{"id":940313,"date":"2024-12-26T20:57:36","date_gmt":"2024-12-26T12:57:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/940313.html"},"modified":"2024-12-26T20:57:38","modified_gmt":"2024-12-26T12:57:38","slug":"python%e5%a6%82%e4%bd%95%e5%a1%ab%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/940313.html","title":{"rendered":"python\u5982\u4f55\u586b\u8272"},"content":{"rendered":"

\"python\u5982\u4f55\u586b\u8272\"<\/p>\n

\u5728Python\u4e2d\u586b\u8272\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u591a\u4e2a\u56fe\u5f62\u5e93\uff0c\u5982matplotlib\u3001PIL\uff08Pillow\uff09\u548cOpenCV\u7b49\u3002Matplotlib\u662f\u6700\u5e38\u7528\u7684\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u586b\u8272\u529f\u80fd\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528fill_between()\u51fd\u6570\u6765\u586b\u5145\u4e24\u4e2a\u66f2\u7ebf\u4e4b\u95f4\u7684\u533a\u57df\uff0c\u4f7f\u7528bar()\u51fd\u6570\u4e3a\u6761\u5f62\u56fe\u7684\u6761\u5f62\u586b\u8272\uff0c\u6216\u4f7f\u7528scatter()\u51fd\u6570\u4e3a\u6563\u70b9\u56fe\u7684\u70b9\u586b\u8272\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528fill_between()\u51fd\u6570\u6765\u586b\u5145\u4e24\u4e2a\u66f2\u7ebf\u4e4b\u95f4\u7684\u533a\u57df\u662f\u975e\u5e38\u5e38\u89c1\u7684\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u586b\u8272\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u4e2d\u7684\u586b\u8272\u65b9\u6cd5<\/p>\n<\/p>\n

Matplotlib \u662f Python \u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u586b\u8272\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

    \n
  1. \u586b\u5145\u66f2\u7ebf\u4e4b\u95f4\u7684\u533a\u57df<\/li>\n<\/ol>\n

    \u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 fill_between()<\/code> \u51fd\u6570\u6765\u586b\u5145\u4e24\u6761\u66f2\u7ebf\u4e4b\u95f4\u7684\u533a\u57df\u3002\u8fd9\u5bf9\u4e8e\u5c55\u793a\u4e24\u4e2a\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u5dee\u5f02\u6216\u4e0d\u786e\u5b9a\u6027\u533a\u57df\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n

    import matplotlib.pyplot as plt<\/p>\n
    import numpy as np<\/p>\n
    x = np.linspace(0, 10, 100)<\/p>\n
    y1 = np.sin(x)<\/p>\n
    y2 = np.cos(x)<\/p>\n
    plt.fill_between(x, y1, y2, color='lightblue', alpha=0.5)<\/p>\n
    plt.plot(x, y1, label='sin(x)')<\/p>\n
    plt.plot(x, y2, label='cos(x)')<\/p>\n
    plt.legend()<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u586b\u5145\u4e86 sin(x)<\/code> \u548c cos(x)<\/code> \u66f2\u7ebf\u4e4b\u95f4\u7684\u533a\u57df\uff0c\u5e76\u4f7f\u7528 lightblue<\/code> \u4f5c\u4e3a\u586b\u5145\u989c\u8272\u3002<\/p>\n<\/p>\n
      \n
    1. \u4e3a\u6761\u5f62\u56fe\u586b\u8272<\/li>\n<\/ol>\n
      \u5728\u7ed8\u5236\u6761\u5f62\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 bar()<\/code> \u51fd\u6570\u7684 color<\/code> \u53c2\u6570\u4e3a\u6bcf\u4e2a\u6761\u5f62\u6307\u5b9a\u989c\u8272\u3002<\/p>\n<\/p>\n
      labels = ['A', 'B', 'C', 'D']<\/p>\n
      values = [3, 7, 2, 5]<\/p>\n
      plt.bar(labels, values, color=['red', 'blue', 'green', 'purple'])<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u91cc\uff0c\u6211\u4eec\u4e3a\u6bcf\u4e2a\u6761\u5f62\u6307\u5b9a\u4e86\u4e0d\u540c\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n
        \n
      1. \u4e3a\u6563\u70b9\u56fe\u586b\u8272<\/li>\n<\/ol>\n
        \u5728\u6563\u70b9\u56fe\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 scatter()<\/code> \u51fd\u6570\u7684 c<\/code> \u53c2\u6570\u4e3a\u6bcf\u4e2a\u70b9\u6307\u5b9a\u989c\u8272\u3002\u989c\u8272\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u67d0\u4e2a\u7279\u5f81\u53d8\u5316\u3002<\/p>\n<\/p>\n
        x = np.random.rand(50)<\/p>\n
        y = np.random.rand(50)<\/p>\n
        colors = np.random.rand(50)<\/p>\n
        plt.scatter(x, y, c=colors, cmap='viridis', alpha=0.6)<\/p>\n
        plt.colorbar()<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u70b9\u7684\u989c\u8272\u6839\u636e colors<\/code> \u6570\u7ec4\u7684\u503c\u53d8\u5316\uff0c\u5e76\u4f7f\u7528\u4e86 viridis<\/code> \u8272\u56fe\u3002<\/p>\n<\/p>\n
        \u4e8c\u3001PIL\uff08Pillow\uff09\u4e2d\u7684\u586b\u8272\u65b9\u6cd5<\/p>\n<\/p>\n
        Pillow \u662f Python \u4e2d\u7684\u4e00\u4e2a\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u548c\u7f16\u8f91\u56fe\u50cf\u3002<\/p>\n<\/p>\n
          \n
        1. \u521b\u5efa\u586b\u8272\u56fe\u50cf<\/li>\n<\/ol>\n
          \u4f60\u53ef\u4ee5\u4f7f\u7528 Pillow \u5e93\u4e2d\u7684 Image<\/code> \u6a21\u5757\u521b\u5efa\u4e00\u4e2a\u7eaf\u8272\u56fe\u50cf\u3002<\/p>\n<\/p>\n
          from PIL import Image<\/p>\n
          \u521b\u5efa\u4e00\u4e2a 100x100 \u7684\u7ea2\u8272\u56fe\u50cf<\/strong><\/h2>\n
          img = Image.new('RGB', (100, 100), color='red')<\/p>\n
          img.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u586b\u8272\u5f62\u72b6<\/li>\n<\/ol>\n
            Pillow \u7684 ImageDraw<\/code> \u6a21\u5757\u53ef\u4ee5\u7528\u6765\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u5f62\u72b6\uff0c\u5e76\u4e3a\u8fd9\u4e9b\u5f62\u72b6\u586b\u8272\u3002<\/p>\n<\/p>\n
            from PIL import Image, ImageDraw<\/p>\n
            img = Image.new('RGB', (200, 200), color='white')<\/p>\n
            draw = ImageDraw.Draw(img)<\/p>\n
            \u7ed8\u5236\u4e00\u4e2a\u586b\u5145\u7684\u84dd\u8272\u77e9\u5f62<\/strong><\/h2>\n
            draw.rectangle([50, 50, 150, 150], fill='blue', outline='black')<\/p>\n
            img.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001OPENCV\u4e2d\u7684\u586b\u8272\u65b9\u6cd5<\/p>\n<\/p>\n
            OpenCV \u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u4e5f\u652f\u6301\u56fe\u50cf\u7684\u586b\u8272\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
              \n
            1. \u4f7f\u7528 OpenCV \u521b\u5efa\u586b\u8272\u56fe\u50cf<\/li>\n<\/ol>\n
              import cv2<\/p>\n
              import numpy as np<\/p>\n
              \u521b\u5efa\u4e00\u4e2a 100x100 \u7684\u7ea2\u8272\u56fe\u50cf<\/strong><\/h2>\n
              img = np.zeros((100, 100, 3), dtype=np.uint8)<\/p>\n
              img[:] = (0, 0, 255)  # BGR \u683c\u5f0f\uff0c\u7ea2\u8272<\/p>\n
              cv2.imshow('Red Image', img)<\/p>\n
              cv2.wAI<\/a>tKey(0)<\/p>\n
              cv2.destroyAllWindows()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u586b\u8272\u5f62\u72b6<\/li>\n<\/ol>\n
                OpenCV \u63d0\u4f9b\u4e86\u591a\u79cd\u7ed8\u56fe\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u548c\u586b\u5145\u5f62\u72b6\u3002<\/p>\n<\/p>\n
                import cv2<\/p>\n
                import numpy as np<\/p>\n
                img = np.zeros((200, 200, 3), dtype=np.uint8)<\/p>\n
                \u7ed8\u5236\u4e00\u4e2a\u586b\u5145\u7684\u84dd\u8272\u77e9\u5f62<\/strong><\/h2>\n
                cv2.rectangle(img, (50, 50), (150, 150), (255, 0, 0), thickness=-1)  # BGR \u683c\u5f0f\uff0c\u84dd\u8272<\/p>\n
                cv2.imshow('Rectangle', img)<\/p>\n
                cv2.waitKey(0)<\/p>\n
                cv2.destroyAllWindows()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u4f7f\u7528SEABORN\u8fdb\u884c\u9ad8\u7ea7\u586b\u8272<\/p>\n<\/p>\n
                Seaborn \u662f\u57fa\u4e8e Matplotlib \u7684\u4e00\u4e2a\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u548c\u9ad8\u7ea7\u7684\u586b\u8272\u9009\u9879\u3002<\/p>\n<\/p>\n
                  \n
                1. \u4f7f\u7528 Seaborn \u7ed8\u5236\u5e26\u586b\u8272\u7684\u7edf\u8ba1\u56fe\u8868<\/li>\n<\/ol>\n
                  Seaborn \u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u5e26\u586b\u8272\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u5982\u6838\u5bc6\u5ea6\u4f30\u8ba1\u56fe\u548c\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
                  import seaborn as sns<\/p>\n
                  import numpy as np<\/p>\n
                  data = np.random.randn(1000)<\/p>\n
                  sns.histplot(data, kde=True, color='skyblue', fill=True)<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u4f7f\u7528 Seaborn \u7ed8\u5236\u5e26\u586b\u8272\u7684\u5206\u7c7b\u56fe\u8868<\/li>\n<\/ol>\n
                    Seaborn \u8fd8\u63d0\u4f9b\u4e86\u7528\u4e8e\u5206\u7c7b\u6570\u636e\u7684\u586b\u8272\u9009\u9879\uff0c\u5982\u5e26\u586b\u8272\u7684\u7bb1\u7ebf\u56fe\u548c\u5c0f\u63d0\u7434\u56fe\u3002<\/p>\n<\/p>\n
                    import seaborn as sns<\/p>\n
                    import pandas as pd<\/p>\n
                    \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                    data = pd.DataFrame({<\/p>\n
                        'Category': ['A', 'B', 'A', 'B'],<\/p>\n
                        'Value': [1, 2, 3, 4]<\/p>\n
                    })<\/p>\n
                    sns.boxplot(x='Category', y='Value', data=data, palette='pastel')<\/p>\n
                    plt.show()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u4e94\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/p>\n<\/p>\n
                    \u5728Python\u4e2d\u586b\u8272\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
                      \n
                    • Matplotlib<\/strong>\uff1a\u9002\u5408\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u586b\u8272\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u586b\u5145\u66f2\u7ebf\u3001\u6761\u5f62\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/li>\n
                    • Pillow<\/strong>\uff1a\u9002\u5408\u7528\u4e8e\u56fe\u50cf\u5904\u7406\uff0c\u63d0\u4f9b\u4e86\u521b\u5efa\u548c\u7f16\u8f91\u56fe\u50cf\u7684\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u9759\u6001\u56fe\u50cf\u7684\u586b\u8272\u3002<\/li>\n
                    • OpenCV<\/strong>\uff1a\u9002\u5408\u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u56fe\u50cf\u5904\u7406\u80fd\u529b\uff0c\u9002\u7528\u4e8e\u89c6\u9891\u548c\u52a8\u6001\u56fe\u50cf\u7684\u586b\u8272\u3002<\/li>\n
                    • Seaborn<\/strong>\uff1a\u57fa\u4e8eMatplotlib\uff0c\u9002\u5408\u7528\u4e8e\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u7684\u56fe\u8868\u548c\u586b\u8272\u9009\u9879\u3002<\/li>\n<\/ul>\n
                      \u6839\u636e\u4f60\u7684\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\uff0c\u4ee5\u5b9e\u73b0\u6700\u4f73\u7684\u586b\u8272\u6548\u679c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u7ec4\u5408\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u4ee5\u5b9e\u73b0\u66f4\u4e3a\u590d\u6742\u548c\u7cbe\u7f8e\u7684\u56fe\u5f62\u548c\u56fe\u50cf\u6548\u679c\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u5e93\u8fdb\u884c\u586b\u8272\u64cd\u4f5c\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u6709\u51e0\u4e2a\u6d41\u884c\u7684\u5e93\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u586b\u8272\u64cd\u4f5c\uff0c\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cPygame\u3002\u4f7f\u7528Matplotlib\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u53c2\u6570\u5728\u56fe\u5f62\u4e2d\u586b\u5145\u533a\u57df\uff1b\u800c\u5728Pygame\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9aRGB\u503c\u6765\u4e3a\u56fe\u50cf\u7684\u8868\u9762\u586b\u5145\u989c\u8272\u3002\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u4f7f\u7528\u65b9\u6cd5\u53ef\u4ee5\u5728\u5404\u81ea\u7684\u6587\u6863\u4e2d\u627e\u5230\u3002<\/p>\n
                      Python\u4e2d\u586b\u8272\u7684\u5e94\u7528\u573a\u666f\u6709\u54ea\u4e9b\uff1f<\/strong>
                      \u586b\u8272\u5728\u6570\u636e\u53ef\u89c6\u5316\u3001\u6e38\u620f\u5f00\u53d1\u548c\u56fe\u5f62\u8bbe\u8ba1\u4e2d\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u6bd4\u5982\uff0c\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\uff0c\u586b\u8272\u53ef\u4ee5\u5e2e\u52a9\u7a81\u51fa\u663e\u793a\u4e0d\u540c\u7684\u6570\u636e\u96c6\uff0c\u4f7f\u5f97\u56fe\u8868\u66f4\u52a0\u6613\u4e8e\u7406\u89e3\uff1b\u5728\u6e38\u620f\u5f00\u53d1\u4e2d\uff0c\u586b\u8272\u7528\u4e8e\u89d2\u8272\u548c\u573a\u666f\u7684\u6e32\u67d3\uff0c\u589e\u5f3a\u6e38\u620f\u4f53\u9a8c\uff1b\u5728\u56fe\u5f62\u8bbe\u8ba1\u4e2d\uff0c\u586b\u8272\u5219\u7528\u4e8e\u521b\u5efa\u89c6\u89c9\u5438\u5f15\u529b\u7684\u8bbe\u8ba1\u4f5c\u54c1\u3002<\/p>\n
                      \u6211\u9700\u8981\u4e86\u89e3\u54ea\u4e9b\u57fa\u7840\u77e5\u8bc6\u624d\u80fd\u5728Python\u4e2d\u8fdb\u884c\u586b\u8272\uff1f<\/strong>
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