{"id":987175,"date":"2024-12-27T07:51:15","date_gmt":"2024-12-26T23:51:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/987175.html"},"modified":"2024-12-27T07:51:16","modified_gmt":"2024-12-26T23:51:16","slug":"python%e7%bb%98%e5%9b%be%e5%a6%82%e4%bd%95%e8%a7%84%e5%ae%9a%e5%ad%97%e4%bd%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/987175.html","title":{"rendered":"python\u7ed8\u56fe\u5982\u4f55\u89c4\u5b9a\u5b57\u4f53"},"content":{"rendered":"

\"python\u7ed8\u56fe\u5982\u4f55\u89c4\u5b9a\u5b57\u4f53\"<\/p>\n

\u5728Python\u7ed8\u56fe\u4e2d\uff0c\u89c4\u5b9a\u5b57\u4f53\u4e3b\u8981\u901a\u8fc7\u8bbe\u7f6e\u5b57\u4f53\u5bb6\u65cf\u3001\u5b57\u4f53\u5927\u5c0f\u3001\u5b57\u4f53\u989c\u8272\u7b49\u5c5e\u6027\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3002\u53ef\u4ee5\u4f7f\u7528rcParams\u5168\u5c40\u8bbe\u7f6e\u3001\u5728\u7ed8\u56fe\u51fd\u6570\u4e2d\u8bbe\u7f6e\u3001\u901a\u8fc7\u81ea\u5b9a\u4e49\u5b57\u4f53\u6587\u4ef6\u5b9e\u73b0\u5177\u4f53\u9700\u6c42\u3002<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u7ed8\u56fe\u5e38\u5e38\u4f7f\u7528\u7684\u5e93\u6709Matplotlib\u548cSeaborn\uff0c\u5b83\u4eec\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u8bbe\u7f6e\u5b57\u4f53\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u89c4\u5b9a\u7ed8\u56fe\u4e2d\u7684\u5b57\u4f53\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93\u4e2d\u7684\u5b57\u4f53\u8bbe\u7f6e<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8bbe\u7f6e\u5b57\u4f53\uff0c\u5305\u62ec\u5168\u5c40\u8bbe\u7f6e\u3001\u5c40\u90e8\u8bbe\u7f6e\u548c\u81ea\u5b9a\u4e49\u5b57\u4f53\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528rcParams\u8bbe\u7f6e\u5168\u5c40\u5b57\u4f53<\/li>\n<\/ol>\n

    Matplotlib\u7684rcParams\u662f\u4e00\u4e2a\u5168\u5c40\u914d\u7f6e\u5b57\u5178\uff0c\u53ef\u4ee5\u7528\u6765\u63a7\u5236\u56fe\u5f62\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5305\u62ec\u5b57\u4f53\u8bbe\u7f6e\u3002\u901a\u8fc7rcParams\u8bbe\u7f6e\u5168\u5c40\u5b57\u4f53\uff0c\u53ef\u4ee5\u786e\u4fdd\u6240\u6709\u7684\u56fe\u5f62\u4f7f\u7528\u76f8\u540c\u7684\u5b57\u4f53\u6837\u5f0f\u3002<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    \u8bbe\u7f6e\u5168\u5c40\u5b57\u4f53<\/strong><\/h2>\n
    plt.rcParams['font.family'] = 'serif'<\/p>\n
    plt.rcParams['font.size'] = 12<\/p>\n
    plt.rcParams['font.weight'] = 'bold'<\/p>\n
    \u793a\u4f8b\u7ed8\u56fe<\/strong><\/h2>\n
    plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
    plt.title('\u793a\u4f8b\u56fe\u5f62')<\/p>\n
    plt.xlabel('X\u8f74')<\/p>\n
    plt.ylabel('Y\u8f74')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5728\u7ed8\u56fe\u51fd\u6570\u4e2d\u5c40\u90e8\u8bbe\u7f6e\u5b57\u4f53<\/li>\n<\/ol>\n
      \u6709\u65f6\u5019\uff0c\u6211\u4eec\u53ef\u80fd\u53ea\u60f3\u5bf9\u67d0\u4e2a\u7279\u5b9a\u7684\u56fe\u5f62\u6216\u6587\u672c\u5143\u7d20\u8bbe\u7f6e\u5b57\u4f53\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7\u5728\u7ed8\u56fe\u51fd\u6570\u4e2d\u6307\u5b9a\u5b57\u4f53\u53c2\u6570\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
      # \u793a\u4f8b\u7ed8\u56fe<\/p>\n
      plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
      plt.title('\u793a\u4f8b\u56fe\u5f62', fontdict={'family': 'serif', 'size': 14, 'weight': 'bold'})<\/p>\n
      plt.xlabel('X\u8f74', fontsize=12)<\/p>\n
      plt.ylabel('Y\u8f74', fontsize=12)<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u4f7f\u7528\u81ea\u5b9a\u4e49\u5b57\u4f53\u6587\u4ef6<\/li>\n<\/ol>\n
        Matplotlib\u652f\u6301\u4f7f\u7528TTF\u548cOTF\u683c\u5f0f\u7684\u5b57\u4f53\u6587\u4ef6\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u5b57\u4f53\u8def\u5f84\u6765\u4f7f\u7528\u81ea\u5b9a\u4e49\u5b57\u4f53\u3002<\/p>\n<\/p>\n
        from matplotlib import font_manager<\/p>\n
        \u52a0\u8f7d\u81ea\u5b9a\u4e49\u5b57\u4f53<\/strong><\/h2>\n
        font_path = '\/path\/to\/your\/font.ttf'<\/p>\n
        font = font_manager.FontProperties(fname=font_path)<\/p>\n
        \u793a\u4f8b\u7ed8\u56fe<\/strong><\/h2>\n
        plt.plot([1, 2, 3], [4, 5, 6])<\/p>\n
        plt.title('\u793a\u4f8b\u56fe\u5f62', fontproperties=font)<\/p>\n
        plt.xlabel('X\u8f74', fontproperties=font)<\/p>\n
        plt.ylabel('Y\u8f74', fontproperties=font)<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001SEABORN\u5e93\u4e2d\u7684\u5b57\u4f53\u8bbe\u7f6e<\/p>\n<\/p>\n
        Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u5b83\u7b80\u5316\u4e86\u7edf\u8ba1\u56fe\u5f62\u7684\u521b\u5efa\u8fc7\u7a0b\uff0c\u5e76\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u6837\u5f0f\u548c\u8c03\u8272\u677f\u3002\u867d\u7136Seaborn\u7684\u5b57\u4f53\u8bbe\u7f6e\u9009\u9879\u6ca1\u6709Matplotlib\u90a3\u4e48\u7075\u6d3b\uff0c\u4f46\u4ecd\u7136\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u65b9\u6cd5\u6765\u8c03\u6574\u5b57\u4f53\u3002<\/p>\n<\/p>\n
          \n
        1. \u4f7f\u7528set_context\u548cset_style\u8bbe\u7f6e\u5b57\u4f53<\/li>\n<\/ol>\n
          Seaborn\u7684set_context\u548cset_style\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u4e0a\u4e0b\u6587\u548c\u6837\u5f0f\uff0c\u5176\u4e2d\u5305\u62ec\u5b57\u4f53\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
          import seaborn as sns<\/p>\n
          \u8bbe\u7f6e\u4e0a\u4e0b\u6587\u548c\u6837\u5f0f<\/strong><\/h2>\n
          sns.set_context("notebook", font_scale=1.5)<\/p>\n
          sns.set_style("whitegrid")<\/p>\n
          \u793a\u4f8b\u7ed8\u56fe<\/strong><\/h2>\n
          sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
          plt.title('\u793a\u4f8b\u56fe\u5f62')<\/p>\n
          plt.xlabel('X\u8f74')<\/p>\n
          plt.ylabel('Y\u8f74')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u4f7f\u7528rc\u53c2\u6570\u7ed3\u5408Seaborn\u8bbe\u7f6e\u5b57\u4f53<\/li>\n<\/ol>\n
            \u7531\u4e8eSeaborn\u662f\u57fa\u4e8eMatplotlib\u7684\uff0c\u56e0\u6b64\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684rcParams\u6765\u8bbe\u7f6e\u5b57\u4f53\uff0c\u5e76\u7ed3\u5408Seaborn\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
            # \u8bbe\u7f6e\u5b57\u4f53<\/p>\n
            plt.rcParams['font.family'] = 'sans-serif'<\/p>\n
            plt.rcParams['font.size'] = 12<\/p>\n
            \u793a\u4f8b\u7ed8\u56fe<\/strong><\/h2>\n
            sns.lineplot(x=[1, 2, 3], y=[4, 5, 6])<\/p>\n
            plt.title('\u793a\u4f8b\u56fe\u5f62')<\/p>\n
            plt.xlabel('X\u8f74')<\/p>\n
            plt.ylabel('Y\u8f74')<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u5b57\u4f53\u8bbe\u7f6e\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
              \n
            1. \n
              \u5b57\u4f53\u517c\u5bb9\u6027<\/strong>\uff1a\u5728\u8bbe\u7f6e\u5b57\u4f53\u65f6\uff0c\u9700\u8981\u786e\u4fdd\u6240\u4f7f\u7528\u7684\u5b57\u4f53\u5728\u5f53\u524d\u7cfb\u7edf\u4e2d\u662f\u53ef\u7528\u7684\uff0c\u5426\u5219\u53ef\u80fd\u5bfc\u81f4\u5b57\u4f53\u8bbe\u7f6e\u65e0\u6548\u6216\u8005\u62a5\u9519\u3002\u53ef\u4ee5\u901a\u8fc7matplotlib.font_manager.findSystemFonts()<\/code>\u6765\u67e5\u770b\u7cfb\u7edf\u4e2d\u53ef\u7528\u7684\u5b57\u4f53\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u5b57\u4f53\u5927\u5c0f\u548c\u6bd4\u4f8b<\/strong>\uff1a\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u5b57\u4f53\u5927\u5c0f\u5e94\u4e0e\u56fe\u5f62\u7684\u5176\u4ed6\u5143\u7d20\u6210\u5408\u7406\u7684\u6bd4\u4f8b\uff0c\u4ee5\u786e\u4fdd\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u3002\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574font.size<\/code>\u6216\u8005font_scale<\/code>\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n
            3. \n
              \u5b57\u4f53\u98ce\u683c<\/strong>\uff1a\u4e0d\u540c\u7684\u5b57\u4f53\u98ce\u683c\uff08\u5982serif\u3001sans-serif\u3001monospace\u7b49\uff09\u5728\u4e0d\u540c\u7684\u573a\u5408\u4e0b\u6709\u4e0d\u540c\u7684\u9002\u7528\u6027\u3002\u4e00\u822c\u6765\u8bf4\uff0cserif\u5b57\u4f53\u9002\u7528\u4e8e\u6b63\u5f0f\u7684\u573a\u5408\uff0c\u800csans-serif\u5b57\u4f53\u66f4\u52a0\u73b0\u4ee3\u548c\u7b80\u7ea6\u3002<\/p>\n<\/p>\n<\/li>\n
            4. \n
              \u591a\u8bed\u8a00\u652f\u6301<\/strong>\uff1a\u5982\u679c\u9700\u8981\u7ed8\u5236\u5305\u542b\u591a\u79cd\u8bed\u8a00\u5b57\u7b26\u7684\u56fe\u5f62\uff0c\u9700\u8981\u786e\u4fdd\u6240\u9009\u5b57\u4f53\u652f\u6301\u8fd9\u4e9b\u5b57\u7b26\u96c6\u3002\u5426\u5219\uff0c\u53ef\u80fd\u51fa\u73b0\u4e71\u7801\u6216\u8005\u65e0\u6cd5\u663e\u793a\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
              \u56db\u3001\u603b\u7ed3<\/p>\n<\/p>\n
              \u5728Python\u7ed8\u56fe\u4e2d\u89c4\u5b9a\u5b57\u4f53\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u5b83\u4e0d\u4ec5\u5f71\u54cd\u56fe\u5f62\u7684\u7f8e\u89c2\u7a0b\u5ea6\uff0c\u8fd8\u5173\u7cfb\u5230\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u4fe1\u606f\u4f20\u8fbe\u7684\u51c6\u786e\u6027\u3002\u901a\u8fc7Matplotlib\u548cSeaborn\u7b49\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u7075\u6d3b\u5730\u8bbe\u7f6e\u5b57\u4f53\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5e94\u8be5\u6839\u636e\u5177\u4f53\u7684\u573a\u5408\u548c\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5b57\u4f53\u98ce\u683c\u548c\u5927\u5c0f\uff0c\u4ece\u800c\u589e\u5f3a\u56fe\u5f62\u7684\u8868\u73b0\u529b\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5982\u4f55\u5728Python\u7ed8\u56fe\u4e2d\u8bbe\u7f6e\u7279\u5b9a\u5b57\u4f53\uff1f<\/strong>
              \u5728Python\u4e2d\u4f7f\u7528Matplotlib\u7ed8\u5236\u56fe\u5f62\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7fontdict<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u7279\u5b9a\u7684\u5b57\u4f53\u3002\u53ef\u4ee5\u5728plt.title()<\/code>, plt.xlabel()<\/code>, plt.ylabel()<\/code>\u7b49\u51fd\u6570\u4e2d\u4f20\u9012\u4e00\u4e2a\u5305\u542b\u5b57\u4f53\u5c5e\u6027\u7684\u5b57\u5178\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u8bbe\u7f6e\u5b57\u4f53\u540d\u79f0\u3001\u5927\u5c0f\u548c\u6837\u5f0f\u3002\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a  <\/p>\n
              import matplotlib.pyplot as plt\n\nplt.title('\u56fe\u8868\u6807\u9898', fontdict={'fontsize': 20, 'fontweight': 'bold', 'family': 'serif'})\n<\/code><\/pre>\n
              \u53ef\u4ee5\u5728\u54ea\u4e9b\u5730\u65b9\u8bbe\u7f6e\u5b57\u4f53\u5c5e\u6027\uff1f<\/strong>
              \u5b57\u4f53\u5c5e\u6027\u53ef\u4ee5\u5728\u591a\u79cd\u7ed8\u56fe\u5143\u7d20\u4e2d\u8bbe\u7f6e\uff0c\u5305\u62ec\u6807\u9898\u3001\u5750\u6807\u8f74\u6807\u7b7e\u3001\u523b\u5ea6\u6807\u7b7e\u548c\u56fe\u4f8b\u3002\u901a\u8fc7\u5728\u76f8\u5e94\u7684\u51fd\u6570\u4e2d\u4f7f\u7528fontdict<\/code>\u53c2\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u81ea\u5b9a\u4e49\u5b57\u4f53\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u5728\u8bbe\u7f6e\u5750\u6807\u8f74\u6807\u7b7e\u65f6\uff0c\u53ef\u4ee5\u5982\u4e0b\u64cd\u4f5c\uff1a  <\/p>\n
              plt.xlabel('X\u8f74\u6807\u7b7e', fontdict={'fontsize': 15, 'family': 'sans-serif'})\n<\/code><\/pre>\n
              \u5982\u4f55\u67e5\u770b\u53ef\u7528\u7684\u5b57\u4f53\u5217\u8868\uff1f<\/strong>
              \u8981\u67e5\u770b\u7cfb\u7edf\u4e2d\u53ef\u7528\u7684\u5b57\u4f53\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684font_manager<\/code>\u6a21\u5757\u3002\u6267\u884c\u4ee5\u4e0b\u4ee3\u7801\u53ef\u4ee5\u5217\u51fa\u6240\u6709\u53ef\u7528\u5b57\u4f53\uff1a  <\/p>\n
              import matplotlib.font_manager\n\nfor font in matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf'):\n    print(font)\n<\/code><\/pre>\n
              \u6b64\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u60a8\u627e\u5230\u5408\u9002\u7684\u5b57\u4f53\uff0c\u4ee5\u4fbf\u5728\u7ed8\u56fe\u65f6\u8fdb\u884c\u9009\u62e9\u548c\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u7ed8\u56fe\u4e2d\uff0c\u89c4\u5b9a\u5b57\u4f53\u4e3b\u8981\u901a\u8fc7\u8bbe\u7f6e\u5b57\u4f53\u5bb6\u65cf\u3001\u5b57\u4f53\u5927\u5c0f\u3001\u5b57\u4f53\u989c\u8272\u7b49\u5c5e\u6027\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib […]","protected":false},"author":3,"featured_media":987176,"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\/987175"}],"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=987175"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/987175\/revisions"}],"predecessor-version":[{"id":987178,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/987175\/revisions\/987178"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/987176"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=987175"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=987175"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=987175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}