{"id":119819,"date":"2026-10-30T14:24:49","date_gmt":"2026-10-30T06:24:49","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/119819.html"},"modified":"2026-10-30T14:24:49","modified_gmt":"2026-10-30T06:24:49","slug":"%e3%80%90python%e3%80%91%e5%9f%ba%e7%a1%80%e8%af%ad%e6%b3%95%e5%85%a5%e9%97%a8%ef%bc%88%e4%b8%89%e5%8d%81%e4%ba%94%ef%bc%89-%e6%95%b0%e6%8d%ae%e5%8f%af%e8%a7%86%e5%8c%96%ef%bc%9a","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/119819.html","title":{"rendered":"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe"},"content":{"rendered":"

\u524d\u8a00：\u4e0a\u7bc7\u6211\u4eec\u5b66\u4f1a\u4e86\u7528 pandas \u5904\u7406\u8868\u683c\u6570\u636e，\u4f46\u8868\u683c\u91cc\u5168\u662f\u6570\u5b57，\u4e0d\u591f\u76f4\u89c2\u3002\u4fd7\u8bdd\u8bf4"\u4e00\u56fe\u80dc\u5343\u8a00"，\u6570\u636e\u53ef\u89c6\u5316\u80fd\u628a\u67af\u71e5\u7684\u6570\u5b57\u53d8\u6210\u76f4\u89c2\u7684\u56fe\u8868，\u8ba9\u4eba\u4e00\u773c\u770b\u51fa\u8d8b\u52bf\u3001\u5206\u5e03\u548c\u5f02\u5e38\u3002\u672c\u7bc7\u5e26\u4f60\u638c\u63e1 Python \u6700\u7ecf\u5178\u7684\u7ed8\u56fe\u5e93\u2014\u2014matplotlib\u3002<\/p>\n

\n

\u4e00\u3001\u4e3a\u4ec0\u4e48\u8981\u753b\u56fe<\/h3>\n

\u4eba\u7c7b\u5927\u8111\u5bf9\u56fe\u5f62\u7684\u611f\u77e5\u8fdc\u5feb\u4e8e\u5bf9\u6570\u5b57\u7684\u611f\u77e5\u3002\u8003\u8651\u8fd9\u4e24\u7ec4\u6570\u636e：<\/p>\n

A: [12, 15, 18, 22, 25, 30, 35, 40, 38, 42]
\nB: [42, 38, 40, 35, 30, 25, 22, 18, 15, 12]<\/p>\n

\u5149\u770b\u6570\u5b57，\u4f60\u80fd\u7acb\u523b\u8bf4\u51fa\u5b83\u4eec\u7684\u533a\u522b\u5417？\u4f46\u753b\u6210\u6298\u7ebf\u56fe，\u4e00\u4e2a\u4e0a\u5347\u4e00\u4e2a\u4e0b\u964d，\u4e00\u76ee\u4e86\u7136\u3002<\/p>\n

\u6570\u636e\u53ef\u89c6\u5316\u7684\u4ef7\u503c：<\/p>\n\n\u573a\u666f\u4f5c\u7528<\/tr>\n\n\n\n\n\n
\u63a2\u7d22\u6027\u5206\u6790<\/td>\n\u53d1\u73b0\u89c4\u5f8b\u3001\u5f02\u5e38\u3001\u8d8b\u52bf<\/td>\n<\/tr>\n
\u6c47\u62a5\u5c55\u793a<\/td>\n\u8ba9\u975e\u6280\u672f\u4eba\u5458\u770b\u61c2\u6570\u636e<\/td>\n<\/tr>\n
\u51b3\u7b56\u652f\u6301<\/td>\n\u7528\u6570\u636e\u9a71\u52a8\u51b3\u7b56<\/td>\n<\/tr>\n
\u76d1\u63a7\u9884\u8b66<\/td>\n\u5b9e\u65f6\u56fe\u8868\u53d1\u73b0\u5f02\u5e38<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e8c\u3001matplotlib \u7b80\u4ecb<\/h3>\n

matplotlib \u662f Python \u6700\u57fa\u7840\u3001\u6700\u5168\u9762\u7684\u7ed8\u56fe\u5e93，\u7531 John Hunter \u4e8e 2003 \u5e74\u5f00\u53d1\u3002\u51e0\u4e4e\u6240\u6709\u9ad8\u7ea7\u7ed8\u56fe\u5e93（seaborn\u3001pandas \u7684 plot）\u90fd\u57fa\u4e8e\u5b83\u3002<\/p>\n

\u6838\u5fc3\u67b6\u6784：<\/p>\n

Figure（\u753b\u5e03）
\n \u2514\u2500\u2500 Axes（\u5750\u6807\u7cfb\/\u5b50\u56fe）
\n \u2514\u2500\u2500 Artist（\u56fe\u5f62\u5143\u7d20：\u7ebf\u3001\u70b9\u3001\u6587\u5b57\u7b49）<\/p>\n

    \n
  • Figure：\u6574\u4e2a\u753b\u5e03，\u53ef\u4ee5\u5305\u542b\u591a\u4e2a\u5b50\u56fe\u3002<\/li>\n
  • Axes：\u4e00\u4e2a\u5750\u6807\u7cfb，\u6211\u4eec\u753b\u7684\u56fe\u5c31\u5728\u8fd9\u91cc（\u6ce8\u610f\u4e0d\u662f"\u8f74"axis）\u3002<\/li>\n
  • Artist：\u6240\u6709\u53ef\u89c1\u5143\u7d20，\u5982\u7ebf\u6761\u3001\u6807\u9898\u3001\u56fe\u4f8b\u3002<\/li>\n<\/ul>\n

    pylab vs pyplot：<\/p>\n

      \n
    • pylab：\u628a matplotlib \u548c numpy \u7684\u547d\u540d\u7a7a\u95f4\u5408\u5e76，\u7c7b\u4f3c MATLAB，\u73b0\u5df2\u4e0d\u63a8\u8350\u3002<\/li>\n
    • pyplot：\u63d0\u4f9b\u7c7b\u4f3c MATLAB \u7684\u7b80\u6d01\u63a5\u53e3，\u6700\u5e38\u7528，import matplotlib.pyplot as plt\u3002<\/li>\n<\/ul>\n
      \n

      \u4e09\u3001\u5b89\u88c5<\/h3>\n

      pip install matplotlib<\/p>\n

      \u9a8c\u8bc1：<\/p>\n

      import matplotlib.pyplot as plt
      \nimport matplotlib
      \nprint(matplotlib.__version__) # 3.7.2<\/p>\n

      \u5728 Jupyter Notebook \u4e2d，\u52a0\u4e0a\u00a0%matplotlib inline\u00a0\u5373\u53ef\u5728\u7f51\u9875\u5185\u663e\u793a\u56fe\u8868\u3002\u5728\u811a\u672c\u4e2d\u7528\u00a0plt.show()\u00a0\u5f39\u51fa\u7a97\u53e3\u3002<\/p>\n

      \n

      \u56db\u3001\u6298\u7ebf\u56fe plot<\/h3>\n

      4.1 \u57fa\u672c\u6298\u7ebf<\/h4>\n

      import matplotlib.pyplot as plt<\/p>\n

      x = [1, 2, 3, 4, 5]
      \ny = [2, 4, 6, 8, 10]<\/p>\n

      plt.plot(x, y)
      \nplt.show()<\/p>\n

      \u8fd9\u4f1a\u5f39\u51fa\u4e00\u4e2a\u7a97\u53e3，\u663e\u793a\u4e00\u6761\u4ece\u5de6\u4e0b\u5230\u53f3\u4e0a\u7684\u76f4\u7ebf\u3002<\/p>\n

      4.2 \u591a\u6761\u7ebf<\/h4>\n

      x = [1, 2, 3, 4, 5]
      \ny1 = [2, 4, 6, 8, 10]
      \ny2 = [1, 3, 5, 7, 9]<\/p>\n

      plt.plot(x, y1, label='y=2x')
      \nplt.plot(x, y2, label='y=2x-1')
      \nplt.legend() # \u663e\u793a\u56fe\u4f8b
      \nplt.show()<\/p>\n

      4.3 \u6837\u5f0f\u8bbe\u7f6e<\/h4>\n

      plt.plot(x, y1, color='red', linestyle='–', marker='o',
      \n linewidth=2, markersize=8, label='\u7ea2\u8272\u865a\u7ebf')
      \nplt.plot(x, y2, color='blue', linestyle='-', marker='s',
      \n linewidth=3, markersize=6, label='\u84dd\u8272\u5b9e\u7ebf')
      \nplt.legend()
      \nplt.show()<\/p>\n

      \u6837\u5f0f\u53c2\u6570\u8bf4\u660e：<\/p>\n\n\u53c2\u6570\u8bf4\u660e\u5e38\u7528\u503c<\/tr>\n\n\n\n\n\n\n
      color<\/td>\n\u989c\u8272<\/td>\n'red'\u3001'#FF0000'\u3001'r'<\/td>\n<\/tr>\n
      linestyle<\/td>\n\u7ebf\u578b<\/td>\n'-'\u3001'–'\u3001'-.'\u3001':'<\/td>\n<\/tr>\n
      marker<\/td>\n\u6807\u8bb0<\/td>\n'o'\u3001's'\u3001'^'\u3001'D'\u3001'*'<\/td>\n<\/tr>\n
      linewidth<\/td>\n\u7ebf\u5bbd<\/td>\n\u6570\u5b57<\/td>\n<\/tr>\n
      markersize<\/td>\n\u6807\u8bb0\u5927\u5c0f<\/td>\n\u6570\u5b57<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u7b80\u5199\u683c\u5f0f：'ro–'\u00a0\u8868\u793a\u7ea2\u8272 + \u5706\u5f62\u6807\u8bb0 + \u865a\u7ebf\u3002<\/p>\n

      plt.plot(x, y1, 'ro–', label='\u7b80\u5199')
      \nplt.show()<\/p>\n

      4.4 \u6807\u9898\u3001\u6807\u7b7e\u3001\u56fe\u4f8b\u3001\u7f51\u683c<\/h4>\n

      plt.plot(x, y1, label='\u9500\u91cf')
      \nplt.title('\u6708\u5ea6\u9500\u91cf\u8d8b\u52bf', fontsize=16)
      \nplt.xlabel('\u6708\u4efd', fontsize=12)
      \nplt.ylabel('\u9500\u91cf（\u4ef6）', fontsize=12)
      \nplt.legend(loc='upper left', fontsize=10)
      \nplt.grid(True, linestyle='–', alpha=0.5)
      \nplt.xlim(0, 6) # x\u8f74\u8303\u56f4
      \nplt.ylim(0, 12) # y\u8f74\u8303\u56f4
      \nplt.show()<\/p>\n

      4.5 \u4fdd\u5b58\u548c\u663e\u793a<\/h4>\n

      plt.plot(x, y1)
      \nplt.savefig('line.png', dpi=150, bbox_inches='tight') # \u4fdd\u5b58\u56fe\u7247
      \nplt.show()
      \n# plt.close() # \u5173\u95ed\u56fe\u5f62，\u91ca\u653e\u5185\u5b58<\/p>\n

      dpi\u00a0\u63a7\u5236\u5206\u8fa8\u7387，bbox_inches='tight'\u00a0\u53bb\u9664\u591a\u4f59\u767d\u8fb9\u3002<\/p>\n

      \n

      \u4e94\u3001\u67f1\u72b6\u56fe bar \u548c barh<\/h3>\n

      5.1 \u666e\u901a\u67f1\u72b6\u56fe<\/h4>\n

      categories = ['\u6280\u672f\u90e8', '\u5e02\u573a\u90e8', '\u8d22\u52a1\u90e8', '\u4eba\u4e8b\u90e8']
      \nvalues = [15, 12, 8, 6]<\/p>\n

      plt.bar(categories, values, color=['#4CAF50', '#2196F3', '#FF9800', '#F44336'])
      \nplt.title('\u5404\u90e8\u95e8\u4eba\u6570')
      \nplt.ylabel('\u4eba\u6570')
      \nfor i, v in enumerate(values):
      \n plt.text(i, v + 0.3, str(v), ha='center') # \u5728\u67f1\u5b50\u4e0a\u6807\u6ce8\u6570\u503c
      \nplt.show()<\/p>\n

      5.2 \u6a2a\u5411\u67f1\u72b6\u56fe<\/h4>\n

      plt.barh(categories, values, color='skyblue')
      \nplt.xlabel('\u4eba\u6570')
      \nplt.show()<\/p>\n

      5.3 \u5806\u53e0\u67f1\u72b6\u56fe<\/h4>\n

      men = [12, 15, 10, 8]
      \nwomen = [8, 10, 12, 6]<\/p>\n

      plt.bar(categories, men, label='\u7537', color='steelblue')
      \nplt.bar(categories, women, bottom=men, label='\u5973', color='pink')
      \nplt.legend()
      \nplt.title('\u5404\u90e8\u95e8\u7537\u5973\u4eba\u6570')
      \nplt.show()<\/p>\n

      5.4 \u5206\u7ec4\u67f1\u72b6\u56fe<\/h4>\n

      import numpy as np<\/p>\n

      x = np.arange(len(categories))
      \nwidth = 0.35<\/p>\n

      plt.bar(x – width\/2, men, width, label='\u7537', color='steelblue')
      \nplt.bar(x + width\/2, women, width, label='\u5973', color='pink')
      \nplt.xticks(x, categories)
      \nplt.legend()
      \nplt.title('\u5404\u90e8\u95e8\u7537\u5973\u5bf9\u6bd4')
      \nplt.show()<\/p>\n

      \n

      \u516d\u3001\u997c\u56fe pie<\/h3>\n

      sizes = [30, 25, 20, 15, 10]
      \nlabels = ['\u6280\u672f\u90e8', '\u5e02\u573a\u90e8', '\u8d22\u52a1\u90e8', '\u4eba\u4e8b\u90e8', '\u5176\u4ed6']
      \ncolors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#ffb3e6']
      \nexplode = (0.1, 0, 0, 0, 0) # \u7a81\u51fa\u7b2c\u4e00\u5757<\/p>\n

      plt.pie(sizes, explode=explode, labels=labels, colors=colors,
      \n autopct='%1.1f%%', shadow=True, startangle=90)
      \nplt.axis('equal') # \u5706\u5f62
      \nplt.title('\u90e8\u95e8\u5360\u6bd4')
      \nplt.show()<\/p>\n

      \u8fd0\u884c\u6548\u679c：\u00a0\u4e00\u4e2a\u997c\u56fe，\u6280\u672f\u90e8\u90a3\u4e00\u5757\u88ab\u62c9\u51fa\u6765\u7a81\u51fa\u663e\u793a，\u6bcf\u5757\u6807\u6ce8\u767e\u5206\u6bd4\u3002<\/p>\n

      \n

      \u4e03\u3001\u76f4\u65b9\u56fe hist<\/h3>\n

      \u76f4\u65b9\u56fe\u7528\u4e8e\u5c55\u793a\u6570\u636e\u5206\u5e03\u3002<\/p>\n

      import numpy as np<\/p>\n

      scores = np.random.normal(75, 10, 200) # \u751f\u6210200\u4e2a\u6b63\u6001\u5206\u5e03\u6210\u7ee9<\/p>\n

      plt.hist(scores, bins=20, color='skyblue', edgecolor='black', alpha=0.7)
      \nplt.title('\u8003\u8bd5\u6210\u7ee9\u5206\u5e03')
      \nplt.xlabel('\u5206\u6570')
      \nplt.ylabel('\u4eba\u6570')
      \nplt.axvline(np.mean(scores), color='red', linestyle='–', label=f'\u5e73\u5747\u5206{np.mean(scores):.1f}')
      \nplt.legend()
      \nplt.show()<\/p>\n

      \u8bf4\u660e：\u00a0bins=20\u00a0\u8868\u793a\u520620\u4e2a\u533a\u95f4，\u80fd\u770b\u51fa\u6210\u7ee9\u96c6\u4e2d\u5728 65-85 \u4e4b\u95f4，\u5448\u949f\u5f62\u5206\u5e03\u3002<\/p>\n

      \n

      \u516b\u3001\u6563\u70b9\u56fe scatter<\/h3>\n

      \u6563\u70b9\u56fe\u7528\u4e8e\u89c2\u5bdf\u4e24\u4e2a\u53d8\u91cf\u7684\u76f8\u5173\u6027\u3002<\/p>\n

      height = np.random.normal(170, 8, 100)
      \nweight = height * 0.6 + np.random.normal(-30, 5, 100)<\/p>\n

      plt.scatter(height, weight, c='purple', alpha=0.6, s=50)
      \nplt.title('\u8eab\u9ad8\u4e0e\u4f53\u91cd\u5173\u7cfb')
      \nplt.xlabel('\u8eab\u9ad8(cm)')
      \nplt.ylabel('\u4f53\u91cd(kg)')
      \nplt.show()<\/p>\n

      \u8bf4\u660e：\u00a0\u70b9\u4ece\u5de6\u4e0b\u5230\u53f3\u4e0a\u5206\u5e03，\u8bf4\u660e\u8eab\u9ad8\u548c\u4f53\u91cd\u6b63\u76f8\u5173\u3002<\/p>\n

      \u5e26\u989c\u8272\u6620\u5c04\u7684\u6563\u70b9\u56fe：<\/p>\n

      x = np.random.rand(100)
      \ny = np.random.rand(100)
      \ncolors = np.random.rand(100)
      \nsizes = 1000 * np.random.rand(100)<\/p>\n

      plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='viridis')
      \nplt.colorbar() # \u989c\u8272\u6761
      \nplt.show()<\/p>\n

      \n

      \u4e5d\u3001\u7bb1\u7ebf\u56fe box<\/h3>\n

      \u7bb1\u7ebf\u56fe\u7528\u4e8e\u68c0\u6d4b\u79bb\u7fa4\u503c（\u5f02\u5e38\u503c）\u3002<\/p>\n

      data = [np.random.normal(0, std, 100) for std in range(1, 5)]<\/p>\n

      plt.boxplot(data, labels=['A', 'B', 'C', 'D'])
      \nplt.title('\u5404\u7ec4\u6570\u636e\u5206\u5e03')
      \nplt.ylabel('\u503c')
      \nplt.show()<\/p>\n

      \u5982\u4f55\u770b\u7bb1\u7ebf\u56fe：<\/p>\n

        \n
      • \u7bb1\u5b50\u4e0a\u4e0b\u8fb9：\u4e0a\u4e0b\u56db\u5206\u4f4d\u6570（Q3\u3001Q1）<\/li>\n
      • \u7bb1\u5185\u6a2a\u7ebf：\u4e2d\u4f4d\u6570<\/li>\n
      • \u4e0a\u4e0b\u987b：\u6b63\u5e38\u8303\u56f4\u8fb9\u754c<\/li>\n
      • \u5706\u70b9：\u79bb\u7fa4\u503c（\u5f02\u5e38\u70b9）<\/li>\n<\/ul>\n
        \n

        \u5341\u3001\u5b50\u56fe subplots<\/h3>\n

        \u4e00\u5f20\u56fe\u91cc\u753b\u591a\u4e2a\u5b50\u56fe\u3002<\/p>\n

        fig, axes = plt.subplots(2, 2, figsize=(10, 8))<\/p>\n

        x = np.linspace(0, 10, 100)<\/p>\n

        axes[0, 0].plot(x, np.sin(x))
        \naxes[0, 0].set_title('sin(x)')<\/p>\n

        axes[0, 1].plot(x, np.cos(x), 'r')
        \naxes[0, 1].set_title('cos(x)')<\/p>\n

        axes[1, 0].plot(x, x**2)
        \naxes[1, 0].set_title('x\u00b2')<\/p>\n

        axes[1, 1].plot(x, np.log(x + 1), 'g')
        \naxes[1, 1].set_title('ln(x+1)')<\/p>\n

        plt.tight_layout() # \u81ea\u52a8\u8c03\u6574\u95f4\u8ddd
        \nplt.show()<\/p>\n

        \u5171\u4eab x \u8f74：<\/p>\n

        fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8, 6))
        \nax1.plot(x, np.sin(x))
        \nax2.plot(x, np.cos(x))
        \nplt.show()<\/p>\n

        \n

        \u5341\u4e00\u3001\u914d\u5408 pandas \u753b\u56fe<\/h3>\n

        pandas \u5185\u7f6e\u4e86\u00a0.plot()\u00a0\u65b9\u6cd5，\u5e95\u5c42\u8c03\u7528 matplotlib，\u66f4\u7b80\u6d01\u3002<\/p>\n

        import pandas as pd<\/p>\n

        df = pd.DataFrame({
        \n '\u6708\u4efd': ['1\u6708', '2\u6708', '3\u6708', '4\u6708', '5\u6708', '6\u6708'],
        \n '\u9500\u91cf': [120, 150, 180, 200, 170, 210],
        \n '\u5229\u6da6': [12, 18, 25, 30, 22, 35]
        \n})
        \ndf = df.set_index('\u6708\u4efd')<\/p>\n

        # \u6298\u7ebf\u56fe
        \ndf.plot(kind='line', marker='o')
        \nplt.title('\u4e0a\u534a\u5e74\u9500\u91cf\u4e0e\u5229\u6da6')
        \nplt.show()<\/p>\n

        # \u67f1\u72b6\u56fe
        \ndf.plot(kind='bar')
        \nplt.show()<\/p>\n

        # \u9762\u79ef\u56fe
        \ndf.plot(kind='area', alpha=0.5)
        \nplt.show()<\/p>\n

        # \u76f4\u65b9\u56fe
        \ndf['\u9500\u91cf'].plot(kind='hist', bins=10)
        \nplt.show()<\/p>\n

        kind \u53c2\u6570\u53ef\u9009\u503c：\u00a0line\u3001bar\u3001barh\u3001hist\u3001box\u3001area\u3001scatter\u3001pie\u3001kde\u3002<\/p>\n

        \n

        \u5341\u4e8c\u3001\u4e2d\u6587\u663e\u793a\u95ee\u9898<\/h3>\n

        matplotlib \u9ed8\u8ba4\u4e0d\u652f\u6301\u4e2d\u6587，\u6807\u9898\u4f1a\u663e\u793a\u4e3a\u65b9\u6846\u3002\u89e3\u51b3\u65b9\u6cd5：<\/p>\n

        macOS \u65b9\u6848<\/h4>\n

        import matplotlib.pyplot as plt
        \nplt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
        \nplt.rcParams['axes.unicode_minus'] = False # \u8d1f\u53f7\u6b63\u5e38\u663e\u793a<\/p>\n

        Windows \u65b9\u6848<\/h4>\n

        plt.rcParams['font.sans-serif'] = ['SimHei'] # \u9ed1\u4f53
        \nplt.rcParams['axes.unicode_minus'] = False<\/p>\n

        \u901a\u7528\u65b9\u6848（\u67e5\u627e\u7cfb\u7edf\u5b57\u4f53）<\/h4>\n

        from matplotlib import font_manager
        \nfonts = [f.name for f in font_manager.fontManager.ttflist]
        \nprint([f for f in set(fonts) if 'Hei' in f or 'Song' in f or 'Unicode' in f])<\/p>\n

        \u6d4b\u8bd5：<\/p>\n

        plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
        \nplt.rcParams['axes.unicode_minus'] = False<\/p>\n

        plt.plot([1, 2, 3], [4, 5, 6])
        \nplt.title('\u4e2d\u6587\u6807\u9898\u6b63\u5e38\u663e\u793a')
        \nplt.xlabel('\u6a2a\u8f74\u6807\u7b7e')
        \nplt.show()<\/p>\n

        \n

        \u5341\u4e09\u3001\u6837\u5f0f\u7f8e\u5316<\/h3>\n

        13.1 \u5185\u7f6e\u6837\u5f0f<\/h4>\n

        print(plt.style.available)
        \n# ['Solarize_Light2', 'seaborn-v0_8', 'ggplot', 'grayscale', 'dark_background', …]<\/p>\n

        plt.style.use('ggplot') # \u4f7f\u7528ggplot\u98ce\u683c
        \nplt.plot([1, 2, 3], [4, 5, 6])
        \nplt.show()<\/p>\n

        plt.style.use('dark_background') # \u6697\u8272\u80cc\u666f
        \nplt.plot([1, 2, 3], [4, 5, 6])
        \nplt.show()<\/p>\n

        13.2 seaborn \u98ce\u683c<\/h4>\n

        plt.style.use('seaborn-v0_8-darkgrid')
        \nplt.plot([1, 2, 3], [4, 5, 6])
        \nplt.show()<\/p>\n

        13.3 \u4e34\u65f6\u6837\u5f0f<\/h4>\n

        with plt.style.context('ggplot'):
        \n plt.plot([1, 2, 3], [4, 5, 6])
        \n plt.show()
        \n# \u9000\u51fawith\u540e\u6062\u590d\u9ed8\u8ba4<\/p>\n

        \n

        \u5341\u56db\u3001\u8fdb\u9636：seaborn \u7b80\u4ecb<\/h3>\n

        seaborn \u57fa\u4e8e matplotlib，\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868，\u9ed8\u8ba4\u6837\u5f0f\u66f4\u6f02\u4eae\u3002<\/p>\n

        pip install seaborn<\/p>\n

        import seaborn as sns
        \nimport pandas as pd
        \nimport matplotlib.pyplot as plt<\/p>\n

        # \u51c6\u5907\u6570\u636e
        \ndf = pd.DataFrame({
        \n 'department': ['\u6280\u672f', '\u6280\u672f', '\u5e02\u573a', '\u5e02\u573a', '\u8d22\u52a1', '\u8d22\u52a1'],
        \n 'salary': [8000, 9500, 12000, 11000, 15000, 14000]
        \n})<\/p>\n

        # \u7bb1\u7ebf\u56fe
        \nsns.boxplot(x='department', y='salary', data=df)
        \nplt.show()<\/p>\n

        # \u5c0f\u63d0\u7434\u56fe
        \nsns.violinplot(x='department', y='salary', data=df)
        \nplt.show()<\/p>\n

        \u70ed\u529b\u56fe<\/h4>\n

        import numpy as np
        \ndata = np.random.rand(5, 5)
        \nsns.heatmap(data, annot=True, cmap='YlOrRd')
        \nplt.title('\u70ed\u529b\u56fe')
        \nplt.show()<\/p>\n

        pairplot（\u591a\u53d8\u91cf\u5173\u7cfb\u77e9\u9635）<\/h4>\n

        iris = sns.load_dataset('iris')
        \nsns.pairplot(iris, hue='species')
        \nplt.show()<\/p>\n

        \n

        \u5341\u4e94\u3001\u8fdb\u9636：plotly \u4ea4\u4e92\u5f0f\u56fe\u8868<\/h3>\n

        plotly \u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f HTML \u56fe\u8868，\u652f\u6301\u7f29\u653e\u3001\u60ac\u505c\u67e5\u770b\u6570\u636e\u3002<\/p>\n

        pip install plotly<\/p>\n

        import plotly.express as px<\/p>\n

        df = px.data.gapminder().query("country in ['China', 'India', 'United States']")
        \nfig = px.line(df, x='year', y='gdpPercap', color='country',
        \n title='\u4eba\u5747GDP\u8d8b\u52bf')
        \nfig.show() # \u5728\u6d4f\u89c8\u5668\u4e2d\u6253\u5f00，\u53ef\u4ea4\u4e92<\/p>\n

        \u4f18\u52bf：\u00a0\u9f20\u6807\u60ac\u505c\u663e\u793a\u6570\u503c\u3001\u53ef\u7f29\u653e\u3001\u53ef\u5bfc\u51fa HTML \u5206\u4eab\u3002<\/p>\n

        \n

        \u5341\u516d\u3001\u5b9e\u6218\u6848\u4f8b：\u9500\u552e\u6570\u636e\u53ef\u89c6\u5316\u4eea\u8868\u76d8<\/h3>\n

        \u628a\u591a\u79cd\u56fe\u8868\u7ec4\u5408\u6210\u4e00\u4e2a\u4eea\u8868\u76d8\u3002<\/p>\n

        import matplotlib.pyplot as plt
        \nimport numpy as np
        \nimport pandas as pd<\/p>\n

        plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
        \nplt.rcParams['axes.unicode_minus'] = False<\/p>\n

        # \u6a21\u62df\u9500\u552e\u6570\u636e
        \nnp.random.seed(42)
        \nmonths = ['1\u6708', '2\u6708', '3\u6708', '4\u6708', '5\u6708', '6\u6708']
        \nsales = np.random.randint(100, 300, 6)
        \nproducts = ['\u624b\u673a', '\u7535\u8111', '\u5e73\u677f', '\u8033\u673a']
        \nproduct_sales = np.random.randint(50, 200, 4)
        \nregions = ['\u534e\u5317', '\u534e\u4e1c', '\u534e\u5357', '\u534e\u897f']
        \nregion_sales = np.random.randint(80, 250, 4)<\/p>\n

        # \u521b\u5efa2×2\u4eea\u8868\u76d8
        \nfig, axes = plt.subplots(2, 2, figsize=(12, 10))
        \nfig.suptitle('2024\u5e74\u4e0a\u534a\u5e74\u9500\u552e\u4eea\u8868\u76d8', fontsize=18, fontweight='bold')<\/p>\n

        # 1. \u6708\u5ea6\u8d8b\u52bf\u6298\u7ebf\u56fe
        \naxes[0, 0].plot(months, sales, 'o-', color='#2196F3', linewidth=2, markersize=8)
        \naxes[0, 0].fill_between(range(6), sales, alpha=0.2, color='#2196F3')
        \naxes[0, 0].set_title('\u6708\u5ea6\u9500\u552e\u989d\u8d8b\u52bf')
        \naxes[0, 0].set_ylabel('\u9500\u552e\u989d（\u4e07\u5143）')
        \nfor i, v in enumerate(sales):
        \n axes[0, 0].text(i, v + 5, str(v), ha='center')<\/p>\n

        # 2. \u4ea7\u54c1\u9500\u91cf\u67f1\u72b6\u56fe
        \naxes[0, 1].bar(products, product_sales, color=['#4CAF50', '#2196F3', '#FF9800', '#F44336'])
        \naxes[0, 1].set_title('\u5404\u4ea7\u54c1\u9500\u91cf')
        \naxes[0, 1].set_ylabel('\u9500\u91cf（\u4ef6）')<\/p>\n

        # 3. \u533a\u57df\u5360\u6bd4\u997c\u56fe
        \naxes[1, 0].pie(region_sales, labels=regions, autopct='%1.1f%%',
        \n colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99'])
        \naxes[1, 0].set_title('\u5404\u533a\u57df\u9500\u552e\u5360\u6bd4')<\/p>\n

        # 4. \u9500\u91cf\u5206\u5e03\u76f4\u65b9\u56fe
        \nall_sales = np.random.normal(150, 40, 200)
        \naxes[1, 1].hist(all_sales, bins=20, color='skyblue', edgecolor='black', alpha=0.7)
        \naxes[1, 1].axvline(np.mean(all_sales), color='red', linestyle='–', label='\u5e73\u5747\u503c')
        \naxes[1, 1].set_title('\u9500\u91cf\u5206\u5e03')
        \naxes[1, 1].set_xlabel('\u9500\u91cf')
        \naxes[1, 1].set_ylabel('\u9891\u6b21')
        \naxes[1, 1].legend()<\/p>\n

        plt.tight_layout()
        \nplt.savefig('dashboard.png', dpi=150, bbox_inches='tight')
        \nplt.show()
        \nprint("\u4eea\u8868\u76d8\u5df2\u4fdd\u5b58\u4e3a dashboard.png")<\/p>\n

        \u8fd0\u884c\u6548\u679c：\u00a0\u751f\u6210\u4e00\u5f20 2\u00d72 \u7684\u4eea\u8868\u76d8\u56fe\u7247，\u5305\u542b\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u3001\u76f4\u65b9\u56fe，\u6807\u9898\u4e3a"2024\u5e74\u4e0a\u534a\u5e74\u9500\u552e\u4eea\u8868\u76d8"，\u4fdd\u5b58\u4e3a\u00a0dashboard.png\u3002<\/p>\n

        \n

        \u5341\u4e03\u3001\u6700\u4f73\u5b9e\u8df5<\/h3>\n
      • \u5148\u60f3\u518d\u753b：\u660e\u786e\u8981\u8868\u8fbe\u4ec0\u4e48（\u8d8b\u52bf\u7528\u6298\u7ebf\u3001\u5bf9\u6bd4\u7528\u67f1\u72b6\u3001\u5360\u6bd4\u7528\u997c\u56fe\u3001\u5206\u5e03\u7528\u76f4\u65b9\u56fe）\u3002<\/li>\n
      • \u56fe\u8868\u7c7b\u578b\u9009\u62e9：\n
          \n
        • \u8d8b\u52bf\u53d8\u5316 \u2192 \u6298\u7ebf\u56fe<\/li>\n
        • \u5206\u7c7b\u5bf9\u6bd4 \u2192 \u67f1\u72b6\u56fe<\/li>\n
        • \u5360\u6bd4\u6784\u6210 \u2192 \u997c\u56fe<\/li>\n
        • \u6570\u636e\u5206\u5e03 \u2192 \u76f4\u65b9\u56fe\/\u7bb1\u7ebf\u56fe<\/li>\n
        • \u76f8\u5173\u5173\u7cfb \u2192 \u6563\u70b9\u56fe<\/li>\n<\/ul>\n<\/li>\n
        • \u5c11\u5373\u662f\u591a：\u4e00\u5f20\u56fe\u522b\u653e\u592a\u591a\u4fe1\u606f，\u5fc5\u8981\u65f6\u7528\u5b50\u56fe\u3002<\/li>\n
        • \u6807\u6ce8\u8981\u5168：\u6807\u9898\u3001\u8f74\u6807\u7b7e\u3001\u5355\u4f4d\u3001\u56fe\u4f8b\u4e0d\u80fd\u5c11\u3002<\/li>\n
        • \u914d\u8272\u534f\u8c03：\u7528\u8272\u76f2\u53cb\u597d\u914d\u8272，\u907f\u514d\u7ea2\u7eff\u540c\u65f6\u7528\u3002<\/li>\n
        • \u4fdd\u5b58\u7528 dpi：dpi=150\u00a0\u4ee5\u4e0a\u4fdd\u8bc1\u6e05\u6670\u3002<\/li>\n
        • \u4e2d\u6587\u8bbe\u7f6e\u653e\u6700\u524d：\u5728\u6240\u6709\u7ed8\u56fe\u4ee3\u7801\u4e4b\u524d\u8bbe\u7f6e\u5b57\u4f53\u3002<\/li>\n
        • \u4ea4\u4e92\u7528 plotly：\u9700\u8981\u7f29\u653e\u3001\u60ac\u505c\u65f6\u7528 plotly \u800c\u975e matplotlib\u3002<\/li>\n

          # \u914d\u8272\u65b9\u6848\u63a8\u8350
          \ncolors = ['#4CAF50', '#2196F3', '#FF9800', '#F44336', '#9C27B0']<\/p>\n

          \n

          <\/h3>\n","protected":false},"excerpt":{"rendered":"

          \u524d\u8a00：\u4e0a\u7bc7\u6211\u4eec\u5b66\u4f1a\u4e86\u7528 pandas \u5904\u7406\u8868\u683c\u6570\u636e，\u4f46\u8868\u683c\u91cc\u5168\u662f\u6570\u5b57，\u4e0d\u591f\u76f4\u89c2\u3002\u4fd7\u8bdd\u8bf4\\”\u4e00\u56fe\u80dc\u5343\u8a00\\”，\u6570\u636e\u53ef\u89c6\u5316\u80fd\u628a\u67af\u71e5\u7684\u6570\u5b57\u53d8\u6210\u76f4\u89c2\u7684\u56fe\u8868，\u8ba9\u4eba\u4e00\u773c\u770b\u51fa\u8d8b\u52bf\u3001\u5206\u5e03\u548c\u5f02\u5e38\u3002\u672c\u7bc7\u5e26\u4f60\u638c\u63e1 Python \u6700\u7ecf\u5178\u7684\u7ed8\u56fe\u5e93\u2014\u2014matplotlib\u3002\u4e00\u3001\u4e3a\u4ec0\u4e48\u8981\u753b\u56fe\u4eba\u7c7b\u5927\u8111\u5bf9\u56fe\u5f62\u7684\u611f\u77e5\u8fdc\u5feb\u4e8e\u5bf9\u6570\u5b57\u7684\u611f\u77e5\u3002\u8003\u8651\u8fd9\u4e24\u7ec4\u6570\u636e：A: [12, 15, 18, 22, 25, 30, 35, 40, 38, 42<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[13178,2468,81,1224],"topic":[],"class_list":["post-119819","post","type-post","status-publish","format-standard","hentry","category-server","tag-1024---","tag-matplotlib","tag-python","tag-1224"],"yoast_head":"\n\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/119819.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u524d\u8a00：\u4e0a\u7bc7\u6211\u4eec\u5b66\u4f1a\u4e86\u7528 pandas \u5904\u7406\u8868\u683c\u6570\u636e，\u4f46\u8868\u683c\u91cc\u5168\u662f\u6570\u5b57，\u4e0d\u591f\u76f4\u89c2\u3002\u4fd7\u8bdd\u8bf4"\u4e00\u56fe\u80dc\u5343\u8a00"，\u6570\u636e\u53ef\u89c6\u5316\u80fd\u628a\u67af\u71e5\u7684\u6570\u5b57\u53d8\u6210\u76f4\u89c2\u7684\u56fe\u8868，\u8ba9\u4eba\u4e00\u773c\u770b\u51fa\u8d8b\u52bf\u3001\u5206\u5e03\u548c\u5f02\u5e38\u3002\u672c\u7bc7\u5e26\u4f60\u638c\u63e1 Python \u6700\u7ecf\u5178\u7684\u7ed8\u56fe\u5e93\u2014\u2014matplotlib\u3002\u4e00\u3001\u4e3a\u4ec0\u4e48\u8981\u753b\u56fe\u4eba\u7c7b\u5927\u8111\u5bf9\u56fe\u5f62\u7684\u611f\u77e5\u8fdc\u5feb\u4e8e\u5bf9\u6570\u5b57\u7684\u611f\u77e5\u3002\u8003\u8651\u8fd9\u4e24\u7ec4\u6570\u636e：A: [12, 15, 18, 22, 25, 30, 35, 40, 38, 42\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/119819.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-30T06:24:49+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/119819.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/119819.html\",\"name\":\"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"isPartOf\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\"},\"datePublished\":\"2026-10-30T06:24:49+00:00\",\"dateModified\":\"2026-10-30T06:24:49+00:00\",\"author\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\"},\"breadcrumb\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/119819.html#breadcrumb\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.wsisp.com\/helps\/119819.html\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/119819.html#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u9996\u9875\",\"item\":\"https:\/\/www.wsisp.com\/helps\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\",\"url\":\"https:\/\/www.wsisp.com\/helps\/\",\"name\":\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"description\":\"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"zh-Hans\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-Hans\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"contentUrl\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"caption\":\"admin\"},\"sameAs\":[\"http:\/\/wp.wsisp.com\"],\"url\":\"https:\/\/www.wsisp.com\/helps\/author\/admin\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.wsisp.com\/helps\/119819.html","og_locale":"zh_CN","og_type":"article","og_title":"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","og_description":"\u524d\u8a00：\u4e0a\u7bc7\u6211\u4eec\u5b66\u4f1a\u4e86\u7528 pandas \u5904\u7406\u8868\u683c\u6570\u636e，\u4f46\u8868\u683c\u91cc\u5168\u662f\u6570\u5b57，\u4e0d\u591f\u76f4\u89c2\u3002\u4fd7\u8bdd\u8bf4\"\u4e00\u56fe\u80dc\u5343\u8a00\"，\u6570\u636e\u53ef\u89c6\u5316\u80fd\u628a\u67af\u71e5\u7684\u6570\u5b57\u53d8\u6210\u76f4\u89c2\u7684\u56fe\u8868，\u8ba9\u4eba\u4e00\u773c\u770b\u51fa\u8d8b\u52bf\u3001\u5206\u5e03\u548c\u5f02\u5e38\u3002\u672c\u7bc7\u5e26\u4f60\u638c\u63e1 Python \u6700\u7ecf\u5178\u7684\u7ed8\u56fe\u5e93\u2014\u2014matplotlib\u3002\u4e00\u3001\u4e3a\u4ec0\u4e48\u8981\u753b\u56fe\u4eba\u7c7b\u5927\u8111\u5bf9\u56fe\u5f62\u7684\u611f\u77e5\u8fdc\u5feb\u4e8e\u5bf9\u6570\u5b57\u7684\u611f\u77e5\u3002\u8003\u8651\u8fd9\u4e24\u7ec4\u6570\u636e：A: [12, 15, 18, 22, 25, 30, 35, 40, 38, 42","og_url":"https:\/\/www.wsisp.com\/helps\/119819.html","og_site_name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","article_published_time":"2026-10-30T06:24:49+00:00","author":"admin","twitter_card":"summary_large_image","twitter_misc":{"\u4f5c\u8005":"admin","\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4":"6 \u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.wsisp.com\/helps\/119819.html","url":"https:\/\/www.wsisp.com\/helps\/119819.html","name":"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","isPartOf":{"@id":"https:\/\/www.wsisp.com\/helps\/#website"},"datePublished":"2026-10-30T06:24:49+00:00","dateModified":"2026-10-30T06:24:49+00:00","author":{"@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41"},"breadcrumb":{"@id":"https:\/\/www.wsisp.com\/helps\/119819.html#breadcrumb"},"inLanguage":"zh-Hans","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.wsisp.com\/helps\/119819.html"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.wsisp.com\/helps\/119819.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u9996\u9875","item":"https:\/\/www.wsisp.com\/helps"},{"@type":"ListItem","position":2,"name":"\u3010Python\u3011\u57fa\u7840\u8bed\u6cd5\u5165\u95e8\uff08\u4e09\u5341\u4e94\uff09\u2014\u2014 \u6570\u636e\u53ef\u89c6\u5316\uff1a\u7528 matplotlib \u753b\u56fe"}]},{"@type":"WebSite","@id":"https:\/\/www.wsisp.com\/helps\/#website","url":"https:\/\/www.wsisp.com\/helps\/","name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","description":"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"zh-Hans"},{"@type":"Person","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41","name":"admin","image":{"@type":"ImageObject","inLanguage":"zh-Hans","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/","url":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","contentUrl":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","caption":"admin"},"sameAs":["http:\/\/wp.wsisp.com"],"url":"https:\/\/www.wsisp.com\/helps\/author\/admin"}]}},"_links":{"self":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/119819","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/comments?post=119819"}],"version-history":[{"count":0,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/119819\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/media?parent=119819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/categories?post=119819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/tags?post=119819"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/topic?post=119819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}