{"id":1155627,"date":"2025-01-13T18:06:03","date_gmt":"2025-01-13T10:06:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1155627.html"},"modified":"2025-01-13T18:06:06","modified_gmt":"2025-01-13T10:06:06","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97csv%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1155627.html","title":{"rendered":"python\u5982\u4f55\u8ba1\u7b97csv\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba1\u7b97csv\u6570\u636e\"<\/p>\n

\u8981\u8ba1\u7b97CSV\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684pandas<\/code>\u5e93\u6765\u5904\u7406\u3002\u4f7f\u7528pandas\u5904\u7406CSV\u6587\u4ef6\u975e\u5e38\u65b9\u4fbf\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u5206\u6790\u5de5\u5177\u3001\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u8bfb\u53d6\u3001\u64cd\u4f5c\u548c\u8ba1\u7b97CSV\u6570\u636e<\/strong>\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86pandas<\/code>\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4f7f\u7528pandas<\/code>\u5e93\u8bfb\u53d6\u3001\u5904\u7406\u548c\u8ba1\u7b97CSV\u6570\u636e\u7684\u57fa\u672c\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
    \n
  1. \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/li>\n
  2. \u67e5\u770b\u6570\u636e<\/strong><\/li>\n
  3. \u6570\u636e\u6e05\u6d17<\/strong><\/li>\n
  4. \u6570\u636e\u8ba1\u7b97<\/strong><\/li>\n
  5. \u4fdd\u5b58\u7ed3\u679c<\/strong><\/li>\n<\/ol>\n
    \u4e00\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6\u662f\u5904\u7406CSV\u6570\u636e\u7684\u7b2c\u4e00\u6b65\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u4e2d\u7684read_csv<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u67e5\u770b\u6570\u636e<\/h3>\n<\/p>\n
    \u5728\u8bfb\u53d6CSV\u6587\u4ef6\u540e\uff0c\u67e5\u770b\u6570\u636e\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002\u53ef\u4ee5\u4f7f\u7528head()<\/code>\u51fd\u6570\u67e5\u770b\u524d\u51e0\u884c\u6570\u636e\uff0c\u4f7f\u7528info()<\/code>\u51fd\u6570\u67e5\u770b\u6570\u636e\u7c7b\u578b\u548c\u7f3a\u5931\u503c\uff0c\u4f7f\u7528describe()<\/code>\u51fd\u6570\u67e5\u770b\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n<\/p>\n
    # \u67e5\u770b\u524d5\u884c\u6570\u636e<\/p>\n
    print(df.head())<\/p>\n
    \u67e5\u770b\u6570\u636e\u7c7b\u578b\u548c\u7f3a\u5931\u503c<\/strong><\/h2>\n
    print(df.info())<\/p>\n
    \u67e5\u770b\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
    print(df.describe())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
    \u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e05\u6d17\u64cd\u4f5c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u5220\u9664\u91cd\u590d\u6570\u636e\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7b49\u3002<\/p>\n<\/p>\n
    # \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
    df = df.dropna()  # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
    df = df.fillna(0)  # \u75280\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
    \u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
    df = df.drop_duplicates()<\/p>\n
    \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong><\/h2>\n
    df['column_name'] = df['column_name'].astype('int')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u6570\u636e\u8ba1\u7b97<\/h3>\n<\/p>\n
    \u5728\u6570\u636e\u6e05\u6d17\u4e4b\u540e\uff0c\u5c31\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u8ba1\u7b97\u4e86\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u8ba1\u7b97\u529f\u80fd\uff0c\u6bd4\u5982\u6c42\u548c\u3001\u5747\u503c\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5206\u7ec4\u805a\u5408\u7b49\u3002<\/p>\n<\/p>\n
    # \u6c42\u548c<\/p>\n
    total = df['column_name'].sum()<\/p>\n
    print(f"Sum: {total}")<\/p>\n
    \u6c42\u5747\u503c<\/strong><\/h2>\n
    average = df['column_name'].mean()<\/p>\n
    print(f"Average: {average}")<\/p>\n
    \u6c42\u6700\u5927\u503c<\/strong><\/h2>\n
    maximum = df['column_name'].max()<\/p>\n
    print(f"Max: {maximum}")<\/p>\n
    \u6c42\u6700\u5c0f\u503c<\/strong><\/h2>\n
    minimum = df['column_name'].min()<\/p>\n
    print(f"Min: {minimum}")<\/p>\n
    \u5206\u7ec4\u805a\u5408<\/strong><\/h2>\n
    grouped = df.groupby('group_column_name').agg({'column_name': ['sum', 'mean']})<\/p>\n
    print(grouped)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u4fdd\u5b58\u7ed3\u679c<\/h3>\n<\/p>\n
    \u5728\u5b8c\u6210\u6570\u636e\u8ba1\u7b97\u540e\uff0c\u53ef\u4ee5\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u65b0\u7684CSV\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
    # \u4fdd\u5b58\u7ed3\u679c\u5230CSV\u6587\u4ef6<\/p>\n
    df.to_csv('result.csv', index=False)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8be6\u7ec6\u63cf\u8ff0\uff1a\u6570\u636e\u8ba1\u7b97<\/h3>\n<\/p>\n
    \u6570\u636e\u8ba1\u7b97\u662f\u5904\u7406CSV\u6570\u636e\u7684\u6838\u5fc3\u73af\u8282\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u4e2d\u7684\u5404\u79cd\u51fd\u6570\u8fdb\u884c\u64cd\u4f5c\uff0c\u5305\u62ec\u7b97\u672f\u8ba1\u7b97\u3001\u7edf\u8ba1\u8ba1\u7b97\u4ee5\u53ca\u5206\u7ec4\u805a\u5408\u7b49<\/strong>\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u8ba1\u7b97\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
    \u7b97\u672f\u8ba1\u7b97<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u5bf9DataFrame\u4e2d\u7684\u5217\u8fdb\u884c\u5404\u79cd\u7b97\u672f\u8ba1\u7b97\uff0c\u5305\u62ec\u52a0\u3001\u51cf\u3001\u4e58\u3001\u9664\u7b49\u3002\u6bd4\u5982\uff1a<\/p>\n<\/p>\n
    # \u65b0\u589e\u4e00\u5217\uff0c\u662f\u67d0\u4e24\u5217\u7684\u548c<\/p>\n
    df['new_column'] = df['column1'] + df['column2']<\/p>\n
    \u8ba1\u7b97\u6240\u6709\u503c\u7684\u5e73\u65b9<\/strong><\/h2>\n
    df['squared'] = df['column'].apply(lambda x: x  2)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u7edf\u8ba1\u8ba1\u7b97<\/h4>\n<\/p>\n
    pandas<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u8ba1\u7b97\u529f\u80fd\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6c42\u548c\u3001\u5747\u503c\u3001\u65b9\u5dee\u3001\u6807\u51c6\u5dee\u7b49\u8ba1\u7b97\u3002\u6bd4\u5982\uff1a<\/p>\n<\/p>\n
    # \u6c42\u5217\u7684\u5747\u503c<\/p>\n
    mean_value = df['column'].mean()<\/p>\n
    \u6c42\u5217\u7684\u65b9\u5dee<\/strong><\/h2>\n
    variance = df['column'].var()<\/p>\n
    \u6c42\u5217\u7684\u6807\u51c6\u5dee<\/strong><\/h2>\n
    std_dev = df['column'].std()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5206\u7ec4\u805a\u5408<\/h4>\n<\/p>\n
    \u5206\u7ec4\u805a\u5408\u662f\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u5e38\u89c1\u7684\u64cd\u4f5c\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u6309\u7167\u67d0\u4e00\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5bf9\u6bcf\u4e2a\u5206\u7ec4\u8fdb\u884c\u805a\u5408\u8ba1\u7b97\u3002\u6bd4\u5982\uff1a<\/p>\n<\/p>\n
    # \u6309\u7167\u67d0\u5217\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u5206\u7ec4\u7684\u5747\u503c<\/p>\n
    grouped_mean = df.groupby('group_column')['column'].mean()<\/p>\n
    \u6309\u7167\u67d0\u5217\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u5206\u7ec4\u7684\u591a\u4e2a\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
    grouped_stats = df.groupby('group_column').agg({'column1': 'mean', 'column2': 'sum'})<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u81ea\u5b9a\u4e49\u51fd\u6570<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u5b9a\u4e49\u81ea\u5df1\u7684\u51fd\u6570\uff0c\u5e76\u4f7f\u7528apply<\/code>\u65b9\u6cd5\u5c06\u51fd\u6570\u5e94\u7528\u5230DataFrame\u7684\u5217\u4e0a\u3002\u6bd4\u5982\uff1a<\/p>\n<\/p>\n
    # \u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u8ba1\u7b97\u67d0\u5217\u7684\u5e73\u65b9\u548c<\/p>\n
    def squared_sum(column):<\/p>\n
        return sum(x  2 for x in column)<\/p>\n
    \u5c06\u81ea\u5b9a\u4e49\u51fd\u6570\u5e94\u7528\u5230\u67d0\u5217<\/strong><\/h2>\n
    df['squared_sum'] = df['column'].apply(squared_sum)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b9e\u4f8b\u64cd\u4f5c<\/h3>\n<\/p>\n
    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u5b9e\u4f8b\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528pandas<\/code>\u5e93\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u8ba1\u7b97\u548c\u4fdd\u5b58CSV\u6570\u636e\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    \u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    print(df.info())<\/p>\n
    print(df.describe())<\/p>\n
    \u6570\u636e\u6e05\u6d17<\/strong><\/h2>\n
    df = df.dropna()  # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
    df = df.drop_duplicates()  # \u5220\u9664\u91cd\u590d\u6570\u636e<\/p>\n
    df['column1'] = df['column1'].astype('int')  # \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/p>\n
    \u6570\u636e\u8ba1\u7b97<\/strong><\/h2>\n
    total = df['column1'].sum()<\/p>\n
    average = df['column1'].mean()<\/p>\n
    maximum = df['column1'].max()<\/p>\n
    minimum = df['column1'].min()<\/p>\n
    print(f"Sum: {total}")<\/p>\n
    print(f"Average: {average}")<\/p>\n
    print(f"Max: {maximum}")<\/p>\n
    print(f"Min: {minimum}")<\/p>\n
    \u5206\u7ec4\u805a\u5408<\/strong><\/h2>\n
    grouped = df.groupby('group_column').agg({'column1': ['sum', 'mean']})<\/p>\n
    print(grouped)<\/p>\n
    \u4fdd\u5b58\u7ed3\u679c\u5230CSV\u6587\u4ef6<\/strong><\/h2>\n
    df.to_csv('result.csv', index=False)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u8fd9\u4e2a\u5b9e\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u6e05\u695a\u5730\u770b\u5230\u5982\u4f55\u4f7f\u7528pandas<\/code>\u5e93\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u8ba1\u7b97\u548c\u4fdd\u5b58CSV\u6570\u636e\u3002\u8fd9\u4e9b\u64cd\u4f5c\u662f\u5904\u7406CSV\u6570\u636e\u7684\u57fa\u7840\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u8c03\u6574\u548c\u6269\u5c55\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff1f<\/strong>
    \u4f7f\u7528Python\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u5229\u7528\u5185\u7f6e\u7684csv<\/code>\u6a21\u5757\u6216pandas<\/code>\u5e93\u3002\u4f7f\u7528csv<\/code>\u6a21\u5757\u65f6\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u6a21\u5757\uff0c\u7136\u540e\u6253\u5f00CSV\u6587\u4ef6\u5e76\u4f7f\u7528csv.reader()<\/code>\u6765\u8bfb\u53d6\u6570\u636e\u3002\u4f8b\u5982\uff1a  <\/p>\n
    import csv\n\nwith open('data.csv', mode='r') as file:\n    reader = csv.reader(file)\n    for row in reader:\n        print(row)\n<\/code><\/pre>\n
    \u5982\u679c\u4f7f\u7528pandas<\/code>\u5e93\uff0c\u8bfb\u53d6CSV\u6587\u4ef6\u53ef\u4ee5\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\uff0c\u793a\u4f8b\u5982\u4e0b\uff1a  <\/p>\n
    import pandas as pd\n\ndata = pd.read_csv('data.csv')\nprint(data)\n<\/code><\/pre>\n
    \u5728CSV\u6587\u4ef6\u4e2d\u5982\u4f55\u8fdb\u884c\u6570\u636e\u8ba1\u7b97\uff1f<\/strong>
    \u5728\u8bfb\u53d6CSV\u6587\u4ef6\u540e\uff0c\u53ef\u4ee5\u5229\u7528Python\u7684\u5404\u79cd\u6570\u636e\u5904\u7406\u5de5\u5177\u8fdb\u884c\u8ba1\u7b97\u3002\u5982\u679c\u4f7f\u7528pandas<\/code>\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u5176\u5185\u7f6e\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u548c\u8ba1\u7b97\uff0c\u4f8b\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u7b49\u3002\u5047\u8bbe\u6709\u4e00\u5217\u540d\u4e3a\u201c\u9500\u552e\u989d\u201d\uff0c\u53ef\u4ee5\u8fd9\u6837\u8ba1\u7b97\u603b\u548c\uff1a  <\/p>\n
    total_sales = data['\u9500\u552e\u989d'].sum()\nprint(f"\u603b\u9500\u552e\u989d\u4e3a: {total_sales}")\n<\/code><\/pre>\n
    \u5982\u679c\u8981\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528mean()<\/code>\u65b9\u6cd5\uff1a  <\/p>\n
    average_sales = data['\u9500\u552e\u989d'].mean()\nprint(f"\u5e73\u5747\u9500\u552e\u989d\u4e3a: {average_sales}")\n<\/code><\/pre>\n
    \u5982\u4f55\u5904\u7406CSV\u6587\u4ef6\u4e2d\u7684\u7f3a\u5931\u6570\u636e\uff1f<\/strong>
    \u5728\u8fdb\u884c\u6570\u636e\u8ba1\u7b97\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u4f7f\u7528pandas<\/code>\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60f3\u75280\u6765\u586b\u5145\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a  <\/p>\n
    data.fillna(0, inplace=True)\n<\/code><\/pre>\n
    \u5982\u679c\u5e0c\u671b\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u53ef\u4ee5\u4f7f\u7528\uff1a  <\/p>\n
    data.dropna(inplace=True)\n<\/code><\/pre>\n
    \u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u786e\u4fdd\u5728\u8fdb\u884c\u8ba1\u7b97\u65f6\u6570\u636e\u7684\u5b8c\u6574\u6027\uff0c\u4ece\u800c\u5f97\u5230\u66f4\u51c6\u786e\u7684\u7ed3\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u8ba1\u7b97CSV\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684pandas\u5e93\u6765\u5904\u7406\u3002\u4f7f\u7528pandas\u5904\u7406CSV\u6587\u4ef6\u975e\u5e38\u65b9\u4fbf\uff0c\u5b83\u63d0\u4f9b […]","protected":false},"author":3,"featured_media":1155630,"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\/1155627"}],"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=1155627"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1155627\/revisions"}],"predecessor-version":[{"id":1155631,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1155627\/revisions\/1155631"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1155630"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1155627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1155627"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1155627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}