{"id":993768,"date":"2024-12-27T08:48:42","date_gmt":"2024-12-27T00:48:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/993768.html"},"modified":"2024-12-27T08:48:44","modified_gmt":"2024-12-27T00:48:44","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e4%ba%ba%e7%be%a4%e6%95%b0%e9%87%8f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/993768.html","title":{"rendered":"Python\u5982\u4f55\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf"},"content":{"rendered":"

\"Python\u5982\u4f55\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\"<\/p>\n

Python\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\uff1a\u56fe\u50cf\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u3001\u4f20\u611f\u5668\u6570\u636e\u5904\u7406\u3002<\/strong> \u5728Python\u4e2d\uff0c\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u7684\u5b9e\u73b0\u53ef\u4ee5\u6d89\u53ca\u5230\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u6570\u636e\u5206\u6790\u7b49\u591a\u4e2a\u9886\u57df\u3002\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u5982OpenCV\u548c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u80fd\u591f\u4ece\u6444\u50cf\u5934\u6355\u83b7\u7684\u89c6\u9891\u6d41\u4e2d\u68c0\u6d4b\u548c\u8ba1\u6570\u4eba\u7fa4\u3002\u6570\u636e\u5206\u6790\u6280\u672f\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u6765\u81ea\u4f20\u611f\u5668\u6216\u8005\u65e5\u5fd7\u7684\u6570\u636e\uff0c\u6bd4\u5982WiFi\u4fe1\u53f7\u3001GPS\u6570\u636e\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\u7684\u4e00\u79cd\uff1a\u5229\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u6765\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u56fe\u50cf\u5904\u7406\u6280\u672f<\/p>\n<\/p>\n

\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\uff0c\u5c24\u5176\u662f\u5728\u76d1\u63a7\u7cfb\u7edf\u4e2d\u3002\u4f7f\u7528Python\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u901a\u5e38\u4f1a\u7528\u5230OpenCV\u5e93\u548c\u4e00\u4e9b\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002<\/p>\n<\/p>\n

    \n
  1. OpenCV\u57fa\u7840\u5e94\u7528<\/strong><\/li>\n<\/ol>\n

    OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u901a\u8fc7OpenCV\uff0c\u53ef\u4ee5\u8bfb\u53d6\u89c6\u9891\u6d41\uff0c\u5bf9\u5e27\u8fdb\u884c\u5904\u7406\uff0c\u7136\u540e\u68c0\u6d4b\u548c\u8ba1\u6570\u4eba\u7fa4\u3002<\/p>\n<\/p>\n

    \u9996\u5148\uff0c\u5b89\u88c5OpenCV\u5e93\uff1a<\/p>\n<\/p>\n

    pip install opencv-python<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u7136\u540e\uff0c\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u89c6\u9891\u6d41\u5e76\u663e\u793a\uff1a<\/p>\n<\/p>\n
    import cv2<\/p>\n
    cap = cv2.VideoCapture('video.mp4')<\/p>\n
    while cap.isOpened():<\/p>\n
        ret, frame = cap.read()<\/p>\n
        if not ret:<\/p>\n
            break<\/p>\n
        cv2.imshow('Video', frame)<\/p>\n
        if cv2.wAI<\/a>tKey(1) & 0xFF == ord('q'):<\/p>\n
            break<\/p>\n
    cap.release()<\/p>\n
    cv2.destroyAllWindows()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u8fdb\u884c\u4eba\u7fa4\u68c0\u6d4b<\/strong><\/li>\n<\/ol>\n
      \u4e3a\u4e86\u51c6\u786e\u68c0\u6d4b\u4eba\u7fa4\uff0c\u53ef\u4ee5\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u6bd4\u5982MobileNet-SSD\u3001YOLO\u7b49\u3002\u8fd9\u4e9b\u6a21\u578b\u53ef\u4ee5\u5feb\u901f\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u4eba\uff0c\u5e76\u6807\u8bb0\u51fa\u6765\u3002<\/p>\n<\/p>\n
      \u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528YOLO\u8fdb\u884c\u4eba\u7fa4\u68c0\u6d4b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      import cv2<\/p>\n
      import numpy as np<\/p>\n
      net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')<\/p>\n
      layer_names = net.getLayerNames()<\/p>\n
      output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]<\/p>\n
      cap = cv2.VideoCapture('video.mp4')<\/p>\n
      while cap.isOpened():<\/p>\n
          ret, frame = cap.read()<\/p>\n
          if not ret:<\/p>\n
              break<\/p>\n
          height, width, channels = frame.shape<\/p>\n
          blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)<\/p>\n
          net.setInput(blob)<\/p>\n
          outs = net.forward(output_layers)<\/p>\n
          class_ids = []<\/p>\n
          confidences = []<\/p>\n
          boxes = []<\/p>\n
          for out in outs:<\/p>\n
              for detection in out:<\/p>\n
                  scores = detection[5:]<\/p>\n
                  class_id = np.argmax(scores)<\/p>\n
                  confidence = scores[class_id]<\/p>\n
                  if confidence > 0.5:<\/p>\n
                      center_x = int(detection[0] * width)<\/p>\n
                      center_y = int(detection[1] * height)<\/p>\n
                      w = int(detection[2] * width)<\/p>\n
                      h = int(detection[3] * height)<\/p>\n
                      x = int(center_x - w \/ 2)<\/p>\n
                      y = int(center_y - h \/ 2)<\/p>\n
                      boxes.append([x, y, w, h])<\/p>\n
                      confidences.append(float(confidence))<\/p>\n
                      class_ids.append(class_id)<\/p>\n
          indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)<\/p>\n
          for i in range(len(boxes)):<\/p>\n
              if i in indexes:<\/p>\n
                  x, y, w, h = boxes[i]<\/p>\n
                  label = str(class_ids[i])<\/p>\n
                  cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)<\/p>\n
          cv2.imshow('Image', frame)<\/p>\n
          if cv2.waitKey(1) & 0xFF == ord('q'):<\/p>\n
              break<\/p>\n
      cap.release()<\/p>\n
      cv2.destroyAllWindows()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u6b64\u4ee3\u7801\u4e2d\uff0cYOLO\u6a21\u578b\u88ab\u7528\u4e8e\u68c0\u6d4b\u89c6\u9891\u4e2d\u7684\u4eba\u3002\u901a\u8fc7\u8bfb\u53d6\u6743\u91cd\u6587\u4ef6\u548c\u914d\u7f6e\u6587\u4ef6\uff0c\u6a21\u578b\u53ef\u4ee5\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u4eba\u5e76\u7ed8\u5236\u8fb9\u754c\u6846\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u6570\u636e\u5206\u6790\u65b9\u6cd5<\/p>\n<\/p>\n
      \u9664\u4e86\u56fe\u50cf\u5904\u7406\u4e4b\u5916\uff0c\u6570\u636e\u5206\u6790\u4e5f\u662f\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u7684\u91cd\u8981\u65b9\u6cd5\u4e4b\u4e00\u3002\u6b64\u65b9\u6cd5\u4e3b\u8981\u7528\u4e8e\u5206\u6790\u4ece\u5404\u79cd\u4f20\u611f\u5668\u6216\u8005\u65e5\u5fd7\u6570\u636e\u4e2d\u63d0\u53d6\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
        \n
      1. WiFi\u4fe1\u53f7\u5206\u6790<\/strong><\/li>\n<\/ol>\n
        \u901a\u8fc7\u5206\u6790WiFi\u4fe1\u53f7\uff0c\u53ef\u4ee5\u4f30\u8ba1\u67d0\u4e00\u533a\u57df\u5185\u7684\u4eba\u6570\u3002\u6bcf\u4e2a\u79fb\u52a8\u8bbe\u5907\u90fd\u4f1a\u5b9a\u671f\u53d1\u9001WiFi\u4fe1\u53f7\uff0c\u6355\u6349\u5230\u8fd9\u4e9b\u4fe1\u53f7\u53ef\u4ee5\u5e2e\u52a9\u4f30\u7b97<\/a>\u4eba\u7fa4\u6570\u91cf\u3002<\/p>\n<\/p>\n
          \n
        1. GPS\u6570\u636e\u5206\u6790<\/strong><\/li>\n<\/ol>\n
          \u5728\u4e00\u4e9b\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528GPS\u6570\u636e\u6765\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\uff0c\u5c24\u5176\u662f\u5728\u5927\u578b\u6d3b\u52a8\u4e2d\u3002\u901a\u8fc7\u5206\u6790\u624b\u673a\u7684GPS\u6570\u636e\uff0c\u53ef\u4ee5\u8ffd\u8e2a\u4eba\u7fa4\u7684\u79fb\u52a8\u548c\u5206\u5e03\u3002<\/p>\n<\/p>\n
          \u4e09\u3001\u4f20\u611f\u5668\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n
          \u4f20\u611f\u5668\u6570\u636e\u5904\u7406\u662f\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u3002\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u7528\u4e8e\u667a\u80fd\u5efa\u7b51\u548c\u667a\u80fd\u57ce\u5e02\u4e2d\u3002<\/p>\n<\/p>\n
            \n
          1. \u7ea2\u5916\u4f20\u611f\u5668<\/strong><\/li>\n<\/ol>\n
            \u7ea2\u5916\u4f20\u611f\u5668\u53ef\u4ee5\u7528\u4e8e\u68c0\u6d4b\u7ecf\u8fc7\u67d0\u4e2a\u5165\u53e3\u6216\u901a\u9053\u7684\u4eba\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u800c\u6709\u6548\uff0c\u9002\u7528\u4e8e\u95e8\u53e3\u3001\u8d70\u5eca\u7b49\u72ed\u7a84\u533a\u57df\u3002<\/p>\n<\/p>\n
              \n
            1. \u70ed\u6210\u50cf<\/strong><\/li>\n<\/ol>\n
              \u70ed\u6210\u50cf\u6280\u672f\u5229\u7528\u4eba\u4f53\u7684\u70ed\u91cf\u7279\u5f81\u8fdb\u884c\u68c0\u6d4b\uff0c\u80fd\u591f\u5728\u4f4e\u5149\u73af\u5883\u4e0b\u53d1\u6325\u4f5c\u7528\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u591c\u95f4\u76d1\u63a7\u6216\u4f4e\u5149\u73af\u5883\u4e0b\u7684\u4eba\u7fa4\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
              \u56db\u3001\u7efc\u5408\u5e94\u7528\u4e0e\u6311\u6218<\/p>\n<\/p>\n
                \n
              1. \u7ed3\u5408\u591a\u79cd\u6280\u672f<\/strong><\/li>\n<\/ol>\n
                \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u7ed3\u5408\u591a\u79cd\u6280\u672f\u6765\u63d0\u9ad8\u4eba\u7fa4\u7edf\u8ba1\u7684\u51c6\u786e\u6027\u3002\u4f8b\u5982\uff0c\u7ed3\u5408\u56fe\u50cf\u5904\u7406\u548cWiFi\u4fe1\u53f7\u5206\u6790\uff0c\u53ef\u4ee5\u540c\u65f6\u5229\u7528\u89c6\u9891\u76d1\u63a7\u548c\u4fe1\u53f7\u6570\u636e\u6765\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6311\u6218\u4e0e\u89e3\u51b3\u65b9\u6848<\/strong><\/li>\n<\/ol>\n
                  \u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u9762\u4e34\u8bf8\u591a\u6311\u6218\uff0c\u5982\u906e\u6321\u3001\u5149\u7167\u53d8\u5316\u3001\u8bbe\u5907\u9650\u5236\u7b49\u3002\u4e3a\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u53ef\u4ee5\u91c7\u7528\u591a\u6444\u50cf\u5934\u5e03\u7f6e\u3001\u4f7f\u7528\u66f4\u5148\u8fdb\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3001\u7ed3\u5408\u591a\u79cd\u6570\u636e\u6e90\u7b49\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
                  \u4e94\u3001\u672a\u6765\u53d1\u5c55\u65b9\u5411<\/p>\n<\/p>\n
                  \u968f\u7740\u6280\u672f\u7684\u8fdb\u6b65\uff0c\u4eba\u7fa4\u7edf\u8ba1\u6280\u672f\u5c06\u53d8\u5f97\u66f4\u52a0\u7cbe\u786e\u548c\u667a\u80fd\u5316\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u672a\u6765\u7684\u53d1\u5c55\u65b9\u5411\uff1a<\/p>\n<\/p>\n
                    \n
                  1. \u66f4\u9ad8\u6548\u7684\u7b97\u6cd5<\/strong><\/li>\n<\/ol>\n
                    \u7814\u53d1\u66f4\u9ad8\u6548\u7684\u7b97\u6cd5\uff0c\u4ee5\u4fbf\u5b9e\u65f6\u5904\u7406\u5927\u91cf\u6570\u636e\uff0c\u63d0\u9ad8\u4eba\u7fa4\u7edf\u8ba1\u7684\u901f\u5ea6\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u8fb9\u7f18\u8ba1\u7b97<\/strong><\/li>\n<\/ol>\n
                      \u901a\u8fc7\u8fb9\u7f18\u8ba1\u7b97\uff0c\u53ef\u4ee5\u5728\u8bbe\u5907\u7aef\u76f4\u63a5\u5904\u7406\u6570\u636e\uff0c\u51cf\u5c11\u4f20\u8f93\u5ef6\u8fdf\uff0c\u63d0\u9ad8\u5b9e\u65f6\u6027\u548c\u6570\u636e\u5b89\u5168\u6027\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u6570\u636e\u878d\u5408<\/strong><\/li>\n<\/ol>\n
                        \u7ed3\u5408\u591a\u79cd\u6570\u636e\u6e90\uff0c\u5305\u62ec\u89c6\u89c9\u3001\u97f3\u9891\u3001\u4f20\u611f\u5668\u6570\u636e\u7b49\uff0c\u5b9e\u73b0\u66f4\u5168\u9762\u7684\u4eba\u7fa4\u7edf\u8ba1\u548c\u884c\u4e3a\u5206\u6790\u3002<\/p>\n<\/p>\n
                        \u7efc\u4e0a\u6240\u8ff0\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\uff0c\u5305\u62ec\u56fe\u50cf\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u548c\u4f20\u611f\u5668\u6570\u636e\u5904\u7406\u7b49\u3002\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u65b9\u6cd5\u6216\u7ed3\u5408\u591a\u79cd\u6280\u672f\u6765\u8fbe\u5230\u6700\u4f73\u6548\u679c\u3002\u968f\u7740\u6280\u672f\u7684\u4e0d\u65ad\u8fdb\u6b65\uff0c\u4eba\u7fa4\u7edf\u8ba1\u5c06\u53d8\u5f97\u66f4\u52a0\u667a\u80fd\u5316\u548c\u9ad8\u6548\uff0c\u4e3a\u667a\u6167\u57ce\u5e02\u3001\u667a\u80fd\u5efa\u7b51\u7b49\u9886\u57df\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002<\/p>\n<\/p>\n
                        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                         \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6\u548c\u5206\u6790\u4eba\u7fa4\u6570\u636e\uff1f<\/strong>
                        \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u8bfb\u53d6\u548c\u5206\u6790\u4eba\u7fa4\u6570\u636e\uff0c\u4f8b\u5982Pandas\u548cNumPy\u3002\u9996\u5148\uff0c\u901a\u8fc7Pandas\u8bfb\u53d6CSV\u6216Excel\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u7136\u540e\u5229\u7528\u6570\u636e\u5206\u6790\u529f\u80fd\u8fdb\u884c\u7edf\u8ba1\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528groupby()<\/code>\u65b9\u6cd5\u6309\u4e0d\u540c\u7279\u5f81\u5206\u7ec4\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\uff0c\u6216\u8005\u4f7f\u7528value_counts()<\/code>\u65b9\u6cd5\u83b7\u53d6\u7279\u5b9a\u5c5e\u6027\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n
                        \u662f\u5426\u53ef\u4ee5\u5229\u7528Python\u8fdb\u884c\u5b9e\u65f6\u4eba\u7fa4\u76d1\u6d4b\uff1f<\/strong>
                        \u662f\u7684\uff0cPython\u652f\u6301\u901a\u8fc7\u4e0e\u4f20\u611f\u5668\u6216\u6444\u50cf\u5934\u8fde\u63a5\u8fdb\u884c\u5b9e\u65f6\u4eba\u7fa4\u76d1\u6d4b\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u5904\u7406\u89c6\u9891\u6d41\uff0c\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6765\u8bc6\u522b\u548c\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u3002\u6b64\u5916\uff0c\u4f7f\u7528Flask\u6216Django\u7b49Web\u6846\u67b6\uff0c\u53ef\u4ee5\u5c06\u76d1\u6d4b\u7ed3\u679c\u5b9e\u65f6\u5c55\u793a\u5728\u7f51\u9875\u4e0a\uff0c\u4fbf\u4e8e\u5b9e\u65f6\u76d1\u63a7\u548c\u5206\u6790\u3002<\/p>\n
                        Python\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u4eba\u7fa4\u7edf\u8ba1\u7684\u53ef\u89c6\u5316\uff1f<\/strong>
                        \u4e3a\u4e86\u5c06\u4eba\u7fa4\u7edf\u8ba1\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\uff0cPython\u6709\u591a\u4e2a\u5f3a\u5927\u7684\u5e93\u53ef\u4f9b\u9009\u62e9\u3002Matplotlib\u548cSeaborn\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u67f1\u72b6\u56fe\u548c\u997c\u56fe\uff0c\u5e2e\u52a9\u5c55\u793a\u4eba\u7fa4\u7684\u6570\u91cf\u548c\u5206\u5e03\u60c5\u51b5\u3002Plotly\u5219\u63d0\u4f9b\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u9f20\u6807\u60ac\u505c\u7b49\u65b9\u5f0f\u83b7\u53d6\u66f4\u8be6\u7ec6\u7684\u6570\u636e\uff0c\u589e\u5f3a\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u7edf\u8ba1\u4eba\u7fa4\u6570\u91cf\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\uff1a\u56fe\u50cf\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u3001\u4f20\u611f\u5668\u6570\u636e\u5904\u7406\u3002 \u5728Python\u4e2d\uff0c\u7edf\u8ba1\u4eba\u7fa4\u6570 […]","protected":false},"author":3,"featured_media":993775,"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\/993768"}],"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=993768"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/993768\/revisions"}],"predecessor-version":[{"id":993777,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/993768\/revisions\/993777"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/993775"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=993768"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=993768"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=993768"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}