{"id":1135322,"date":"2025-01-08T21:24:10","date_gmt":"2025-01-08T13:24:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1135322.html"},"modified":"2025-01-08T21:24:13","modified_gmt":"2025-01-08T13:24:13","slug":"python-%e4%b8%80%e5%88%97%e8%a1%a8%e5%a6%82%e4%bd%95%e5%86%99%e5%85%a5csv","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1135322.html","title":{"rendered":"python \u4e00\u5217\u8868\u5982\u4f55\u5199\u5165csv"},"content":{"rendered":"

\"python<\/p>\n

Python\u4e2d\u5c06\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\u7684\u591a\u79cd\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528csv\u6a21\u5757\u3001pandas\u5e93\u3001numpy\u5e93\u3002<\/strong> <\/p>\n<\/p>\n

\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u5c55\u793a\u5982\u4f55\u901a\u8fc7\u4ee3\u7801\u5b9e\u73b0\u6bcf\u79cd\u65b9\u6cd5\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u4f7f\u7528csv\u6a21\u5757\uff0c\u56e0\u4e3a\u5b83\u662fPython\u5185\u7f6e\u6a21\u5757\uff0c\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u4f7f\u7528csv\u6a21\u5757<\/h3>\n<\/p>\n

Python\u7684csv\u6a21\u5757\u63d0\u4f9b\u4e86\u5bf9CSV\u6587\u4ef6\u8fdb\u884c\u8bfb\u5199\u64cd\u4f5c\u7684\u7b80\u4fbf\u65b9\u6cd5\u3002\u4f7f\u7528\u8fd9\u4e2a\u6a21\u5757\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u5217\u8868\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

1\u3001\u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165csv\u6a21\u5757\u5e76\u51c6\u5907\u6211\u4eec\u7684\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u82e5\u5e72\u884c\u6570\u636e\u7684\u5217\u8868\uff0c\u6bcf\u884c\u6570\u636e\u53c8\u662f\u4e00\u4e2a\u5305\u542b\u82e5\u5e72\u5143\u7d20\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n

import csv<\/p>\n
data = [<\/p>\n
    ["Name", "Age", "City"],<\/p>\n
    ["Alice", 30, "New York"],<\/p>\n
    ["Bob", 25, "San Francisco"],<\/p>\n
    ["Charlie", 35, "Los Angeles"]<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528csv.writer<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
with open('output.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.writer(file)<\/p>\n
    writer.writerows(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0copen<\/code>\u51fd\u6570\u7528\u4e8e\u6253\u5f00\u4e00\u4e2a\u6587\u4ef6\uff0ccsv.writer<\/code>\u521b\u5efa\u4e00\u4e2a\u5199\u5bf9\u8c61\uff0cwriter.writerows<\/code>\u65b9\u6cd5\u5c06\u6211\u4eec\u7684\u5217\u8868\u6570\u636e\u5199\u5165\u6587\u4ef6\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u6613\u884c\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u573a\u666f\u3002<\/strong><\/p>\n<\/p>\n
2\u3001\u6dfb\u52a0\u6807\u9898\u884c<\/h4>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u4e3a\u6211\u4eec\u7684CSV\u6587\u4ef6\u6dfb\u52a0\u6807\u9898\u884c\u3002\u53ef\u4ee5\u901a\u8fc7\u5728\u6570\u636e\u5217\u8868\u7684\u5f00\u5934\u6dfb\u52a0\u6807\u9898\u884c\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
header = ["Name", "Age", "City"]<\/p>\n
data.insert(0, header)<\/p>\n
with open('output.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.writer(file)<\/p>\n
    writer.writerows(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5199\u5165\u5b57\u5178<\/h4>\n<\/p>\n
\u6709\u65f6\u5019\u6211\u4eec\u7684\u6570\u636e\u53ef\u80fd\u662f\u4ee5\u5b57\u5178\u5f62\u5f0f\u5b58\u5728\u7684\uff0ccsv\u6a21\u5757\u540c\u6837\u652f\u6301\u8fd9\u79cd\u60c5\u51b5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528csv.DictWriter<\/code>\u6765\u5199\u5165\u5b57\u5178\u6570\u636e\u3002<\/p>\n<\/p>\n
data_dict = [<\/p>\n
    {"Name": "Alice", "Age": 30, "City": "New York"},<\/p>\n
    {"Name": "Bob", "Age": 25, "City": "San Francisco"},<\/p>\n
    {"Name": "Charlie", "Age": 35, "City": "Los Angeles"}<\/p>\n
]<\/p>\n
with open('output.csv', mode='w', newline='') as file:<\/p>\n
    writer = csv.DictWriter(file, fieldnames=["Name", "Age", "City"])<\/p>\n
    writer.writeheader()<\/p>\n
    writer.writerows(data_dict)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u9996\u5148\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5305\u542b\u5b57\u5178\u7684\u5217\u8868\uff0c\u7136\u540e\u4f7f\u7528DictWriter<\/code>\u65b9\u6cd5\u521b\u5efa\u4e00\u4e2a\u5199\u5bf9\u8c61\uff0c\u5e76\u901a\u8fc7writeheader<\/code>\u65b9\u6cd5\u6dfb\u52a0\u6807\u9898\u884c\uff0c\u6700\u540e\u901a\u8fc7writerows<\/code>\u65b9\u6cd5\u5199\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u80fd\u591f\u5904\u7406\u5404\u79cd\u6570\u636e\u64cd\u4f5c\uff0c\u5305\u62ec\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002\u4e0ecsv\u6a21\u5757\u76f8\u6bd4\uff0cpandas\u66f4\u52a0\u7075\u6d3b\uff0c\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas\u5c06\u5217\u8868\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = [<\/p>\n
    ["Name", "Age", "City"],<\/p>\n
    ["Alice", 30, "New York"],<\/p>\n
    ["Bob", 25, "San Francisco"],<\/p>\n
    ["Charlie", 35, "Los Angeles"]<\/p>\n
]<\/p>\n
df = pd.DataFrame(data[1:], columns=data[0])<\/p>\n
df.to_csv('output_pandas.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5c06\u6570\u636e\u8f6c\u6362\u4e3aDataFrame\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528to_csv<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002\u8fd9\u79cd\u65b9\u6cd5\u5bf9\u4e8e\u9700\u8981\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u6216\u5206\u6790\u7684\u573a\u666f\u975e\u5e38\u6709\u7528\u3002<\/strong><\/p>\n<\/p>\n
2\u3001\u5904\u7406\u5927\u578b\u6570\u636e\u96c6<\/h4>\n<\/p>\n
Pandas\u8fd8\u9002\u7528\u4e8e\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528chunksize<\/code>\u53c2\u6570\u5c06\u6570\u636e\u5206\u5757\u5199\u5165CSV\u6587\u4ef6\uff0c\u4ee5\u907f\u514d\u5185\u5b58\u4e0d\u8db3\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n
chunk_size = 1000<\/p>\n
for i, chunk in enumerate(pd.read_csv('large_input.csv', chunksize=chunk_size)):<\/p>\n
    chunk.to_csv(f'output_chunk_{i}.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4ece\u4e00\u4e2a\u5927\u578bCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u5c06\u5176\u5206\u5757\u5199\u5165\u591a\u4e2a\u5c0f\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528numpy\u5e93<\/h3>\n<\/p>\n
Numpy\u662f\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5numpy\u5e93\u3002<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy\u5c06\u5217\u8868\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([<\/p>\n
    ["Name", "Age", "City"],<\/p>\n
    ["Alice", 30, "New York"],<\/p>\n
    ["Bob", 25, "San Francisco"],<\/p>\n
    ["Charlie", 35, "Los Angeles"]<\/p>\n
])<\/p>\n
np.savetxt('output_numpy.csv', data, delimiter=',', fmt='%s')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5c06\u6570\u636e\u8f6c\u6362\u4e3anumpy\u6570\u7ec4\uff0c\u7136\u540e\u4f7f\u7528savetxt<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u573a\u666f\u3002<\/strong><\/p>\n<\/p>\n
2\u3001\u5904\u7406\u6570\u503c\u6570\u636e<\/h4>\n<\/p>\n
Numpy\u7279\u522b\u9002\u5408\u5904\u7406\u6570\u503c\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy\u7684\u5404\u79cd\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u7136\u540e\u5c06\u7ed3\u679c\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
data = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
mean_data = np.mean(data, axis=0)<\/p>\n
np.savetxt('mean_output.csv', mean_data, delimiter=',')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u9996\u5148\u8ba1\u7b97\u6570\u636e\u7684\u5747\u503c\uff0c\u7136\u540e\u5c06\u5747\u503c\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86Python\u4e2d\u5c06\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528csv\u6a21\u5757\u3001pandas\u5e93\u548cnumpy\u5e93\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f<\/strong>\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
    \n
  1. csv\u6a21\u5757<\/strong>\uff1a\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u5199\u5165\u64cd\u4f5c\uff0c\u6613\u4e8e\u4f7f\u7528\uff0c\u5185\u7f6e\u4e8ePython\u6807\u51c6\u5e93\u3002<\/li>\n
  2. pandas\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u529f\u80fd\u5f3a\u5927\uff0c\u7075\u6d3b\u6027\u9ad8\u3002<\/li>\n
  3. numpy\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u6570\u503c\u6570\u636e\u7684\u5904\u7406\u548c\u8ba1\u7b97\uff0c\u9ad8\u6548\u4e14\u529f\u80fd\u4e30\u5bcc\u3002<\/li>\n<\/ol>\n
    \u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u52a0\u7075\u6d3b\u5730\u5904\u7406\u6570\u636e\uff0c\u5e76\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u9009\u62e9\u6700\u9002\u5408\u7684\u65b9\u6cd5\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5c06Python\u4e2d\u7684\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\uff1f<\/strong>
    \u5c06Python\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5185\u7f6e\u7684csv<\/code>\u6a21\u5757\u6765\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5bfc\u5165\u8be5\u6a21\u5757\uff0c\u7136\u540e\u4f7f\u7528csv.writer()<\/code>\u521b\u5efa\u4e00\u4e2a\u5199\u5165\u5bf9\u8c61\u3002\u63a5\u4e0b\u6765\uff0c\u60a8\u53ef\u4ee5\u8c03\u7528writerow()<\/code>\u6216writerows()<\/code>\u65b9\u6cd5\u5c06\u5217\u8868\u6570\u636e\u5199\u5165\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
    import csv\n\nmy_list = ['\u6570\u636e1', '\u6570\u636e2', '\u6570\u636e3']\nwith open('output.csv', mode='w', newline='') as file:\n    writer = csv.writer(file)\n    writer.writerow(my_list)\n<\/code><\/pre>\n
    \u8fd0\u884c\u6b64\u4ee3\u7801\u540e\uff0c\u60a8\u5c06\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e2d\u627e\u5230\u4e00\u4e2a\u540d\u4e3aoutput.csv<\/code>\u7684\u6587\u4ef6\uff0c\u91cc\u9762\u5305\u542b\u4e86\u60a8\u5217\u8868\u4e2d\u7684\u6570\u636e\u3002<\/p>\n
    \u53ef\u4ee5\u5c06\u5d4c\u5957\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\u5417\uff1f<\/strong>
    \u5f53\u7136\u53ef\u4ee5\uff01\u5d4c\u5957\u5217\u8868\u53ef\u4ee5\u901a\u8fc7writerows()<\/code>\u65b9\u6cd5\u76f4\u63a5\u5199\u5165CSV\u6587\u4ef6\u3002\u6bcf\u4e2a\u5b50\u5217\u8868\u90fd\u4f1a\u88ab\u89c6\u4e3a\u4e00\u884c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a  <\/p>\n
    import csv\n\nnested_list = [['\u59d3\u540d', '\u5e74\u9f84'], ['Alice', 30], ['Bob', 25]]\nwith open('output.csv', mode='w', newline='') as file:\n    writer = csv.writer(file)\n    writer.writerows(nested_list)\n<\/code><\/pre>\n
    \u6267\u884c\u6b64\u4ee3\u7801\u540e\uff0coutput.csv<\/code>\u6587\u4ef6\u5c06\u5305\u542b\u8868\u5934\u548c\u76f8\u5e94\u7684\u6570\u636e\u884c\u3002<\/p>\n
    \u5728\u5199\u5165CSV\u6587\u4ef6\u65f6\u5982\u4f55\u5904\u7406\u7279\u6b8a\u5b57\u7b26\uff1f<\/strong>
    \u5728\u5199\u5165CSV\u6587\u4ef6\u65f6\uff0c\u82e5\u5217\u8868\u4e2d\u5305\u542b\u7279\u6b8a\u5b57\u7b26\uff08\u5982\u9017\u53f7\u3001\u5f15\u53f7\u7b49\uff09\uff0c\u53ef\u4ee5\u901a\u8fc7csv<\/code>\u6a21\u5757\u7684quotechar<\/code>\u548cquoting<\/code>\u53c2\u6570\u6765\u5904\u7406\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0ccsv.writer<\/code>\u4f1a\u81ea\u52a8\u5904\u7406\u8fd9\u4e9b\u5b57\u7b26\u3002\u4f8b\u5982\uff1a  <\/p>\n
    import csv\n\nspecial_list = ['\u6570\u636e1', '\u6570\u636e,2', '\u6570\u636e"3']\nwith open('output.csv', mode='w', newline='') as file:\n    writer = csv.writer(file)\n    writer.writerow(special_list)\n<\/code><\/pre>\n
    \u8fd9\u6837\uff0c\u751f\u6210\u7684CSV\u6587\u4ef6\u5c06\u6b63\u786e\u5904\u7406\u8fd9\u4e9b\u7279\u6b8a\u5b57\u7b26\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u4e2d\u5c06\u5217\u8868\u5199\u5165CSV\u6587\u4ef6\u7684\u591a\u79cd\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528csv\u6a21\u5757\u3001pandas\u5e93\u3001numpy\u5e93\u3002 \u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c […]","protected":false},"author":3,"featured_media":1135327,"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\/1135322"}],"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=1135322"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135322\/revisions"}],"predecessor-version":[{"id":1135328,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135322\/revisions\/1135328"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1135327"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1135322"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1135322"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1135322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}