{"id":1141684,"date":"2025-01-08T22:33:14","date_gmt":"2025-01-08T14:33:14","guid":{"rendered":""},"modified":"2025-01-08T22:33:17","modified_gmt":"2025-01-08T14:33:17","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e4%b8%a4%e4%b8%aa%e6%95%b0%e6%8d%ae%e5%ba%93%e6%8b%bc%e6%8e%a5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1141684.html","title":{"rendered":"python\u5982\u4f55\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5\"<\/p>\n

Python \u5982\u4f55\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5<\/strong><\/p>\n<\/p>\n

\u8981\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5\u5728\u4e00\u8d77\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u4f8b\u5982pandas\u3001SQLAlchemy\u3001sqlite3<\/strong>\u7b49\u3002\u9996\u5148\uff0cpandas<\/strong>\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u5230DataFrame\u4e2d\u5e76\u8fdb\u884c\u62fc\u63a5\u64cd\u4f5c\uff1bSQLAlchemy<\/strong> \u662f\u4e00\u4e2aSQL\u5de5\u5177\u5305\u548c\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\uff08ORM\uff09\u5e93\uff0c\u5b83\u53ef\u4ee5\u4e0e\u5404\u79cd\u6570\u636e\u5e93\u7cfb\u7edf\u8fdb\u884c\u4ea4\u4e92\uff0c\u5e76\u4e14\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5e93\u64cd\u4f5c\uff1bsqlite3<\/strong> \u5219\u662f\u5185\u7f6e\u7684\u8f7b\u91cf\u7ea7\u6570\u636e\u5e93\uff0c\u53ef\u4ee5\u76f4\u63a5\u5728Python\u4e2d\u4f7f\u7528\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5\u5728\u4e00\u8d77\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 Pandas \u8fdb\u884c\u6570\u636e\u5e93\u62fc\u63a5<\/h3>\n<\/p>\n

Pandas \u662f Python \u4e2d\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528 pandas \u8f7b\u677e\u5730\u52a0\u8f7d\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u5c06\u5176\u62fc\u63a5\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1\u3001\u52a0\u8f7d\u6570\u636e\u5e93\u6570\u636e<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u7684\u6570\u636e\u52a0\u8f7d\u5230 pandas DataFrame \u4e2d\u3002\u5047\u8bbe\u6211\u4eec\u4f7f\u7528\u7684\u662f SQLite \u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
import sqlite3<\/p>\n
\u8fde\u63a5\u7b2c\u4e00\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
conn1 = sqlite3.connect('database1.db')<\/p>\n
df1 = pd.read_sql_query("SELECT * FROM table_name1", conn1)<\/p>\n
\u8fde\u63a5\u7b2c\u4e8c\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
conn2 = sqlite3.connect('database2.db')<\/p>\n
df2 = pd.read_sql_query("SELECT * FROM table_name2", conn2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u62fc\u63a5\u6570\u636e<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 pandas \u7684 concat<\/code> \u65b9\u6cd5\u5c06\u4e24\u4e2a DataFrame \u62fc\u63a5\u5728\u4e00\u8d77\u3002\u53ef\u4ee5\u9009\u62e9\u6309\u884c\u6216\u6309\u5217\u62fc\u63a5\u3002<\/p>\n<\/p>\n
# \u6309\u884c\u62fc\u63a5<\/p>\n
df_combined = pd.concat([df1, df2], ignore_index=True)<\/p>\n
\u6309\u5217\u62fc\u63a5<\/strong><\/h2>\n
df_combined = pd.concat([df1, df2], axis=1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4fdd\u5b58\u62fc\u63a5\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u62fc\u63a5\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u5e93\u6216\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u5230\u65b0\u7684\u6570\u636e\u5e93<\/p>\n
conn_combined = sqlite3.connect('combined_database.db')<\/p>\n
df_combined.to_sql('combined_table', conn_combined, index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 SQLAlchemy \u8fdb\u884c\u6570\u636e\u5e93\u62fc\u63a5<\/h3>\n<\/p>\n
SQLAlchemy \u63d0\u4f9b\u4e86\u66f4\u4e3a\u590d\u6742\u548c\u7075\u6d3b\u7684\u6570\u636e\u5e93\u4ea4\u4e92\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528 SQLAlchemy \u6765\u8fdb\u884c\u6570\u636e\u5e93\u8fde\u63a5\u548c\u6570\u636e\u62fc\u63a5\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5 SQLAlchemy<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86 SQLAlchemy \u5e93\uff1a<\/p>\n<\/p>\n
pip install sqlalchemy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8fde\u63a5\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u4f7f\u7528 SQLAlchemy \u8fde\u63a5\u5230\u4e24\u4e2a\u6570\u636e\u5e93\u5e76\u52a0\u8f7d\u6570\u636e\u3002<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
import pandas as pd<\/p>\n
\u8fde\u63a5\u7b2c\u4e00\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
engine1 = create_engine('sqlite:\/\/\/database1.db')<\/p>\n
df1 = pd.read_sql_table('table_name1', engine1)<\/p>\n
\u8fde\u63a5\u7b2c\u4e8c\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
engine2 = create_engine('sqlite:\/\/\/database2.db')<\/p>\n
df2 = pd.read_sql_table('table_name2', engine2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u62fc\u63a5\u6570\u636e<\/h4>\n<\/p>\n
\u4e0e\u4f7f\u7528 pandas \u7684\u65b9\u6cd5\u7c7b\u4f3c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 concat<\/code> \u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u62fc\u63a5\u3002<\/p>\n<\/p>\n
# \u6309\u884c\u62fc\u63a5<\/p>\n
df_combined = pd.concat([df1, df2], ignore_index=True)<\/p>\n
\u6309\u5217\u62fc\u63a5<\/strong><\/h2>\n
df_combined = pd.concat([df1, df2], axis=1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u4fdd\u5b58\u62fc\u63a5\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u5c06\u62fc\u63a5\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u65b0\u7684\u6570\u636e\u5e93\u4e2d\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u5230\u65b0\u7684\u6570\u636e\u5e93<\/p>\n
engine_combined = create_engine('sqlite:\/\/\/combined_database.db')<\/p>\n
df_combined.to_sql('combined_table', engine_combined, index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528 sqlite3 \u8fdb\u884c\u6570\u636e\u5e93\u62fc\u63a5<\/h3>\n<\/p>\n
SQLite \u662f\u4e00\u4e2a\u8f7b\u91cf\u7ea7\u7684\u6570\u636e\u5e93\uff0cPython \u5185\u7f6e\u4e86\u5bf9 SQLite \u7684\u652f\u6301\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528 sqlite3<\/code> \u5e93\u8fdb\u884c\u6570\u636e\u5e93\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u8fde\u63a5\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u8fde\u63a5\u5230\u4e24\u4e2a SQLite \u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n
import sqlite3<\/p>\n
\u8fde\u63a5\u7b2c\u4e00\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
conn1 = sqlite3.connect('database1.db')<\/p>\n
cursor1 = conn1.cursor()<\/p>\n
\u8fde\u63a5\u7b2c\u4e8c\u4e2a\u6570\u636e\u5e93<\/strong><\/h2>\n
conn2 = sqlite3.connect('database2.db')<\/p>\n
cursor2 = conn2.cursor()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u4ece\u4e24\u4e2a\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6\u7b2c\u4e00\u4e2a\u6570\u636e\u5e93\u7684\u6570\u636e<\/p>\n
cursor1.execute("SELECT * FROM table_name1")<\/p>\n
rows1 = cursor1.fetchall()<\/p>\n
\u8bfb\u53d6\u7b2c\u4e8c\u4e2a\u6570\u636e\u5e93\u7684\u6570\u636e<\/strong><\/h2>\n
cursor2.execute("SELECT * FROM table_name2")<\/p>\n
rows2 = cursor2.fetchall()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u62fc\u63a5\u6570\u636e<\/h4>\n<\/p>\n
\u5c06\u8bfb\u53d6\u5230\u7684\u6570\u636e\u8fdb\u884c\u62fc\u63a5\u3002<\/p>\n<\/p>\n
# \u62fc\u63a5\u6570\u636e<\/p>\n
combined_rows = rows1 + rows2<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u4fdd\u5b58\u62fc\u63a5\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u5c06\u62fc\u63a5\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u65b0\u7684\u6570\u636e\u5e93\u4e2d\u3002<\/p>\n<\/p>\n
# \u8fde\u63a5\u5230\u65b0\u7684\u6570\u636e\u5e93<\/p>\n
conn_combined = sqlite3.connect('combined_database.db')<\/p>\n
cursor_combined = conn_combined.cursor()<\/p>\n
\u521b\u5efa\u65b0\u8868<\/strong><\/h2>\n
cursor_combined.execute('''CREATE TABLE combined_table (<\/p>\n
                           column1 type,<\/p>\n
                           column2 type,<\/p>\n
                           ...<\/p>\n
                           )''')<\/p>\n
\u63d2\u5165\u6570\u636e<\/strong><\/h2>\n
cursor_combined.executemany("INSERT INTO combined_table VALUES (?, ?, ...)", combined_rows)<\/p>\n
conn_combined.commit()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u5e93\u62fc\u63a5\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u4e00\u81f4\u6027<\/h4>\n<\/p>\n
\u786e\u4fdd\u4e24\u4e2a\u6570\u636e\u5e93\u4e2d\u7684\u8868\u7ed3\u6784\u4e00\u81f4\uff0c\u5373\u5217\u540d\u548c\u6570\u636e\u7c7b\u578b\u76f8\u540c\u3002\u5982\u679c\u4e0d\u4e00\u81f4\uff0c\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u91cf<\/h4>\n<\/p>\n
\u5982\u679c\u6570\u636e\u91cf\u8f83\u5927\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5185\u5b58\u4e0d\u8db3\u7684\u95ee\u9898\u3002\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u8003\u8651\u5206\u6279\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
3\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u62fc\u63a5\u6570\u636e\u4e4b\u524d\uff0c\u6700\u597d\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002\u4f8b\u5982\uff0c\u53bb\u9664\u91cd\u590d\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u503c\u7b49\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Pandas\u3001SQLAlchemy \u548c sqlite3 \u5e93\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5\u5728\u4e00\u8d77\u3002\u4e0d\u540c\u7684\u65b9\u6cd5\u6709\u4e0d\u540c\u7684\u4f18\u7f3a\u70b9\uff0c\u9009\u62e9\u9002\u5408\u81ea\u5df1\u9700\u6c42\u7684\u65b9\u6cd5\u5c24\u4e3a\u91cd\u8981\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5728 Python \u4e2d\u8fdb\u884c\u6570\u636e\u5e93\u62fc\u63a5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8fde\u63a5\u591a\u4e2a\u6570\u636e\u5e93\u8fdb\u884c\u6570\u636e\u62fc\u63a5\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u5e93\u6765\u8fde\u63a5\u591a\u4e2a\u6570\u636e\u5e93\uff0c\u4f8b\u5982 sqlite3<\/code>\u3001pandas<\/code>\u3001SQLAlchemy<\/code> \u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef\u4ee5\u5206\u522b\u4ece\u4e24\u4e2a\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528 pandas<\/code> \u7684 concat<\/code> \u6216 merge<\/code> \u51fd\u6570\u5c06\u5b83\u4eec\u62fc\u63a5\u5728\u4e00\u8d77\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u5173\u7cfb\u6570\u636e\u5e93\uff0c\u5982 SQLite\u3001MySQL \u548c PostgreSQL\u3002<\/p>\n
\u5728\u62fc\u63a5\u4e24\u4e2a\u6570\u636e\u5e93\u7684\u6570\u636e\u65f6\uff0c\u6709\u54ea\u4e9b\u6700\u4f73\u5b9e\u8df5\uff1f<\/strong>
\u5728\u62fc\u63a5\u6570\u636e\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u7684\u7ed3\u6784\u76f8\u4f3c\uff0c\u4f8b\u5982\u5217\u540d\u548c\u6570\u636e\u7c7b\u578b\u3002\u4f7f\u7528\u6570\u636e\u6e05\u6d17\u5de5\u5177\uff08\u5982 pandas<\/code>\uff09\u6765\u5904\u7406\u7f3a\u5931\u503c\u548c\u91cd\u590d\u9879\u3002\u6b64\u5916\uff0c\u5c3d\u91cf\u9009\u62e9\u9002\u5f53\u7684\u62fc\u63a5\u65b9\u5f0f\uff0c\u4f8b\u5982\u4f7f\u7528 inner join<\/code> \u6216 outer join<\/code>\uff0c\u4ee5\u4fbf\u83b7\u5f97\u7b26\u5408\u9700\u6c42\u7684\u6570\u636e\u7ed3\u679c\u3002\u6700\u7ec8\uff0c\u8981\u6ce8\u610f\u6027\u80fd\u95ee\u9898\uff0c\u5c24\u5176\u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u62fc\u63a5\u540e\u6570\u636e\u4e2d\u7684\u91cd\u590d\u9879\u548c\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u62fc\u63a5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528 pandas<\/code> \u7684 drop_duplicates()<\/code> \u65b9\u6cd5\u6765\u53bb\u9664\u91cd\u590d\u9879\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 fillna()<\/code> \u65b9\u6cd5\u586b\u8865\u7f3a\u5931\u503c\uff0c\u6216\u8005\u901a\u8fc7 dropna()<\/code> \u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u7b56\u7565\u6765\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u95ee\u9898\uff0c\u4ee5\u786e\u4fdd\u6700\u7ec8\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python \u5982\u4f55\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5 \u8981\u5c06\u4e24\u4e2a\u6570\u636e\u5e93\u62fc\u63a5\u5728\u4e00\u8d77\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u4f8b\u5982pa […]","protected":false},"author":3,"featured_media":1141693,"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\/1141684"}],"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=1141684"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1141684\/revisions"}],"predecessor-version":[{"id":1141694,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1141684\/revisions\/1141694"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1141693"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1141684"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1141684"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1141684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}