{"id":1060484,"date":"2024-12-31T15:34:26","date_gmt":"2024-12-31T07:34:26","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1060484.html"},"modified":"2024-12-31T15:34:28","modified_gmt":"2024-12-31T07:34:28","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8a%e6%95%b4%e5%bd%a2%e5%8f%98%e6%88%90%e6%b5%ae%e7%82%b9%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1060484.html","title":{"rendered":"python\u5982\u4f55\u628a\u6574\u5f62\u53d8\u6210\u6d6e\u70b9\u578b"},"content":{"rendered":"

\"python\u5982\u4f55\u628a\u6574\u5f62\u53d8\u6210\u6d6e\u70b9\u578b\"<\/p>\n

\u5c06\u6574\u5f62\u53d8\u6210\u6d6e\u70b9\u578b\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528 float()<\/code> \u51fd\u6570\u3001\u9664\u6cd5\u64cd\u4f5c\u7b26\u3001\u79d1\u5b66\u8ba1\u7b97\u5e93\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528 float()<\/code> \u51fd\u6570\u662f\u6700\u76f4\u63a5\u548c\u5e38\u7528\u7684\u65b9\u6cd5\u3002\u5b83\u4e0d\u4ec5\u7b80\u5355\u6613\u7528\uff0c\u800c\u4e14\u517c\u5bb9\u6027\u5f3a\uff0c\u9002\u5408\u5927\u591a\u6570\u5e94\u7528\u573a\u666f\u3002\u4f8b\u5982\uff0cfloat(5)<\/code> \u4f1a\u5c06\u6574\u5f62 5<\/code> \u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b 5.0<\/code>\u3002\u8fd9\u5728\u6570\u636e\u5904\u7406\u3001\u6570\u5b66\u8ba1\u7b97\u4ee5\u53ca\u79d1\u5b66\u8ba1\u7b97\u7b49\u9886\u57df\u975e\u5e38\u5e38\u89c1\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 float()<\/code> \u51fd\u6570<\/h3>\n<\/p>\n

\u4f7f\u7528 float()<\/code> \u51fd\u6570\u662f\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u3002\u5b83\u53ef\u4ee5\u5c06\u4efb\u4f55\u7c7b\u578b\u7684\u6570\u503c\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

integer_value = 10<\/p>\n
float_value = float(integer_value)<\/p>\n
print(float_value)  # \u8f93\u51fa: 10.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6574\u5f62\u6570\u503c\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u3002\u8fd9\u5728\u5904\u7406\u7528\u6237\u8f93\u5165\u3001\u6570\u636e\u8f6c\u6362\u548c\u8ba1\u7b97\u7b49\u573a\u666f\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u9664\u6cd5\u64cd\u4f5c\u7b26<\/h3>\n<\/p>\n
\u5728 Python 3 \u4e2d\uff0c\u4f7f\u7528\u9664\u6cd5\u64cd\u4f5c\u7b26 \/<\/code> \u4e5f\u53ef\u4ee5\u5c06\u6574\u5f62\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u3002\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n<\/p>\n
integer_value = 10<\/p>\n
float_value = integer_value \/ 1<\/p>\n
print(float_value)  # \u8f93\u51fa: 10.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u5176\u4ed6\u6570\u5b66\u64cd\u4f5c\u3002\u8fd9\u5728\u6570\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u5e38\u89c1\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u79d1\u5b66\u8ba1\u7b97\u5e93<\/h3>\n<\/p>\n
\u5728\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4e2d\uff0c\u901a\u5e38\u4f1a\u4f7f\u7528 NumPy \u548c Pandas \u7b49\u5e93\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u66f4\u4e3a\u4e30\u5bcc\u7684\u6570\u636e\u7c7b\u578b\u548c\u8f6c\u6362\u65b9\u6cd5\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
integer_value = 10<\/p>\n
float_value = np.float64(integer_value)<\/p>\n
print(float_value)  # \u8f93\u51fa: 10.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528 NumPy \u548c Pandas \u7b49\u5e93\uff0c\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u8fdb\u884c\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5904\u7406\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u7684\u6570\u636e\u5904\u7406\u4e2d\uff0c\u7ecf\u5e38\u9700\u8981\u5c06\u6574\u5f62\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u3002\u4f8b\u5982\uff0c\u5728\u5904\u7406\u4f20\u611f\u5668\u6570\u636e\u3001\u91d1\u878d\u6570\u636e\u548c\u7528\u6237\u8f93\u5165\u6570\u636e\u65f6\uff0c\u9700\u8981\u5c06\u6574\u5f62\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u4ee5\u8fdb\u884c\u66f4\u7cbe\u786e\u7684\u8ba1\u7b97\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
float_data = [float(i) for i in data]<\/p>\n
print(float_data)  # \u8f93\u51fa: [1.0, 2.0, 3.0, 4.0, 5.0]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u5217\u8868\u4e2d\u7684\u6240\u6709\u6574\u5f62\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\uff0c\u8fd9\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5728\u51fd\u6570\u548c\u65b9\u6cd5\u4e2d\u4f7f\u7528<\/h3>\n<\/p>\n
\u5728\u5b9a\u4e49\u51fd\u6570\u548c\u65b9\u6cd5\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570\u7c7b\u578b\u8f6c\u6362\u6765\u786e\u4fdd\u8f93\u5165\u6570\u636e\u7684\u7c7b\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
def calculate_area(radius):<\/p>\n
    radius = float(radius)<\/p>\n
    area = 3.14159 * (radius  2)<\/p>\n
    return area<\/p>\n
print(calculate_area(5))  # \u8f93\u51fa: 78.53975<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u786e\u4fdd\u51fd\u6570\u5728\u5904\u7406\u8f93\u5165\u6570\u636e\u65f6\u80fd\u591f\u6b63\u786e\u5730\u8fdb\u884c\u7c7b\u578b\u8f6c\u6362\u548c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u516d\u3001\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\uff0c\u6d6e\u70b9\u578b\u6570\u636e\u901a\u5e38\u7528\u4e8e\u8868\u793a\u6d4b\u91cf\u503c\u3001\u6982\u7387\u3001\u7edf\u8ba1\u6570\u636e\u7b49\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528 Pandas \u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7 astype()<\/code> \u65b9\u6cd5\u5c06\u6574\u5f62\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'value': [1, 2, 3, 4, 5]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['value'] = df['value'].astype(float)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u9700\u8981\u5c06\u6574\u5f62\u7279\u5f81\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u4ee5\u4fbf\u6a21\u578b\u80fd\u591f\u6b63\u786e\u5904\u7406\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528 Scikit-Learn \u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 StandardScaler<\/code> \u5c06\u6570\u636e\u6807\u51c6\u5316\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
data = [[1, 2], [3, 4], [5, 6]]<\/p>\n
scaler = StandardScaler()<\/p>\n
scaled_data = scaler.fit_transform(data)<\/p>\n
print(scaled_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u5728\u8f93\u5165\u6a21\u578b\u4e4b\u524d\u5df2\u7ecf\u8fc7\u6b63\u786e\u7684\u7c7b\u578b\u8f6c\u6362\u548c\u6807\u51c6\u5316\u3002<\/p>\n<\/p>\n
\u516b\u3001\u5728\u56fe\u5f62\u548c\u53ef\u89c6\u5316\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u56fe\u5f62\u548c\u53ef\u89c6\u5316\u4e2d\uff0c\u6d6e\u70b9\u578b\u6570\u636e\u901a\u5e38\u7528\u4e8e\u8868\u793a\u5750\u6807\u3001\u5c3a\u5bf8\u3001\u989c\u8272\u503c\u7b49\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528 Matplotlib \u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u6d6e\u70b9\u578b\u5750\u6807\u8f74\u6765\u7ed8\u5236\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [1.0, 4.0, 9.0, 16.0, 25.0]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.title('Example Plot')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u786e\u4fdd\u56fe\u5f62\u7ed8\u5236\u65f6\u4f7f\u7528\u51c6\u786e\u7684\u6d6e\u70b9\u578b\u6570\u636e\u8fdb\u884c\u5750\u6807\u8f74\u548c\u6570\u636e\u70b9\u7684\u8868\u793a\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u6d6e\u70b9\u6570\u7684\u5b58\u50a8\u548c\u8868\u793a<\/h3>\n<\/p>\n
\u6d6e\u70b9\u6570\u5728\u8ba1\u7b97\u673a\u5185\u90e8\u4ee5\u7279\u5b9a\u7684\u683c\u5f0f\u5b58\u50a8\u548c\u8868\u793a\u3002\u5728 Python \u4e2d\uff0c\u6d6e\u70b9\u6570\u9075\u5faa IEEE 754 \u6807\u51c6\uff0c\u4f7f\u7528\u53cc\u7cbe\u5ea6\uff0864 \u4f4d\uff09\u683c\u5f0f\u5b58\u50a8\u3002\u8fd9\u610f\u5473\u7740\u5728\u8fdb\u884c\u6d6e\u70b9\u6570\u8f6c\u6362\u65f6\u9700\u8981\u8003\u8651\u7cbe\u5ea6\u548c\u820d\u5165\u8bef\u5dee\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import sys<\/p>\n
print(sys.float_info)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u83b7\u53d6\u6709\u5173\u6d6e\u70b9\u6570\u8868\u793a\u548c\u7cbe\u5ea6\u7684\u4fe1\u606f\uff0c\u4ee5\u4fbf\u5728\u7f16\u5199\u4ee3\u7801\u65f6\u8fdb\u884c\u76f8\u5e94\u7684\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5c06\u6574\u5f62\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\u662f Python \u7f16\u7a0b\u4e2d\u5e38\u89c1\u7684\u64cd\u4f5c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u8fdb\u884c\u8f6c\u6362\u3002\u65e0\u8bba\u662f\u4f7f\u7528 float()<\/code> \u51fd\u6570\u3001\u9664\u6cd5\u64cd\u4f5c\u7b26\u3001\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u8fd8\u662f\u5728\u6570\u636e\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u8fdb\u884c\u7c7b\u578b\u8f6c\u6362\uff0c\u90fd\u9700\u8981\u8003\u8651\u6570\u636e\u7684\u7cbe\u5ea6\u548c\u8868\u793a\u65b9\u5f0f\u3002\u901a\u8fc7\u6b63\u786e\u5730\u8fdb\u884c\u7c7b\u578b\u8f6c\u6362\uff0c\u53ef\u4ee5\u786e\u4fdd\u7a0b\u5e8f\u5728\u5904\u7406\u6570\u636e\u65f6\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n
\u5728\u65e5\u5e38\u7f16\u7a0b\u4e2d\uff0c\u638c\u63e1\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7684\u6280\u5de7\u5bf9\u4e8e\u7f16\u5199\u9ad8\u6548\u3001\u5065\u58ee\u7684\u4ee3\u7801\u81f3\u5173\u91cd\u8981\u3002\u5e0c\u671b\u672c\u6587\u63d0\u4f9b\u7684\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528 Python \u4e2d\u7684\u6574\u5f62\u548c\u6d6e\u70b9\u578b\u8f6c\u6362\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684float()<\/code>\u51fd\u6570\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u3002\u4f8b\u5982\uff0c\u4f7f\u7528float(5)<\/code>\u53ef\u4ee5\u5c06\u6574\u65705\u8f6c\u6362\u4e3a\u6d6e\u70b9\u65705.0\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u63a5\uff0c\u9002\u7528\u4e8e\u4efb\u4f55\u6574\u6570\u3002<\/p>\n
\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u540e\u4f1a\u53d1\u751f\u4ec0\u4e48\uff1f<\/strong>
\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u540e\uff0c\u6570\u503c\u7684\u7c7b\u578b\u4f1a\u53d1\u751f\u53d8\u5316\uff0c\u8868\u793a\u65b9\u5f0f\u4e5f\u6709\u6240\u4e0d\u540c\u3002\u6d6e\u70b9\u6570\u80fd\u591f\u8868\u793a\u66f4\u5927\u7684\u8303\u56f4\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u4f1a\u5f15\u5165\u7cbe\u5ea6\u8bef\u5dee\u3002\u56e0\u6b64\uff0c\u5904\u7406\u975e\u5e38\u5927\u7684\u6574\u6570\u6216\u9700\u8981\u9ad8\u7cbe\u5ea6\u7684\u8ba1\u7b97\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u3002<\/p>\n
\u9664\u4e86\u4f7f\u7528float()\uff0c\u8fd8\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u6574\u6570\u5230\u6d6e\u70b9\u6570\u7684\u8f6c\u6362\u5417\uff1f<\/strong>
\u9664\u4e86float()<\/code>\u51fd\u6570\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.float32()<\/code>\u6216numpy.float64()<\/code>\u51fd\u6570\u6765\u8fdb\u884c\u8f6c\u6362\uff0c\u8fd9\u4e9b\u51fd\u6570\u5141\u8bb8\u60a8\u6307\u5b9a\u6d6e\u70b9\u6570\u7684\u7cbe\u5ea6\u3002\u6b64\u5916\uff0c\u76f4\u63a5\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\uff08\u5982\u9664\u6cd5\uff09\u4e5f\u4f1a\u81ea\u52a8\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\uff0c\u4f8b\u59825 \/ 2<\/code>\u5c06\u8fd4\u56de2.5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5c06\u6574\u5f62\u53d8\u6210\u6d6e\u70b9\u578b\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528 float() \u51fd\u6570\u3001\u9664\u6cd5\u64cd\u4f5c\u7b26\u3001\u79d1\u5b66\u8ba1\u7b97\u5e93\u7b49\u3002 \u5176\u4e2d\uff0c\u4f7f\u7528 float() […]","protected":false},"author":3,"featured_media":1060488,"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\/1060484"}],"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=1060484"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1060484\/revisions"}],"predecessor-version":[{"id":1060489,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1060484\/revisions\/1060489"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1060488"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1060484"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1060484"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1060484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}