{"id":1163506,"date":"2025-01-15T14:56:03","date_gmt":"2025-01-15T06:56:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1163506.html"},"modified":"2025-01-15T14:56:07","modified_gmt":"2025-01-15T06:56:07","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e6%97%a5%e6%9c%9f%e7%9a%84%e5%b9%b4%e4%bb%bd","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1163506.html","title":{"rendered":"python\u5982\u4f55\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd"},"content":{"rendered":"

\"python\u5982\u4f55\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd\"<\/p>\n

Python\u63d0\u53d6\u65e5\u671f\u5e74\u4efd\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u3001pandas<\/code> \u5e93\u3001\u5b57\u7b26\u4e32\u64cd\u4f5c\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u89e3\u6790\u65e5\u671f\u5e76\u63d0\u53d6\u5e74\u4efd\u3001\u4f7f\u7528 pandas<\/code> \u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3001\u901a\u8fc7\u5b57\u7b26\u4e32\u5207\u7247\u63d0\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n

\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u89e3\u6790\u65e5\u671f\u5e76\u63d0\u53d6\u5e74\u4efd<\/strong> \u662f\u4e00\u79cd\u5e38\u89c1\u4e14\u7b80\u4fbf\u7684\u65b9\u6cd5\u3002\u9996\u5148\u9700\u8981\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u89e3\u6790\u4e3a datetime<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u901a\u8fc7 year<\/code> \u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u8be6\u7ec6\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

from datetime import datetime<\/p>\n
\u5047\u8bbe\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u4e3a 'YYYY-MM-DD'<\/strong><\/h2>\n
date_str = '2023-10-11'<\/p>\n
date_obj = datetime.strptime(date_str, '%Y-%m-%d')<\/p>\n
year = date_obj.year<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cdatetime.strptime<\/code> \u65b9\u6cd5\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u89e3\u6790\u4e3a datetime<\/code> \u5bf9\u8c61\uff0c\u968f\u540e\u901a\u8fc7 year<\/code> \u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\u3002\u8be5\u65b9\u6cd5\u9002\u7528\u4e8e\u6807\u51c6\u65e5\u671f\u683c\u5f0f\uff0c\u5e76\u4e14\u6613\u4e8e\u7406\u89e3\u548c\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
\n
\u4e00\u3001\u4f7f\u7528 datetime<\/code> \u6a21\u5757<\/h3>\n<\/p>\n
1\u3001\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd<\/h4>\n<\/p>\n
datetime<\/code> \u6a21\u5757\u662fPython\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206\uff0c\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u7684 strptime<\/code> \u65b9\u6cd5\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u89e3\u6790\u4e3a datetime<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u901a\u8fc7\u8bbf\u95ee\u5176 year<\/code> \u5c5e\u6027\u6765\u63d0\u53d6\u5e74\u4efd\u3002\u4ee5\u4e0b\u662f\u66f4\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from datetime import datetime<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32<\/strong><\/h2>\n
date_str = '2023-10-11'<\/p>\n
\u4f7f\u7528 strptime \u65b9\u6cd5\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32<\/strong><\/h2>\n
date_obj = datetime.strptime(date_str, '%Y-%m-%d')<\/p>\n
\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
year = date_obj.year<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cstrptime<\/code> \u65b9\u6cd5\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u65e5\u671f\u5b57\u7b26\u4e32\u7684\u683c\u5f0f\u8bf4\u660e\u7b26\u3002%Y<\/code> \u8868\u793a\u56db\u4f4d\u6570\u7684\u5e74\u4efd\uff0c%m<\/code> \u8868\u793a\u4e24\u4f4d\u6570\u7684\u6708\u4efd\uff0c%d<\/code> \u8868\u793a\u4e24\u4f4d\u6570\u7684\u65e5\u671f\u3002\u8be5\u65b9\u6cd5\u8fd4\u56de\u4e00\u4e2a datetime<\/code> \u5bf9\u8c61\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbf\u95ee year<\/code> \u5c5e\u6027\u6765\u83b7\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
2\u3001\u5904\u7406\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32<\/h4>\n<\/p>\n
\u65e5\u671f\u5b57\u7b26\u4e32\u53ef\u80fd\u6709\u4e0d\u540c\u7684\u683c\u5f0f\uff0c\u4f8b\u5982 '11\/10\/2023'<\/code> \u6216 '2023\u5e7410\u670811\u65e5'<\/code>\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574 strptime<\/code> \u65b9\u6cd5\u7684\u683c\u5f0f\u8bf4\u660e\u7b26\u6765\u89e3\u6790\u8fd9\u4e9b\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\uff1a'11\/10\/2023'<\/p>\n
date_str = '11\/10\/2023'<\/p>\n
date_obj = datetime.strptime(date_str, '%d\/%m\/%Y')<\/p>\n
year = date_obj.year<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\uff1a'2023\u5e7410\u670811\u65e5'<\/strong><\/h2>\n
date_str = '2023\u5e7410\u670811\u65e5'<\/p>\n
date_obj = datetime.strptime(date_str, '%Y\u5e74%m\u6708%d\u65e5')<\/p>\n
year = date_obj.year<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u683c\u5f0f\u8bf4\u660e\u7b26\uff0c\u6211\u4eec\u53ef\u4ee5\u89e3\u6790\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 pandas<\/code> \u5e93<\/h3>\n<\/p>\n
1\u3001\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5e76\u63d0\u53d6\u5e74\u4efd<\/h4>\n<\/p>\n
pandas<\/code> \u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5e93\uff0c\u7279\u522b\u64c5\u957f\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 pandas<\/code> \u5e93\u4e2d\u7684 to_datetime<\/code> \u65b9\u6cd5\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u89e3\u6790\u4e3a datetime<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u901a\u8fc7\u8bbf\u95ee dt.year<\/code> \u5c5e\u6027\u6765\u63d0\u53d6\u5e74\u4efd\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32<\/strong><\/h2>\n
date_str = '2023-10-11'<\/p>\n
\u4f7f\u7528 to_datetime \u65b9\u6cd5\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32<\/strong><\/h2>\n
date_obj = pd.to_datetime(date_str)<\/p>\n
\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
year = date_obj.year<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cto_datetime<\/code> \u65b9\u6cd5\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u89e3\u6790\u4e3a Timestamp<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u901a\u8fc7\u8bbf\u95ee year<\/code> \u5c5e\u6027\u6765\u83b7\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
2\u3001\u5904\u7406\u5305\u542b\u591a\u4e2a\u65e5\u671f\u7684 Series<\/code> \u5bf9\u8c61<\/h4>\n<\/p>\n
pandas<\/code> \u5e93\u7684\u53e6\u4e00\u4e2a\u4f18\u52bf\u662f\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u5305\u542b\u591a\u4e2a\u65e5\u671f\u7684 Series<\/code> \u5bf9\u8c61\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4ece Series<\/code> \u5bf9\u8c61\u4e2d\u63d0\u53d6\u5e74\u4efd\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\u5217\u8868<\/strong><\/h2>\n
date_list = ['2023-10-11', '2022-05-20', '2021-12-31']<\/p>\n
\u521b\u5efa Series \u5bf9\u8c61<\/strong><\/h2>\n
date_series = pd.Series(date_list)<\/p>\n
\u4f7f\u7528 to_datetime \u65b9\u6cd5\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32<\/strong><\/h2>\n
date_series = pd.to_datetime(date_series)<\/p>\n
\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
years = date_series.dt.year<\/p>\n
print("\u63d0\u53d6\u7684\u5e74\u4efd\u5217\u8868\u662f:")<\/p>\n
print(years)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u65e5\u671f\u5b57\u7b26\u4e32\u7684 Series<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528 to_datetime<\/code> \u65b9\u6cd5\u5c06\u5176\u89e3\u6790\u4e3a datetime<\/code> \u5bf9\u8c61\u3002\u6700\u540e\uff0c\u901a\u8fc7\u8bbf\u95ee dt.year<\/code> \u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\uff0c\u5e76\u5f97\u5230\u4e00\u4e2a\u5305\u542b\u5e74\u4efd\u7684 Series<\/code> \u5bf9\u8c61\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c<\/h3>\n<\/p>\n
1\u3001\u901a\u8fc7\u5b57\u7b26\u4e32\u5207\u7247\u63d0\u53d6\u5e74\u4efd<\/h4>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5982\u679c\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u56fa\u5b9a\u4e14\u7b80\u5355\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u5b57\u7b26\u4e32\u5207\u7247\u6765\u63d0\u53d6\u5e74\u4efd\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u975e\u5e38\u89c4\u6574\u7684\u60c5\u51b5\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32<\/p>\n
date_str = '2023-10-11'<\/p>\n
\u63d0\u53d6\u5e74\u4efd\uff08\u5047\u8bbe\u65e5\u671f\u683c\u5f0f\u4e3a 'YYYY-MM-DD'\uff09<\/strong><\/h2>\n
year = date_str[:4]<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5047\u8bbe\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u4e3a YYYY-MM-DD<\/code>\uff0c\u901a\u8fc7\u5b57\u7b26\u4e32\u5207\u7247 date_str[:4]<\/code> \u63d0\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
2\u3001\u5904\u7406\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32<\/h4>\n<\/p>\n
\u5982\u679c\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u4e0d\u540c\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u5b57\u7b26\u4e32\u5207\u7247\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u4f4d\u7f6e\u6765\u63d0\u53d6\u5e74\u4efd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\uff1a'11\/10\/2023'<\/p>\n
date_str = '11\/10\/2023'<\/p>\n
year = date_str[-4:]<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\uff1a'2023\u5e7410\u670811\u65e5'<\/strong><\/h2>\n
date_str = '2023\u5e7410\u670811\u65e5'<\/p>\n
year = date_str[:4]<\/p>\n
print(f"\u63d0\u53d6\u7684\u5e74\u4efd\u662f: {year}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u5b57\u7b26\u4e32\u5207\u7247\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u4f4d\u7f6e\uff0c\u6211\u4eec\u53ef\u4ee5\u5904\u7406\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u4f7f\u7528\u4e0d\u540c\u65b9\u6cd5<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u683c\u5f0f\uff0c\u53ef\u4ee5\u7efc\u5408\u4f7f\u7528\u4e0a\u8ff0\u4e0d\u540c\u7684\u65b9\u6cd5\u6765\u63d0\u53d6\u5e74\u4efd\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5148\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u5bf9\u65e5\u671f\u5b57\u7b26\u4e32\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7136\u540e\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u6216 pandas<\/code> \u5e93\u8fdb\u884c\u8fdb\u4e00\u6b65\u89e3\u6790\u548c\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7efc\u5408\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from datetime import datetime<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\u5217\u8868\uff08\u4e0d\u540c\u683c\u5f0f\uff09<\/strong><\/h2>\n
date_list = ['2023-10-11', '11\/10\/2023', '2023\u5e7410\u670811\u65e5']<\/p>\n
\u9884\u5904\u7406\u65e5\u671f\u5b57\u7b26\u4e32\uff08\u7edf\u4e00\u683c\u5f0f\uff09<\/strong><\/h2>\n
processed_dates = []<\/p>\n
for date_str in date_list:<\/p>\n
    if '\/' in date_str:<\/p>\n
        # \u5904\u7406\u683c\u5f0f 'DD\/MM\/YYYY'<\/p>\n
        date_obj = datetime.strptime(date_str, '%d\/%m\/%Y')<\/p>\n
    elif '\u5e74' in date_str:<\/p>\n
        # \u5904\u7406\u683c\u5f0f 'YYYY\u5e74MM\u6708DD\u65e5'<\/p>\n
        date_obj = datetime.strptime(date_str, '%Y\u5e74%m\u6708%d\u65e5')<\/p>\n
    else:<\/p>\n
        # \u5904\u7406\u683c\u5f0f 'YYYY-MM-DD'<\/p>\n
        date_obj = datetime.strptime(date_str, '%Y-%m-%d')<\/p>\n
    processed_dates.append(date_obj)<\/p>\n
\u8f6c\u6362\u4e3a pandas Series \u5bf9\u8c61<\/strong><\/h2>\n
date_series = pd.Series(processed_dates)<\/p>\n
\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
years = date_series.dt.year<\/p>\n
print("\u63d0\u53d6\u7684\u5e74\u4efd\u5217\u8868\u662f:")<\/p>\n
print(years)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bf9\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7edf\u4e00\u8f6c\u6362\u4e3a datetime<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u5c06\u5176\u8f6c\u6362\u4e3a pandas<\/code> \u7684 Series<\/code> \u5bf9\u8c61\uff0c\u901a\u8fc7\u8bbf\u95ee dt.year<\/code> \u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\u3002\u8fd9\u79cd\u65b9\u6cd5\u7ed3\u5408\u4e86\u5b57\u7b26\u4e32\u64cd\u4f5c\u3001datetime<\/code> \u6a21\u5757\u548c pandas<\/code> \u5e93\u7684\u4f18\u52bf\uff0c\u80fd\u591f\u5904\u7406\u591a\u79cd\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5904\u7406\u5f02\u5e38\u60c5\u51b5<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u65e5\u671f\u5b57\u7b26\u4e32\u53ef\u80fd\u5305\u542b\u65e0\u6548\u6216\u5f02\u5e38\u503c\u3002\u4e3a\u4e86\u63d0\u9ad8\u4ee3\u7801\u7684\u5065\u58ee\u6027\u548c\u53ef\u9760\u6027\uff0c\u6211\u4eec\u9700\u8981\u5904\u7406\u8fd9\u4e9b\u5f02\u5e38\u60c5\u51b5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5904\u7406\u5f02\u5e38\u60c5\u51b5\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528 try-except<\/code> \u5904\u7406\u5f02\u5e38<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 try-except<\/code> \u8bed\u53e5\u6765\u6355\u83b7\u548c\u5904\u7406\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u65f6\u53ef\u80fd\u51fa\u73b0\u7684\u5f02\u5e38\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from datetime import datetime<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\u5217\u8868\uff08\u5305\u542b\u65e0\u6548\u503c\uff09<\/strong><\/h2>\n
date_list = ['2023-10-11', '11\/10\/2023', '\u65e0\u6548\u65e5\u671f', '2023\u5e7410\u670811\u65e5']<\/p>\n
\u9884\u5904\u7406\u65e5\u671f\u5b57\u7b26\u4e32\uff08\u7edf\u4e00\u683c\u5f0f\uff09<\/strong><\/h2>\n
processed_dates = []<\/p>\n
for date_str in date_list:<\/p>\n
    try:<\/p>\n
        if '\/' in date_str:<\/p>\n
            # \u5904\u7406\u683c\u5f0f 'DD\/MM\/YYYY'<\/p>\n
            date_obj = datetime.strptime(date_str, '%d\/%m\/%Y')<\/p>\n
        elif '\u5e74' in date_str:<\/p>\n
            # \u5904\u7406\u683c\u5f0f 'YYYY\u5e74MM\u6708DD\u65e5'<\/p>\n
            date_obj = datetime.strptime(date_str, '%Y\u5e74%m\u6708%d\u65e5')<\/p>\n
        else:<\/p>\n
            # \u5904\u7406\u683c\u5f0f 'YYYY-MM-DD'<\/p>\n
            date_obj = datetime.strptime(date_str, '%Y-%m-%d')<\/p>\n
        processed_dates.append(date_obj)<\/p>\n
    except ValueError:<\/p>\n
        # \u5904\u7406\u65e0\u6548\u65e5\u671f\u5b57\u7b26\u4e32<\/p>\n
        print(f"\u65e0\u6548\u65e5\u671f\u5b57\u7b26\u4e32: {date_str}")<\/p>\n
        processed_dates.append(None)<\/p>\n
\u8f6c\u6362\u4e3a pandas Series \u5bf9\u8c61<\/strong><\/h2>\n
date_series = pd.Series(processed_dates)<\/p>\n
\u63d0\u53d6\u5e74\u4efd\uff08\u6392\u9664 None \u503c\uff09<\/strong><\/h2>\n
years = date_series.dropna().dt.year<\/p>\n
print("\u63d0\u53d6\u7684\u5e74\u4efd\u5217\u8868\u662f:")<\/p>\n
print(years)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 try-except<\/code> \u8bed\u53e5\u6355\u83b7\u548c\u5904\u7406\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u65f6\u53ef\u80fd\u51fa\u73b0\u7684 ValueError<\/code> \u5f02\u5e38\u3002\u5982\u679c\u9047\u5230\u65e0\u6548\u7684\u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u6211\u4eec\u5c06\u5176\u5904\u7406\u4e3a None<\/code>\u3002\u6700\u540e\uff0c\u901a\u8fc7 dropna<\/code> \u65b9\u6cd5\u6392\u9664 None<\/code> \u503c\uff0c\u63d0\u53d6\u6709\u6548\u65e5\u671f\u7684\u5e74\u4efd\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528 pd.to_datetime<\/code> \u7684 errors<\/code> \u53c2\u6570<\/h4>\n<\/p>\n
pandas<\/code> \u5e93\u7684 pd.to_datetime<\/code> \u65b9\u6cd5\u63d0\u4f9b\u4e86 errors<\/code> \u53c2\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u65e0\u6548\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u65e5\u671f\u5b57\u7b26\u4e32\u5217\u8868\uff08\u5305\u542b\u65e0\u6548\u503c\uff09<\/strong><\/h2>\n
date_list = ['2023-10-11', '11\/10\/2023', '\u65e0\u6548\u65e5\u671f', '2023\u5e7410\u670811\u65e5']<\/p>\n
\u4f7f\u7528 to_datetime \u65b9\u6cd5\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u5904\u7406\u65e0\u6548\u503c<\/strong><\/h2>\n
date_series = pd.to_datetime(date_list, errors='coerce')<\/p>\n
\u63d0\u53d6\u5e74\u4efd\uff08\u6392\u9664 NaT \u503c\uff09<\/strong><\/h2>\n
years = date_series.dropna().year<\/p>\n
print("\u63d0\u53d6\u7684\u5e74\u4efd\u5217\u8868\u662f:")<\/p>\n
print(years)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 pd.to_datetime<\/code> \u65b9\u6cd5\u7684 errors<\/code> \u53c2\u6570\u5c06\u65e0\u6548\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u5904\u7406\u4e3a NaT<\/code>\uff08Not a Time\uff09\uff0c\u7136\u540e\u901a\u8fc7 dropna<\/code> \u65b9\u6cd5\u6392\u9664 NaT<\/code> \u503c\uff0c\u63d0\u53d6\u6709\u6548\u65e5\u671f\u7684\u5e74\u4efd\u3002<\/p>\n<\/p>\n
\u516d\u3001\u5e94\u7528\u573a\u666f\u548c\u5b9e\u9645\u6848\u4f8b<\/h3>\n<\/p>\n
1\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4e2d\uff0c\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd\u662f\u5e38\u89c1\u7684\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u5728\u5206\u6790\u9500\u552e\u6570\u636e\u65f6\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u6309\u5e74\u4efd\u6c47\u603b\u9500\u552e\u989d\uff0c\u7ed8\u5236\u5e74\u5ea6\u9500\u552e\u8d8b\u52bf\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528 pandas<\/code> \u5e93\u63d0\u53d6\u5e74\u4efd\u5e76\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u9500\u552e\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'date': ['2023-01-15', '2023-02-20', '2022-11-30', '2021-05-25', '2021-12-31'],<\/p>\n
    'sales': [1500, 2000, 1800, 1700, 1900]<\/p>\n
}<\/p>\n
\u521b\u5efa DataFrame \u5bf9\u8c61<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
df['year'] = df['date'].dt.year<\/p>\n
\u6309\u5e74\u4efd\u6c47\u603b\u9500\u552e\u989d<\/strong><\/h2>\n
yearly_sales = df.groupby('year')['sales'].sum()<\/p>\n
\u7ed8\u5236\u5e74\u5ea6\u9500\u552e\u8d8b\u52bf\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
yearly_sales.plot(kind='bar')<\/p>\n
plt.title('\u5e74\u5ea6\u9500\u552e\u8d8b\u52bf\u56fe')<\/p>\n
plt.xlabel('\u5e74\u4efd')<\/p>\n
plt.ylabel('\u9500\u552e\u989d')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u5b9e\u9645\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u65e5\u671f\u548c\u9500\u552e\u6570\u636e\u7684 DataFrame<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd\u3002\u901a\u8fc7\u6309\u5e74\u4efd\u6c47\u603b\u9500\u552e\u989d\uff0c\u6211\u4eec\u7ed8\u5236\u4e86\u5e74\u5ea6\u9500\u552e\u8d8b\u52bf\u56fe\uff0c\u5c55\u793a\u4e86\u4e0d\u540c\u5e74\u4efd\u7684\u9500\u552e\u989d\u53d8\u5316\u3002<\/p>\n<\/p>\n
2\u3001\u65e5\u5fd7\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u65e5\u5fd7\u5206\u6790\u4e2d\uff0c\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u6309\u5e74\u4efd\u7edf\u8ba1\u548c\u5206\u6790\u65e5\u5fd7\u6570\u636e\u3002\u4f8b\u5982\uff0c\u5728\u5206\u6790\u670d\u52a1\u5668\u65e5\u5fd7\u65f6\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u6309\u5e74\u4efd\u7edf\u8ba1\u8bf7\u6c42\u6b21\u6570\u3001\u9519\u8bef\u6b21\u6570\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528 pandas<\/code> \u5e93\u63d0\u53d6\u5e74\u4efd\u5e76\u8fdb\u884c\u65e5\u5fd7\u5206\u6790\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u670d\u52a1\u5668\u65e5\u5fd7\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'timestamp': ['2023-01-15 08:45:00', '2023-02-20 14:30:00', '2022-11-30 22:15:00', '2021-05-25 09:00:00', '2021-12-31 23:59:59'],<\/p>\n
    'request': ['GET \/index.html', 'POST \/login', 'GET \/about', 'GET \/contact', 'GET \/index.html']<\/p>\n
}<\/p>\n
\u521b\u5efa DataFrame \u5bf9\u8c61<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u89e3\u6790\u65f6\u95f4\u6233\u5e76\u63d0\u53d6\u5e74\u4efd<\/strong><\/h2>\n
df['timestamp'] = pd.to_datetime(df['timestamp'])<\/p>\n
df['year'] = df['timestamp'].dt.year<\/p>\n
\u6309\u5e74\u4efd\u7edf\u8ba1\u8bf7\u6c42\u6b21\u6570<\/strong><\/h2>\n
yearly_requests = df['year'].value_counts().sort_index()<\/p>\n
print("\u6309\u5e74\u4efd\u7edf\u8ba1\u8bf7\u6c42\u6b21\u6570:")<\/p>\n
print(yearly_requests)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u5b9e\u9645\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u65f6\u95f4\u6233\u548c\u8bf7\u6c42\u6570\u636e\u7684 DataFrame<\/code> \u5bf9\u8c61\uff0c\u7136\u540e\u89e3\u6790\u65f6\u95f4\u6233\u5e76\u63d0\u53d6\u5e74\u4efd\u3002\u901a\u8fc7\u6309\u5e74\u4efd\u7edf\u8ba1\u8bf7\u6c42\u6b21\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u4e86\u89e3\u4e0d\u540c\u5e74\u4efd\u7684\u8bf7\u6c42\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86Python\u63d0\u53d6\u65e5\u671f\u5e74\u4efd\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u3001pandas<\/code> \u5e93\u3001\u5b57\u7b26\u4e32\u64cd\u4f5c\u7b49\u3002\u6211\u4eec\u901a\u8fc7\u5b9e\u9645\u6848\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u6570\u636e\u5206\u6790\u548c\u65e5\u5fd7\u5206\u6790\u4e2d\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002\u65e0\u8bba\u662f\u5904\u7406\u5355\u4e2a\u65e5\u671f\u5b57\u7b26\u4e32\uff0c\u8fd8\u662f\u5904\u7406\u5305\u542b\u591a\u4e2a\u65e5\u671f\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u90fd\u80fd\u591f\u6709\u6548\u5730\u63d0\u53d6\u5e74\u4efd\uff0c\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u4f7f\u7528 datetime<\/code> \u6a21\u5757\u89e3\u6790\u65e5\u671f\u5e76\u63d0\u53d6\u5e74\u4efd<\/strong> \u662f\u4e00\u79cd\u5e38\u89c1\u4e14\u7b80\u4fbf\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u6807\u51c6\u65e5\u671f\u683c\u5f0f\u3002<\/p>\n<\/p>\n
\u4f7f\u7528 pandas<\/code> \u5e93\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong> \u662f\u4e00\u79cd\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u5b57\u7b26\u4e32\u5207\u7247\u63d0\u53d6\u5e74\u4efd<\/strong> \u9002\u7528\u4e8e\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u56fa\u5b9a\u4e14\u7b80\u5355\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
\u7efc\u5408\u4f7f\u7528\u4e0d\u540c\u65b9\u6cd5<\/strong> \u53ef\u4ee5\u5904\u7406\u591a\u79cd\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u5e76\u63d0\u53d6\u5e74\u4efd\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u5065\u58ee\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u683c\u5f0f\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u5904\u7406\u5f02\u5e38\u60c5\u51b5\uff0c\u80fd\u591f\u6709\u6548\u5730\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd\uff0c\u5e76\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u65e5\u5fd7\u5206\u6790\u7b49\u9886\u57df\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4ece\u65e5\u671f\u5b57\u7b26\u4e32\u4e2d\u63d0\u53d6\u5e74\u4efd\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528datetime<\/code>\u6a21\u5757\u6765\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3adatetime<\/code>\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528.year<\/code>\u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528strptime<\/code>\u51fd\u6570\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61\uff0c\u63a5\u7740\u76f4\u63a5\u8bbf\u95ee\u5e74\u4efd\u5c5e\u6027\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
from datetime import datetime\n\ndate_str = "2023-10-12"\ndate_obj = datetime.strptime(date_str, "%Y-%m-%d")\nyear = date_obj.year\nprint(year)  # \u8f93\u51fa\uff1a2023\n<\/code><\/pre>\n
\u662f\u5426\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\u6765\u63d0\u53d6\u65e5\u671f\u7684\u5e74\u4efd\uff1f<\/strong>
\u662f\u7684\uff0c\u9664\u4e86datetime<\/code>\u6a21\u5757\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u65e5\u671f\u6570\u636e\u3002\u5982\u679c\u60a8\u6709\u4e00\u4e2a\u5305\u542b\u65e5\u671f\u7684DataFrame\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528dt.year<\/code>\u5c5e\u6027\u63d0\u53d6\u5e74\u4efd\u3002\u793a\u4f8b\uff1a<\/p>\n
import pandas as pd\n\ndata = {'date': ['2023-10-12', '2022-05-15']}\ndf = pd.DataFrame(data)\ndf['date'] = pd.to_datetime(df['date'])\ndf['year'] = df['date'].dt.year\nprint(df)\n<\/code><\/pre>\n
\u6b64\u4ee3\u7801\u5c06\u8f93\u51fa\u6bcf\u4e2a\u65e5\u671f\u5bf9\u5e94\u7684\u5e74\u4efd\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u4e0d\u540c\u683c\u5f0f\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u4ee5\u63d0\u53d6\u5e74\u4efd\uff1f<\/strong>
\u5982\u679c\u60a8\u7684\u65e5\u671f\u5b57\u7b26\u4e32\u683c\u5f0f\u4e0d\u4e00\u81f4\uff0c\u53ef\u4ee5\u4f7f\u7528dateutil<\/code>\u5e93\u4e2d\u7684parser<\/code>\u6a21\u5757\u6765\u81ea\u52a8\u89e3\u6790\u65e5\u671f\u5b57\u7b26\u4e32\u3002parser.parse<\/code>\u51fd\u6570\u80fd\u591f\u8bc6\u522b\u591a\u79cd\u65e5\u671f\u683c\u5f0f\uff0c\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n
from dateutil import parser\n\ndate_str = "12th October 2023"\ndate_obj = parser.parse(date_str)\nyear = date_obj.year\nprint(year)  # \u8f93\u51fa\uff1a2023\n<\/code><\/pre>\n
\u4f7f\u7528\u8fd9\u79cd\u65b9\u5f0f\uff0c\u60a8\u65e0\u9700\u62c5\u5fc3\u65e5\u671f\u5b57\u7b26\u4e32\u7684\u5177\u4f53\u683c\u5f0f\uff0c\u5e93\u4f1a\u81ea\u52a8\u4e3a\u60a8\u89e3\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u63d0\u53d6\u65e5\u671f\u5e74\u4efd\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528 datetime \u6a21\u5757\u3001pandas \u5e93\u3001\u5b57\u7b26\u4e32\u64cd\u4f5c\u7b49\u3002 \u5176\u4e2d […]","protected":false},"author":3,"featured_media":1163518,"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\/1163506"}],"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=1163506"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1163506\/revisions"}],"predecessor-version":[{"id":1163519,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1163506\/revisions\/1163519"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1163518"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1163506"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1163506"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1163506"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}