{"id":935525,"date":"2024-12-26T18:55:07","date_gmt":"2024-12-26T10:55:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/935525.html"},"modified":"2024-12-26T18:55:09","modified_gmt":"2024-12-26T10:55:09","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%88%86%e7%bb%84","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/935525.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5206\u7ec4"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5206\u7ec4\"<\/p>\n

\u5728Python\u4e2d\u8fdb\u884c\u5206\u7ec4\u64cd\u4f5c\uff0c\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001itertools\u5e93\u4ee5\u53ca\u5b57\u5178\u548c\u5217\u8868\u63a8\u5bfc\u7b49\u65b9\u6cd5\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3001itertools\u5e93\u63d0\u4f9b\u4e86\u8fed\u4ee3\u5668\u51fd\u6570\u3001\u5b57\u5178\u548c\u5217\u8868\u63a8\u5bfc\u53ef\u4ee5\u7075\u6d3b\u5b9e\u73b0\u5206\u7ec4\u64cd\u4f5c\u3002<\/strong> \u5176\u4e2d\uff0cPandas\u5e93\u7684groupby()<\/code>\u51fd\u6570\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u80fd\u9ad8\u6548\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u3001\u805a\u5408\u548c\u5206\u6790\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u5206\u7ec4\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u4e2d\u7684GROUPBY\u51fd\u6570<\/p>\n<\/p>\n

Pandas\u5e93\u662fPython\u4e2d\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5176\u4e2d\u7684groupby()<\/code>\u51fd\u6570\u662f\u8fdb\u884c\u6570\u636e\u5206\u7ec4\u64cd\u4f5c\u7684\u6838\u5fc3\u5de5\u5177\u3002\u4f7f\u7528groupby()<\/code>\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u3001\u805a\u5408\u548c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u57fa\u672c\u7528\u6cd5<\/strong><\/p>\n<\/p>\n

    \u5728Pandas\u4e2d\uff0cgroupby()<\/code>\u51fd\u6570\u901a\u5e38\u7528\u4e8eDataFrame\u5bf9\u8c61\u3002\u5b83\u53ef\u4ee5\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5728\u5206\u7ec4\u540e\u8fdb\u884c\u5404\u79cd\u805a\u5408\u64cd\u4f5c\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    data = {'Category': ['A', 'B', 'A', 'B', 'A'],<\/p>\n
            'Values': [10, 20, 30, 40, 50]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    grouped = df.groupby('Category')<\/p>\n
    print(grouped.sum())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2aDataFrame\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528groupby()<\/code>\u51fd\u6570\u6839\u636eCategory<\/code>\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u5206\u7ec4\u540e\u7684\u603b\u548c\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u591a\u5217\u5206\u7ec4<\/strong><\/p>\n<\/p>\n
    Pandas\u7684groupby()<\/code>\u51fd\u6570\u652f\u6301\u591a\u5217\u5206\u7ec4\uff0c\u8fd9\u5728\u5904\u7406\u590d\u6742\u6570\u636e\u65f6\u975e\u5e38\u6709\u7528\u3002\u901a\u8fc7\u4f20\u9012\u4e00\u4e2a\u5217\u8868\u7ed9groupby()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5b9e\u73b0\u591a\u5217\u5206\u7ec4\uff1a<\/p>\n<\/p>\n
    grouped_multi = df.groupby(['Category', 'Values'])<\/p>\n
    print(grouped_multi.size())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6839\u636eCategory<\/code>\u548cValues<\/code>\u4e24\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u4e86\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u5206\u7ec4\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n<\/li>\n
  3. \n
    \u805a\u5408\u64cd\u4f5c<\/strong><\/p>\n<\/p>\n
    \u9664\u4e86\u603b\u548c\uff0cgroupby()<\/code>\u51fd\u6570\u8fd8\u652f\u6301\u5176\u4ed6\u805a\u5408\u64cd\u4f5c\uff0c\u5982\u5e73\u5747\u503c\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u7684agg()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u591a\u79cd\u805a\u5408\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
    agg_operations = grouped.agg({'Values': ['sum', 'mean', 'max']})<\/p>\n
    print(agg_operations)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8be5\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5206\u7ec4\u540e\u540c\u65f6\u8ba1\u7b97\u603b\u548c\u3001\u5e73\u5747\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u4f7f\u7528ITERTTOOLS\u5e93\u8fdb\u884c\u5206\u7ec4<\/p>\n<\/p>\n
    \u5728Python\u6807\u51c6\u5e93\u4e2d\uff0citertools<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u6548\u7684\u8fed\u4ee3\u5668\u51fd\u6570\uff0c\u5176\u4e2dgroupby()<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u5206\u7ec4\u64cd\u4f5c\u3002\u867d\u7136\u5b83\u7684\u529f\u80fd\u4e0d\u5982Pandas\u5e93\u5f3a\u5927\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u57fa\u672c\u7528\u6cd5<\/strong><\/p>\n<\/p>\n
      itertools.groupby()<\/code>\u51fd\u6570\u53ef\u4ee5\u5bf9\u4e00\u4e2a\u6709\u5e8f\u7684\u8fed\u4ee3\u5668\u8fdb\u884c\u5206\u7ec4\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cgroupby()<\/code>\u51fd\u6570\u53ea\u5bf9\u8fde\u7eed\u76f8\u540c\u7684\u5143\u7d20\u8fdb\u884c\u5206\u7ec4\uff0c\u56e0\u6b64\u5728\u4f7f\u7528\u524d\u9700\u8981\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff1a<\/p>\n<\/p>\n
      from itertools import groupby<\/p>\n
      data = [('A', 10), ('B', 20), ('A', 30), ('B', 40), ('A', 50)]<\/p>\n
      data.sort(key=lambda x: x[0])<\/p>\n
      for key, group in groupby(data, key=lambda x: x[0]):<\/p>\n
          print(key, list(group))<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\u540e\uff0c\u6839\u636e\u7b2c\u4e00\u4e2a\u5143\u7d20\u8fdb\u884c\u5206\u7ec4\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u81ea\u5b9a\u4e49\u5206\u7ec4\u6761\u4ef6<\/strong><\/p>\n<\/p>\n
      groupby()<\/code>\u51fd\u6570\u53ef\u4ee5\u63a5\u53d7\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u952e\u51fd\u6570\uff0c\u7528\u4e8e\u6307\u5b9a\u5206\u7ec4\u7684\u6761\u4ef6\u3002\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u7f16\u5199\u81ea\u5b9a\u4e49\u7684\u952e\u51fd\u6570\uff1a<\/p>\n<\/p>\n
      data.sort(key=lambda x: x[1] % 2)<\/p>\n
      for key, group in groupby(data, key=lambda x: x[1] % 2):<\/p>\n
          print(key, list(group))<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6839\u636e\u5143\u7d20\u7684\u7b2c\u4e8c\u4e2a\u503c\u662f\u5426\u4e3a\u5076\u6570\u8fdb\u884c\u5206\u7ec4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u5b57\u5178\u548c\u5217\u8868\u63a8\u5bfc\u5b9e\u73b0\u5206\u7ec4<\/p>\n<\/p>\n
      \u9664\u4e86\u4f7f\u7528Pandas\u548citertools\u5e93\uff0cPython\u7684\u5b57\u5178\u548c\u5217\u8868\u63a8\u5bfc\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u7b80\u5355\u7684\u5206\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u4f7f\u7528\u5b57\u5178<\/strong><\/p>\n<\/p>\n
        \u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\u6765\u624b\u52a8\u5b9e\u73b0\u5206\u7ec4\u64cd\u4f5c\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5c0f\u578b\u6570\u636e\u96c6\u65f6\u975e\u5e38\u65b9\u4fbf\uff1a<\/p>\n<\/p>\n
        data = [('A', 10), ('B', 20), ('A', 30), ('B', 40), ('A', 50)]<\/p>\n
        grouped_dict = {}<\/p>\n
        for key, value in data:<\/p>\n
            if key not in grouped_dict:<\/p>\n
                grouped_dict[key] = []<\/p>\n
            grouped_dict[key].append(value)<\/p>\n
        print(grouped_dict)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u5b57\u5178\u5c06\u6570\u636e\u6309\u952e\u5206\u7ec4\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u5217\u8868\u63a8\u5bfc<\/strong><\/p>\n<\/p>\n
        \u5217\u8868\u63a8\u5bfc\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5206\u7ec4\u540e\u7684\u5217\u8868\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u5bf9\u5206\u7ec4\u540e\u7684\u6570\u636e\u8fdb\u884c\u8fdb\u4e00\u6b65\u64cd\u4f5c\u65f6\uff1a<\/p>\n<\/p>\n
        grouped_list = [(key, [v for k, v in data if k == key]) for key in set(k for k, v in data)]<\/p>\n
        print(grouped_list)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u4e2a\u793a\u4f8b\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5c06\u6570\u636e\u6309\u952e\u5206\u7ec4\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728\u5217\u8868\u4e2d\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u4f7f\u7528NUMPY\u5e93\u5b9e\u73b0\u5206\u7ec4<\/p>\n<\/p>\n
        \u867d\u7136Numpy\u5e93\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4f46\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u7b80\u5355\u7684\u6570\u636e\u5206\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u4f7f\u7528BINCOUT<\/strong><\/p>\n<\/p>\n
          Numpy\u7684bincount()<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u8fd9\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u5206\u7ec4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          data = np.array([1, 2, 1, 2, 1, 3])<\/p>\n
          counts = np.bincount(data)<\/p>\n
          print(counts)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8be5\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u4f7f\u7528WHERE\u548cUNIQUE<\/strong><\/p>\n<\/p>\n
          Numpy\u7684where()<\/code>\u548cunique()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528\u6765\u5b9e\u73b0\u5206\u7ec4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
          unique_values = np.unique(data)<\/p>\n
          grouped_data = {value: data[np.where(data == value)] for value in unique_values}<\/p>\n
          print(grouped_data)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528unique()<\/code>\u51fd\u6570\u627e\u5230\u6570\u636e\u4e2d\u7684\u552f\u4e00\u503c\uff0c\u5e76\u4f7f\u7528where()<\/code>\u51fd\u6570\u8fdb\u884c\u5206\u7ec4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u4f7f\u7528SQLALCHEMY\u8fdb\u884c\u6570\u636e\u5e93\u5206\u7ec4<\/p>\n<\/p>\n
          \u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u6216\u6570\u636e\u5e93\u65f6\uff0cSQLAlchemy\u662f\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\u3002\u5b83\u63d0\u4f9b\u4e86\u4f7f\u7528SQL\u8bed\u53e5\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u7684\u80fd\u529b\uff0c\u5305\u62ec\u5206\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u57fa\u672c\u7528\u6cd5<\/strong><\/p>\n<\/p>\n
            \u4f7f\u7528SQLAlchemy\u8fdb\u884c\u5206\u7ec4\u64cd\u4f5c\u9700\u8981\u9996\u5148\u5b9a\u4e49\u6570\u636e\u5e93\u6a21\u578b\uff0c\u7136\u540e\u4f7f\u7528\u67e5\u8be2\u8bed\u53e5\u8fdb\u884c\u5206\u7ec4\uff1a<\/p>\n<\/p>\n
            from sqlalchemy import create_engine, Column, Integer, String, func<\/p>\n
            from sqlalchemy.ext.declarative import declarative_base<\/p>\n
            from sqlalchemy.orm import sessionmaker<\/p>\n
            Base = declarative_base()<\/p>\n
            class Data(Base):<\/p>\n
                __tablename__ = 'data'<\/p>\n
                id = Column(Integer, primary_key=True)<\/p>\n
                category = Column(String)<\/p>\n
                value = Column(Integer)<\/p>\n
            engine = create_engine('sqlite:\/\/\/:memory:')<\/p>\n
            Base.metadata.create_all(engine)<\/p>\n
            Session = sessionmaker(bind=engine)<\/p>\n
            session = Session()<\/p>\n
            \u6dfb\u52a0\u6570\u636e<\/strong><\/h2>\n
            session.add_all([<\/p>\n
                Data(category='A', value=10),<\/p>\n
                Data(category='B', value=20),<\/p>\n
                Data(category='A', value=30),<\/p>\n
                Data(category='B', value=40),<\/p>\n
                Data(category='A', value=50)<\/p>\n
            ])<\/p>\n
            session.commit()<\/p>\n
            \u5206\u7ec4\u67e5\u8be2<\/strong><\/h2>\n
            results = session.query(Data.category, func.sum(Data.value)).group_by(Data.category).all()<\/p>\n
            for category, total in results:<\/p>\n
                print(category, total)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u6570\u636e\u5e93\u6a21\u578b\uff0c\u5e76\u4f7f\u7528SQLAlchemy\u7684\u67e5\u8be2\u8bed\u53e5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u548c\u805a\u5408\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u9ad8\u7ea7\u67e5\u8be2<\/strong><\/p>\n<\/p>\n
            SQLAlchemy\u652f\u6301\u590d\u6742\u7684\u67e5\u8be2\uff0c\u5305\u62ec\u591a\u5217\u5206\u7ec4\u548c\u591a\u4e2a\u805a\u5408\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
            results = session.query(Data.category, func.sum(Data.value), func.avg(Data.value)).group_by(Data.category).all()<\/p>\n
            for category, total, average in results:<\/p>\n
                print(category, total, average)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8be5\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5206\u7ec4\u67e5\u8be2\u4e2d\u540c\u65f6\u8ba1\u7b97\u603b\u548c\u548c\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u7684\u5206\u7ec4<\/p>\n<\/p>\n
            \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u590d\u6742\u7684\u6570\u636e\u5206\u7ec4\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\uff0c\u7136\u540e\u4f7f\u7528SQLAlchemy\u8fdb\u884c\u6570\u636e\u5e93\u67e5\u8be2\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u6570\u636e\u9884\u5904\u7406<\/strong><\/p>\n<\/p>\n
              \u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u53bb\u9664\u7f3a\u5931\u503c\u3001\u6807\u51c6\u5316\u6570\u636e\u7b49\uff1a<\/p>\n<\/p>\n
              df = pd.DataFrame(data)<\/p>\n
              df.dropna(inplace=True)<\/p>\n
              df['Values'] = (df['Values'] - df['Values'].mean()) \/ df['Values'].std()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u5e93\u53bb\u9664\u7f3a\u5931\u503c\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u6570\u636e\u5e93\u67e5\u8be2<\/strong><\/p>\n<\/p>\n
              \u5728\u9884\u5904\u7406\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528SQLAlchemy\u5c06\u6570\u636e\u63d2\u5165\u6570\u636e\u5e93\uff0c\u5e76\u8fdb\u884c\u590d\u6742\u7684\u67e5\u8be2\u548c\u5206\u7ec4\uff1a<\/p>\n<\/p>\n
              df.to_sql('data', con=engine, if_exists='replace', index=False)<\/p>\n
              results = session.query(Data.category, func.sum(Data.value)).group_by(Data.category).all()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u8be5\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408Pandas\u548cSQLAlchemy\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
              \u901a\u8fc7\u4ee5\u4e0a\u51e0\u79cd\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u7075\u6d3b\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u9009\u62e9\u9002\u5408\u60a8\u9700\u6c42\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u5c06\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u6548\u679c\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5728Python\u4e2d\u5982\u4f55\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8fdb\u884c\u5206\u7ec4\u64cd\u4f5c\uff1f<\/strong>
              Python\u63d0\u4f9b\u4e86\u5185\u7f6e\u7684groupby<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u901a\u8fc7\u8be5\u51fd\u6570\u5bf9\u53ef\u8fed\u4ee3\u5bf9\u8c61\u8fdb\u884c\u5206\u7ec4\u3002\u4f7f\u7528\u65f6\u9700\u8981\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u4ee5\u786e\u4fdd\u76f8\u540c\u7684\u5143\u7d20\u76f8\u90bb\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528itertools<\/code>\u6a21\u5757\u4e2d\u7684groupby<\/code>\u51fd\u6570\u6765\u5bf9\u5217\u8868\u4e2d\u7684\u5b57\u5178\u6309\u67d0\u4e2a\u952e\u8fdb\u884c\u5206\u7ec4\u3002<\/p>\n
              \u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u5982\u4f55\u5229\u7528Pandas\u8fdb\u884c\u5206\u7ec4\uff1f<\/strong>
              Pandas\u5e93\u662f\u6570\u636e\u5206\u6790\u4e2d\u5e38\u7528\u7684\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5206\u7ec4\u529f\u80fd\u3002\u4f7f\u7528groupby()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u6309\u67d0\u4e00\u5217\u6216\u591a\u5217\u5bf9\u6570\u636e\u6846\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5bf9\u6bcf\u7ec4\u6570\u636e\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\uff0c\u6bd4\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u7b49\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u7528\u6237\u80fd\u591f\u9ad8\u6548\u5730\u5bf9\u5927\u89c4\u6a21\u6570\u636e\u8fdb\u884c\u5206\u6790\u3002<\/p>\n
              \u5982\u4f55\u5728\u5206\u7ec4\u64cd\u4f5c\u4e2d\u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570\uff1f<\/strong>
              \u5728Python\u4e2d\uff0c\u4f7f\u7528groupby()<\/code>\u65f6\u53ef\u4ee5\u901a\u8fc7agg()<\/code>\u65b9\u6cd5\u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570\u3002\u8fd9\u4f7f\u5f97\u7528\u6237\u80fd\u591f\u6839\u636e\u5177\u4f53\u9700\u6c42\u8ba1\u7b97\u81ea\u5b9a\u4e49\u7684\u7edf\u8ba1\u503c\uff0c\u6bd4\u5982\u7528\u6237\u53ef\u4ee5\u5b9a\u4e49\u4e00\u4e2a\u8ba1\u7b97\u6807\u51c6\u5dee\u6216\u81ea\u5b9a\u4e49\u6743\u91cd\u7684\u51fd\u6570\uff0c\u7075\u6d3b\u5730\u5904\u7406\u5206\u7ec4\u540e\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8fdb\u884c\u5206\u7ec4\u64cd\u4f5c\uff0c\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001itertools\u5e93\u4ee5\u53ca\u5b57\u5178\u548c\u5217\u8868\u63a8\u5bfc\u7b49\u65b9\u6cd5\u3002Pan […]","protected":false},"author":3,"featured_media":935528,"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\/935525"}],"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=935525"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/935525\/revisions"}],"predecessor-version":[{"id":935529,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/935525\/revisions\/935529"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/935528"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=935525"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=935525"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=935525"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}