{"id":860,"date":"2025-01-11T11:55:54","date_gmt":"2025-01-11T03:55:54","guid":{"rendered":"https:\/\/www.laixuexila.com\/?p=860"},"modified":"2025-01-11T11:55:54","modified_gmt":"2025-01-11T03:55:54","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8pandas%e8%bf%9b%e8%a1%8c%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%ef%bc%9a%e5%bf%ab%e9%80%9f%e5%85%a5%e9%97%a8%e4%b8%8e%e5%ae%9e%e7%94%a8%e6%8a%80%e5%b7%a7","status":"publish","type":"post","link":"https:\/\/www.laixuexila.com\/index.php\/2025\/01\/11\/%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8pandas%e8%bf%9b%e8%a1%8c%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%ef%bc%9a%e5%bf%ab%e9%80%9f%e5%85%a5%e9%97%a8%e4%b8%8e%e5%ae%9e%e7%94%a8%e6%8a%80%e5%b7%a7\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790\uff1a\u5feb\u901f\u5165\u95e8\u4e0e\u5b9e\u7528\u6280\u5de7"},"content":{"rendered":"\n
\n

Pandas\u662f\u6570\u636e\u5206\u6790\u4e2d\u6700\u5f3a\u5927\u548c\u6700\u5e38\u7528\u7684\u5de5\u5177\u4e4b\u4e00\u3002\u901a\u8fc7\u638c\u63e1\u5176\u57fa\u672c\u529f\u80fd\uff0c\u5982\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u9009\u62e9\u4e0e\u7b5b\u9009\u3001\u6570\u636e\u6c47\u603b\u4e0e\u5206\u7ec4\u7b49\uff0c\u60a8\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u548c\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u3002\u540c\u65f6\uff0cPandas\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u9ad8\u7ea7\u529f\u80fd\uff0c\u5e2e\u52a9\u4f60\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u548c\u6570\u636e\u5408\u5e76\u4efb\u52a1\u3002<\/p>\n<\/blockquote>\n\n\n\n

1. \u5f15\u8a00<\/h4>\n\n\n\n

\u5728\u6570\u636e\u79d1\u5b66\u548c\u6570\u636e\u5206\u6790\u9886\u57df\uff0cPandas<\/strong> \u662f\u4e00\u4e2a\u4e0d\u53ef\u6216\u7f3a\u7684Python\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u3001\u7075\u6d3b\u7684\u6570\u636e\u7ed3\u6784\u548c\u5de5\u5177\uff0c\u4e13\u4e3a\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u800c\u8bbe\u8ba1\u3002\u65e0\u8bba\u662f\u6e05\u6d17\u6570\u636e\u3001\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\u3001\u6570\u636e\u53d8\u6362\uff0c\u8fd8\u662f\u6570\u636e\u53ef\u89c6\u5316\uff0cPandas\u90fd\u80fd\u663e\u8457\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u3002<\/p>\n\n\n\n

\u672c\u6587\u5c06\u901a\u8fc7\u5feb\u901f\u5165\u95e8\u6307\u5357\uff0c\u5e26\u4f60\u4e86\u89e3\u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u5305\u62ec\u5e38\u7528\u529f\u80fd\u3001\u6280\u5de7\u548c\u5e94\u7528\u5b9e\u4f8b\u3002<\/p>\n\n\n\n

2. \u5b89\u88c5\u4e0e\u5bfc\u5165Pandas<\/h4>\n\n\n\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u5982\u679c\u8fd8\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n\n\n\n

pip install pandas<\/code><\/pre>\n\n\n\n
\u5b89\u88c5\u540e\uff0c\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5bfc\u5165Pandas\uff1a<\/p>\n\n\n\n
import pandas as pd<\/code><\/pre>\n\n\n\n
3. Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784<\/h4>\n\n\n\n
Pandas\u63d0\u4f9b\u4e86\u4e24\u79cd\u4e3b\u8981\u7684\u6570\u636e\u7ed3\u6784\uff1a<\/p>\n\n\n\n
    \n
  • Series<\/strong>\uff1a\u4e00\u7ef4\u6570\u636e\uff0c\u7c7b\u4f3c\u4e8ePython\u7684\u5217\u8868\u6216\u6570\u7ec4\u3002<\/li>\n\n\n\n
  • DataFrame<\/strong>\uff1a\u4e8c\u7ef4\u6570\u636e\uff0c\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\u3002<\/li>\n<\/ul>\n\n\n\n
    3.1 \u521b\u5efaSeries\u5bf9\u8c61<\/h5>\n\n\n\n
    # \u521b\u5efaSeries\u5bf9\u8c61\nimport pandas as pd\ns = pd.Series([1, 2, 3, 4, 5])\nprint(s)<\/code><\/pre>\n\n\n\n
    \u8f93\u51fa<\/strong>\uff1a<\/p>\n\n\n\n
    0    1\n1    2\n2    3\n3    4\n4    5\ndtype: int64<\/code><\/pre>\n\n\n\n
    Series\u4e5f\u53ef\u4ee5\u901a\u8fc7\u5b57\u5178\u521b\u5efa\uff0c\u952e\u4f5c\u4e3a\u7d22\u5f15\uff0c\u503c\u4f5c\u4e3a\u6570\u636e\uff1a<\/p>\n\n\n\n
    s_dict = pd.Series({\"a\": 1, \"b\": 2, \"c\": 3})\nprint(s_dict)<\/code><\/pre>\n\n\n\n
    \u8f93\u51fa<\/strong>\uff1a<\/p>\n\n\n\n
    a    1\nb    2\nc    3\ndtype: int64<\/code><\/pre>\n\n\n\n
    3.2 \u521b\u5efaDataFrame\u5bf9\u8c61<\/h5>\n\n\n\n
    DataFrame\u662fPandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u8868\u683c\u6216\u4e8c\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u901a\u8fc7\u5b57\u5178\u3001\u5217\u8868\u7b49\u65b9\u5f0f\u521b\u5efa\uff1a<\/p>\n\n\n\n
    # \u901a\u8fc7\u5b57\u5178\u521b\u5efaDataFrame\ndata = {\n    'Name': ['Tom', 'Jerry', 'Mickey'],\n    'Age': [20, 25, 22],\n    'City': ['New York', 'Los Angeles', 'Chicago']\n}\n\ndf = pd.DataFrame(data)\nprint(df)<\/code><\/pre>\n\n\n\n
    \u8f93\u51fa<\/strong>\uff1a<\/p>\n\n\n\n
        Name  Age         City\n0    Tom   20     New York\n1  Jerry   25  Los Angeles\n2 Mickey   22      Chicago<\/code><\/pre>\n\n\n\n
    4. \u6570\u636e\u8bfb\u53d6\u4e0e\u5bfc\u5165<\/h4>\n\n\n\n
    Pandas\u652f\u6301\u4ece\u591a\u79cd\u683c\u5f0f\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u4e0b\u9762\u6211\u4eec\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n\n\n\n
    # \u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\ndf = pd.read_csv(\"data.csv\")<\/code><\/pre>\n\n\n\n
    Pandas\u652f\u6301\u975e\u5e38\u591a\u7684\u8bfb\u53d6\u9009\u9879\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8c03\u6574\uff0c\u4f8b\u5982\u6307\u5b9a\u5206\u9694\u7b26\u3001\u8bfb\u53d6\u7279\u5b9a\u5217\u3001\u5904\u7406\u7f3a\u5931\u503c\u7b49\u3002<\/p>\n\n\n\n
    5. \u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h4>\n\n\n\n
    \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u91cd\u590d\u6570\u636e\u3001\u7c7b\u578b\u8f6c\u6362\u7b49\u95ee\u9898\u3002<\/p>\n\n\n\n
    5.1 \u5904\u7406\u7f3a\u5931\u503c<\/h5>\n\n\n\n
    Pandas\u63d0\u4f9b\u4e86\u4e24\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u51fd\u6570\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1aisnull()<\/code> \u548c dropna()<\/code>\u3002<\/p>\n\n\n\n
    # \u68c0\u67e5\u7f3a\u5931\u503c\nprint(df.isnull())\n\n# \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\ndf_cleaned = df.dropna()<\/code><\/pre>\n\n\n\n
    \u4f60\u8fd8\u53ef\u4ee5\u901a\u8fc7 fillna()<\/code> \u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n\n\n\n
    # \u75280\u586b\u5145\u7f3a\u5931\u503c\ndf_filled = df.fillna(0)<\/code><\/pre>\n\n\n\n
    5.2 \u5220\u9664\u91cd\u590d\u503c<\/h5>\n\n\n\n
    Pandas\u7684 drop_duplicates()<\/code> \u53ef\u4ee5\u5220\u9664\u91cd\u590d\u7684\u6570\u636e\u884c\u3002<\/p>\n\n\n\n
    # \u5220\u9664\u91cd\u590d\u884c\ndf_no_duplicates = df.drop_duplicates()<\/code><\/pre>\n\n\n\n
    5.3 \u4fee\u6539\u5217\u7c7b\u578b<\/h5>\n\n\n\n
    \u4f60\u53ef\u4ee5\u4f7f\u7528 astype()<\/code> \u65b9\u6cd5\u5c06\u5217\u8f6c\u6362\u4e3a\u5176\u4ed6\u6570\u636e\u7c7b\u578b\u3002<\/p>\n\n\n\n
    df['Age'] = df['Age'].astype(float)<\/code><\/pre>\n\n\n\n
    6. \u6570\u636e\u9009\u62e9\u4e0e\u7b5b\u9009<\/h4>\n\n\n\n
    Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u9009\u62e9\u4e0e\u7b5b\u9009\u529f\u80fd\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u5217\u7d22\u5f15\u3001\u884c\u7d22\u5f15\u548c\u6761\u4ef6\u7b5b\u9009\u6765\u83b7\u53d6\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n\n\n\n
    6.1 \u6309\u5217\u9009\u62e9\u6570\u636e<\/h5>\n\n\n\n
    # \u9009\u62e9\u5355\u5217\nprint(df['Name'])\n\n# \u9009\u62e9\u591a\u5217\nprint(df[['Name', 'Age']])<\/code><\/pre>\n\n\n\n
    6.2 \u6309\u884c\u9009\u62e9\u6570\u636e<\/h5>\n\n\n\n
    \u53ef\u4ee5\u4f7f\u7528 .loc[]<\/code> \u6216 .iloc[]<\/code> \u65b9\u6cd5\u8fdb\u884c\u884c\u9009\u62e9\uff1a<\/p>\n\n\n\n
    # \u4f7f\u7528\u6807\u7b7e\u8fdb\u884c\u9009\u62e9\nprint(df.loc[0])\n\n# \u4f7f\u7528\u4f4d\u7f6e\u7d22\u5f15\u8fdb\u884c\u9009\u62e9\nprint(df.iloc[1])<\/code><\/pre>\n\n\n\n
    6.3 \u57fa\u4e8e\u6761\u4ef6\u8fdb\u884c\u7b5b\u9009<\/h5>\n\n\n\n
    \u901a\u8fc7\u6761\u4ef6\u8868\u8fbe\u5f0f\u7b5b\u9009\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u6700\u5e38\u7528\u7684\u64cd\u4f5c\u4e4b\u4e00\uff1a<\/p>\n\n\n\n
    # \u7b5b\u9009Age\u5927\u4e8e22\u7684\u6570\u636e\u884c\nfiltered_data = df[df['Age'] > 22]<\/code><\/pre>\n\n\n\n
    7. \u6570\u636e\u6c47\u603b\u4e0e\u5206\u7ec4<\/h4>\n\n\n\n
    \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6c47\u603b\u6570\u636e\u4e0e\u5206\u7ec4\u662f\u975e\u5e38\u5e38\u89c1\u7684\u4efb\u52a1\u3002Pandas\u63d0\u4f9b\u4e86 groupby()<\/code> \u65b9\u6cd5\u6765\u5206\u7ec4\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\u3002<\/p>\n\n\n\n
    7.1 \u4f7f\u7528 groupby()<\/code> \u8fdb\u884c\u5206\u7ec4<\/h5>\n\n\n\n
    # \u6839\u636eCity\u8fdb\u884c\u5206\u7ec4\u5e76\u8ba1\u7b97\u6bcf\u7ec4\u7684\u5e73\u5747Age\ngrouped_data = df.groupby('City')['Age'].mean()\nprint(grouped_data)<\/code><\/pre>\n\n\n\n
    \u8f93\u51fa<\/strong>\uff1a<\/p>\n\n\n\n
    City\nChicago        22.0\nLos Angeles    25.0\nNew York       20.0\nName: Age, dtype: float64<\/code><\/pre>\n\n\n\n
    7.2 \u591a\u91cd\u805a\u5408\u64cd\u4f5c<\/h5>\n\n\n\n
    \u4f60\u53ef\u4ee5\u5bf9\u5206\u7ec4\u540e\u7684\u6570\u636e\u8fdb\u884c\u591a\u79cd\u805a\u5408\u64cd\u4f5c\uff1a<\/p>\n\n\n\n
    # \u591a\u91cd\u805a\u5408\u64cd\u4f5c\ngrouped_data = df.groupby('City').agg({'Age': ['mean', 'max', 'min']})\nprint(grouped_data)<\/code><\/pre>\n\n\n\n
    8. \u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5<\/h4>\n\n\n\n
    Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u5408\u5e76\u3001\u8fde\u63a5\u548c\u62fc\u63a5\u6570\u636e\u3002\u8fd9\u5bf9\u4e8e\u6574\u5408\u6765\u81ea\u4e0d\u540c\u6765\u6e90\u7684\u6570\u636e\u975e\u5e38\u6709\u7528\u3002<\/p>\n\n\n\n
    8.1 \u4f7f\u7528 concat()<\/code> \u8fdb\u884c\u6570\u636e\u62fc\u63a5<\/h5>\n\n\n\n
    # \u6309\u884c\u62fc\u63a5\u4e24\u4e2aDataFrame\ndf_concat = pd.concat([df1, df2], axis=0)\n\n# \u6309\u5217\u62fc\u63a5\ndf_concat = pd.concat([df1, df2], axis=1)<\/code><\/pre>\n\n\n\n
    8.2 \u4f7f\u7528 merge()<\/code> \u8fdb\u884c\u6570\u636e\u5408\u5e76<\/h5>\n\n\n\n
    # \u6309\u5217\u5408\u5e76\u4e24\u4e2aDataFrame\ndf_merged = pd.merge(df1, df2, on='common_column')<\/code><\/pre>\n\n\n\n
    9. \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n\n\n\n
    \u867d\u7136Pandas\u5185\u7f6e\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u529f\u80fd\uff0c\u4f46\u901a\u5e38\u4f1a\u4e0eMatplotlib\u6216Seaborn\u7ed3\u5408\u4f7f\u7528\uff0c\u4ee5\u521b\u5efa\u66f4\u590d\u6742\u7684\u56fe\u8868\u3002<\/p>\n\n\n\n
    9.1 \u4f7f\u7528Pandas\u8fdb\u884c\u57fa\u672c\u7684\u53ef\u89c6\u5316<\/h5>\n\n\n\n
    import matplotlib.pyplot as plt\n\n# \u4f7f\u7528Pandas\u7ed8\u5236\u57fa\u672c\u7684\u6298\u7ebf\u56fe\ndf['Age'].plot(kind='line')\nplt.show()<\/code><\/pre>\n\n\n\n
    9.2 \u4f7f\u7528Seaborn\u8fdb\u884c\u66f4\u590d\u6742\u7684\u53ef\u89c6\u5316<\/h5>\n\n\n\n
    import seaborn as sns\n\n# \u4f7f\u7528Seaborn\u7ed8\u5236\u7bb1\u578b\u56fe\nsns.boxplot(x='City', y='Age', data=df)\nplt.show()<\/code><\/pre>\n\n\n\n
    10. \u5e38\u89c1Pandas\u6280\u5de7\u4e0e\u8fdb\u9636\u4f7f\u7528<\/h4>\n\n\n\n
    10.1 \u5e94\u7528\u51fd\u6570\uff08apply()<\/code>\uff09<\/h5>\n\n\n\n
    \u4f60\u53ef\u4ee5\u4f7f\u7528 apply()<\/code> \u65b9\u6cd5\u5bf9\u5217\u6216\u884c\u5e94\u7528\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\uff1a<\/p>\n\n\n\n
    df['Age'] = df['Age'].apply(lambda x: x + 1)<\/code><\/pre>\n\n\n\n
    10.2 \u6570\u636e\u900f\u89c6\u8868\uff08pivot_table()<\/code>\uff09<\/h5>\n\n\n\n
    Pandas\u652f\u6301\u7c7b\u4f3cExcel\u7684\u900f\u89c6\u8868\u529f\u80fd\uff1a<\/p>\n\n\n\n
    pivot = df.pivot_table(values='Age', index='City', aggfunc='mean')\nprint(pivot)<\/code><\/pre>\n\n\n\n
    10.3 \u65f6\u95f4\u5e8f\u5217\u5904\u7406<\/h5>\n\n\n\n
    Pandas\u7279\u522b\u9002\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528 to_datetime()<\/code> \u51fd\u6570\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u7c7b\u578b\uff1a<\/p>\n\n\n\n
    df['Date'] = pd.to_datetime(df['Date'])<\/code><\/pre>\n\n\n\n
    \u65e0\u8bba\u4f60\u662f\u6570\u636e\u79d1\u5b66\u521d\u5b66\u8005\u8fd8\u662f\u6709\u7ecf\u9a8c\u7684\u6570\u636e\u5206\u6790\u5e08\uff0cPandas\u90fd\u80fd\u591f\u663e\u8457\u63d0\u9ad8\u4f60\u7684\u6570\u636e\u5206\u6790\u6548\u7387\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u6316\u6398\u6570\u636e\u80cc\u540e\u7684\u4ef7\u503c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
    Pandas\u662f\u6570\u636e\u5206\u6790\u4e2d\u6700\u5f3a\u5927\u548c\u6700\u5e38\u7528\u7684\u5de5\u5177\u4e4b\u4e00\u3002\u901a\u8fc7\u638c\u63e1\u5176\u57fa\u672c\u529f\u80fd\uff0c\u5982\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u9009\u62e9\u4e0e\u7b5b\u9009\u3001\u6570 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[],"class_list":["post-860","post","type-post","status-publish","format-standard","hentry","category-python"],"_links":{"self":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/860","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/comments?post=860"}],"version-history":[{"count":1,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/860\/revisions"}],"predecessor-version":[{"id":861,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/860\/revisions\/861"}],"wp:attachment":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/media?parent=860"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/categories?post=860"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/tags?post=860"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}