548\u5730\u7ba1\u7406\u548c\u4f7f\u7528\u56fe\u5f62\u7a97\u53e3\u8d44\u6e90\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u53ef\u63a7\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u5728Python\u4e2d\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\uff1f<\/strong>
          \u5728\u4f7f\u7528Python\u8fdb\u884c\u7ed8\u56fe\u65f6\uff0c\u901a\u5e38\u4f1a\u4f7f\u7528Matplotlib\u5e93\u3002\u8981\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\uff0c\u53ef\u4ee5\u4f7f\u7528plt.close()<\/code>\u51fd\u6570\u3002\u8c03\u7528\u6b64\u51fd\u6570\u540e\uff0c\u5f53\u524d\u6d3b\u52a8\u7684\u56fe\u5f62\u7a97\u53e3\u5c06\u88ab\u5173\u95ed\u3002\u5982\u679c\u5e0c\u671b\u5173\u95ed\u6240\u6709\u6253\u5f00\u7684\u56fe\u5f62\u7a97\u53e3\uff0c\u53ef\u4ee5\u4f7f\u7528plt.close('all')<\/code>\u3002\u786e\u4fdd\u5728\u8c03\u7528\u8fd9\u4e9b\u51fd\u6570\u4e4b\u524d\uff0c\u5df2\u7ecf\u5bfc\u5165Matplotlib\u5e93\u5e76\u521b\u5efa\u4e86\u7ed8\u56fe\u3002<\/p>\n
          \u5728\u7ed8\u56fe\u65f6\u5982\u4f55\u63a7\u5236\u7a97\u53e3\u7684\u663e\u793a\u65f6\u95f4\uff1f<\/strong>
          \u5982\u679c\u5e0c\u671b\u5728\u7ed8\u56fe\u5b8c\u6210\u540e\u81ea\u52a8\u5173\u95ed\u7a97\u53e3\uff0c\u53ef\u4ee5\u4f7f\u7528plt.pause()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u7a97\u53e3\u663e\u793a\u7684\u65f6\u95f4\u3002\u4f8b\u5982\uff0cplt.pause(3)<\/code>\u5c06\u57283\u79d2\u540e\u81ea\u52a8\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\u3002\u8fd9\u6837\u53ef\u4ee5\u8ba9\u7528\u6237\u67e5\u770b\u56fe\u5f62\uff0c\u7136\u540e\u7a0b\u5e8f\u4f1a\u81ea\u52a8\u5173\u95ed\u7a97\u53e3\u3002<\/p>\n
          \u662f\u5426\u53ef\u4ee5\u901a\u8fc7\u56fe\u5f62\u7528\u6237\u754c\u9762\u624b\u52a8\u5173\u95ed\u7ed8\u56fe\u7a97\u53e3\uff1f<\/strong>
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