{"id":933672,"date":"2024-12-26T18:19:39","date_gmt":"2024-12-26T10:19:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/933672.html"},"modified":"2024-12-26T18:19:41","modified_gmt":"2024-12-26T10:19:41","slug":"python%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/933672.html","title":{"rendered":"python\u5982\u4f55\u6dfb\u52a0\u5217"},"content":{"rendered":"

\"python\u5982\u4f55\u6dfb\u52a0\u5217\"<\/p>\n

\u5728Python\u4e2d\u6dfb\u52a0\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u548c\u539f\u751fPython\u7b49\u65b9\u5f0f\u3002Pandas\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u7b80\u5355\u4e14\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002\u53ef\u4ee5\u901a\u8fc7\u76f4\u63a5\u8d4b\u503c\u3001\u4f7f\u7528assign\u65b9\u6cd5\u3001concat\u51fd\u6570\u7b49\u65b9\u5f0f\u6765\u6dfb\u52a0\u5217\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u901a\u8fc7\u5177\u4f53\u793a\u4f8b\u5c55\u793a\u5982\u4f55\u5728Python\u4e2d\u6709\u6548\u5730\u64cd\u4f5c\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93\u6dfb\u52a0\u5217<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u6700\u5e38\u7528\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5de5\u5177\u3002\u5728Pandas\u4e2d\uff0c\u6dfb\u52a0\u5217\u901a\u5e38\u662f\u5904\u7406DataFrame\u5bf9\u8c61\u7684\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n

    \n
  1. \u76f4\u63a5\u8d4b\u503c\u6dfb\u52a0\u65b0\u5217<\/strong><\/li>\n<\/ol>\n

    \u5728Pandas\u4e2d\uff0c\u6700\u7b80\u5355\u7684\u6dfb\u52a0\u5217\u7684\u65b9\u6cd5\u662f\u76f4\u63a5\u4e3aDataFrame\u5bf9\u8c61\u6307\u5b9a\u4e00\u4e2a\u65b0\u7684\u5217\u540d\u5e76\u8d4b\u503c\u3002\u53ef\u4ee5\u901a\u8fc7\u4e3a\u65b0\u5217\u63d0\u4f9b\u4e00\u4e2a\u5217\u8868\u6216\u5e8f\u5217\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n
    data = {'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
            'Age': [25, 30, 35]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u76f4\u63a5\u8d4b\u503c\u6dfb\u52a0\u65b0\u5217<\/strong><\/h2>\n
    df['City'] = ['New York', 'Los Angeles', 'Chicago']<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cCity<\/code>\u5217\u88ab\u6dfb\u52a0\u5230DataFrame\u4e2d\uff0c\u5305\u542b\u57ce\u5e02\u540d\u79f0\u3002<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528assign\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
      assign\u65b9\u6cd5\u662fPandas\u63d0\u4f9b\u7684\u53e6\u4e00\u79cd\u6dfb\u52a0\u5217\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5728\u94fe\u5f0f\u64cd\u4f5c\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
      # \u4f7f\u7528assign\u65b9\u6cd5\u6dfb\u52a0\u65b0\u5217<\/p>\n
      df = df.assign(Country=['USA', 'USA', 'USA'])<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      assign\u65b9\u6cd5\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684DataFrame\uff0c\u53ef\u4ee5\u5c06\u5176\u5206\u914d\u56de\u539f\u6765\u7684DataFrame\u53d8\u91cf\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u4f7f\u7528NumPy\u5e93\u6dfb\u52a0\u5217<\/p>\n<\/p>\n
      NumPy\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u7528\u4e8e\u6267\u884c\u5feb\u901f\u7684\u6570\u5b66\u8ba1\u7b97\u548c\u6570\u7ec4\u64cd\u4f5c\u3002\u5c3d\u7ba1NumPy\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \u901a\u8fc7hstack\u51fd\u6570\u6dfb\u52a0\u5217<\/strong><\/li>\n<\/ol>\n
        NumPy\u7684hstack\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u5c06\u6570\u7ec4\u6c34\u5e73\u5806\u53e0\uff0c\u4ece\u800c\u6dfb\u52a0\u65b0\u5217\u3002<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        \u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4<\/strong><\/h2>\n
        array = np.array([[1, 2], [3, 4], [5, 6]])<\/p>\n
        \u521b\u5efa\u8981\u6dfb\u52a0\u7684\u65b0\u5217<\/strong><\/h2>\n
        new_column = np.array([[7], [8], [9]])<\/p>\n
        \u4f7f\u7528hstack\u6dfb\u52a0\u65b0\u5217<\/strong><\/h2>\n
        result = np.hstack((array, new_column))<\/p>\n
        print(result)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cnew_column<\/code>\u88ab\u6dfb\u52a0\u5230array<\/code>\u7684\u672b\u5c3e\u3002<\/p>\n<\/p>\n
        \u4e09\u3001\u4f7f\u7528\u539f\u751fPython\u6dfb\u52a0\u5217<\/p>\n<\/p>\n
        \u5bf9\u4e8e\u5c0f\u578b\u6570\u636e\u96c6\u6216\u7b80\u5355\u7684\u6570\u636e\u64cd\u4f5c\uff0c\u4f7f\u7528\u539f\u751fPython\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u6dfb\u52a0\u5217\u7684\u529f\u80fd\u3002\u901a\u5e38\uff0c\u539f\u751fPython\u9002\u7528\u4e8e\u5217\u8868\u6216\u5b57\u5178\u7684\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
          \n
        1. \u4f7f\u7528\u5217\u8868\u6dfb\u52a0\u5217<\/strong><\/li>\n<\/ol>\n
          \u5982\u679c\u6570\u636e\u8868\u793a\u4e3a\u5217\u8868\u7684\u5217\u8868\uff0c\u53ef\u4ee5\u904d\u5386\u6bcf\u4e2a\u5b50\u5217\u8868\u5e76\u8ffd\u52a0\u65b0\u5143\u7d20\u3002<\/p>\n<\/p>\n
          # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e<\/p>\n
          data = [['Alice', 25], ['Bob', 30], ['Charlie', 35]]<\/p>\n
          \u6dfb\u52a0\u65b0\u5217<\/strong><\/h2>\n
          for row in data:<\/p>\n
              row.append('USA')<\/p>\n
          print(data)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u4f7f\u7528\u5b57\u5178\u6dfb\u52a0\u5217<\/strong><\/li>\n<\/ol>\n
            \u5bf9\u4e8e\u5b57\u5178\uff0c\u76f4\u63a5\u4e3a\u6bcf\u4e2a\u952e\u6dfb\u52a0\u65b0\u952e\u503c\u5bf9\u3002<\/p>\n<\/p>\n
            # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e<\/p>\n
            data = [{'Name': 'Alice', 'Age': 25},<\/p>\n
                    {'Name': 'Bob', 'Age': 30},<\/p>\n
                    {'Name': 'Charlie', 'Age': 35}]<\/p>\n
            \u6dfb\u52a0\u65b0\u5217<\/strong><\/h2>\n
            for entry in data:<\/p>\n
                entry['Country'] = 'USA'<\/p>\n
            print(data)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u56db\u3001Python\u4e2d\u6dfb\u52a0\u5217\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
              \n
            1. \u6570\u636e\u5bf9\u9f50<\/strong><\/li>\n<\/ol>\n
              \u5728\u4e3aDataFrame\u6216\u6570\u7ec4\u6dfb\u52a0\u65b0\u5217\u65f6\uff0c\u786e\u4fdd\u65b0\u5217\u7684\u6570\u636e\u957f\u5ea6\u4e0e\u73b0\u6709\u6570\u636e\u7684\u884c\u6570\u5339\u914d\u3002\u8fd9\u662f\u907f\u514d\u9519\u8bef\u548c\u4fdd\u8bc1\u6570\u636e\u5b8c\u6574\u6027\u7684\u5173\u952e\u3002<\/p>\n<\/p>\n
                \n
              1. \u6570\u636e\u7c7b\u578b\u4e00\u81f4\u6027<\/strong><\/li>\n<\/ol>\n
                \u6dfb\u52a0\u5217\u65f6\uff0c\u6ce8\u610f\u65b0\u5217\u7684\u6570\u636e\u7c7b\u578b\u662f\u5426\u4e0e\u6570\u636e\u7ed3\u6784\u7684\u9884\u671f\u7c7b\u578b\u4e00\u81f4\u3002\u8fd9\u6709\u52a9\u4e8e\u907f\u514d\u5728\u540e\u7eed\u6570\u636e\u5904\u7406\u9636\u6bb5\u51fa\u73b0\u95ee\u9898\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6027\u80fd\u8003\u8651<\/strong><\/li>\n<\/ol>\n
                  \u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\uff0c\u4f7f\u7528Pandas\u548cNumPy\u7b49\u5e93\u53ef\u4ee5\u63d0\u4f9b\u66f4\u597d\u7684\u6027\u80fd\u3002\u5b83\u4eec\u7684\u5e95\u5c42\u5b9e\u73b0\u662f\u7528C\u8bed\u8a00\u7f16\u5199\u7684\uff0c\u56e0\u6b64\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u94fe\u5f0f\u64cd\u4f5c<\/strong><\/li>\n<\/ol>\n
                    Pandas\u7684assign\u65b9\u6cd5\u5728\u94fe\u5f0f\u64cd\u4f5c\u4e2d\u975e\u5e38\u6709\u7528\uff0c\u53ef\u4ee5\u4f7f\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u548c\u6613\u8bfb\u3002<\/p>\n<\/p>\n
                    \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                    \u5728Python\u4e2d\u6dfb\u52a0\u5217\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u6570\u636e\u7ed3\u6784\u3002Pandas\u63d0\u4f9b\u4e86\u7b80\u5355\u800c\u5f3a\u5927\u7684\u5de5\u5177\u6765\u5904\u7406DataFrame\u6570\u636e\uff0c\u800cNumPy\u5219\u9002\u5408\u6570\u503c\u8ba1\u7b97\u548c\u6570\u7ec4\u64cd\u4f5c\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\uff0c\u539f\u751fPython\u4e5f\u80fd\u5f88\u597d\u5730\u5b8c\u6210\u4efb\u52a1\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7406\u89e3\u8fd9\u4e9b\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\u548c\u9002\u7528\u573a\u666f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u901a\u8fc7\u5b9e\u9645\u64cd\u4f5c\u548c\u7ec3\u4e60\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u638c\u63e1\u8fd9\u4e9b\u6280\u5de7\uff0c\u5e76\u5728\u65e5\u5e38\u6570\u636e\u5904\u7406\u4e2d\u5e94\u7528\u81ea\u5982\u3002<\/p>\n<\/p>\n
                    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                     \u5982\u4f55\u5728Python\u4e2d\u4e3aDataFrame\u6dfb\u52a0\u65b0\u5217\uff1f<\/strong>
                    \u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3aDataFrame\u6dfb\u52a0\u65b0\u5217\u3002\u53ef\u4ee5\u901a\u8fc7\u76f4\u63a5\u8d4b\u503c\u7684\u65b9\u5f0f\u6765\u521b\u5efa\u65b0\u5217\uff0c\u4f8b\u5982\uff1adf['\u65b0\u5217\u540d'] = \u503c<\/code>\uff0c\u5176\u4e2ddf<\/code>\u662f\u4f60\u7684DataFrame\uff0c\u65b0\u5217\u540d<\/code>\u662f\u4f60\u60f3\u6dfb\u52a0\u7684\u5217\u540d\uff0c\u503c<\/code>\u53ef\u4ee5\u662f\u4e00\u4e2a\u5217\u8868\u3001Series\u6216\u5176\u4ed6\u53ef\u8fed\u4ee3\u5bf9\u8c61\u3002\u8fd9\u6837\uff0c\u65b0\u7684\u5217\u5c31\u4f1a\u81ea\u52a8\u6269\u5c55\u4e3a\u4e0e\u539fDataFrame\u76f8\u540c\u7684\u884c\u6570\u3002<\/p>\n
                    \u5728\u6dfb\u52a0\u5217\u65f6\uff0c\u5982\u4f55\u4fdd\u8bc1\u65b0\u5217\u7684\u6570\u636e\u7c7b\u578b\u4e0e\u5176\u4ed6\u5217\u4e00\u81f4\uff1f<\/strong>
                    \u5728\u6dfb\u52a0\u65b0\u5217\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u63d0\u4f9b\u7684\u5404\u79cd\u6570\u636e\u7c7b\u578b\u51fd\u6570\u6765\u786e\u4fdd\u65b0\u5217\u7684\u6570\u636e\u7c7b\u578b\u4e00\u81f4\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u6765\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff0c\u5982\uff1adf['\u65b0\u5217\u540d'] = df['\u539f\u5217\u540d'].astype(float)<\/code>\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u65b0\u5217\u7684\u6570\u636e\u7c7b\u578b\u7b26\u5408\u4f60\u7684\u8981\u6c42\uff0c\u907f\u514d\u540e\u7eed\u7684\u6570\u636e\u5904\u7406\u51fa\u73b0\u95ee\u9898\u3002<\/p>\n
                    \u5982\u4f55\u5728\u6dfb\u52a0\u5217\u65f6\u8fdb\u884c\u6761\u4ef6\u5224\u65ad\uff1f<\/strong>
                    \u5728\u6dfb\u52a0\u5217\u65f6\uff0c\u53ef\u4ee5\u6839\u636e\u5176\u4ed6\u5217\u7684\u503c\u8fdb\u884c\u6761\u4ef6\u5224\u65ad\uff0c\u4f7f\u7528np.where()<\/code>\u6216apply()<\/code>\u65b9\u6cd5\u662f\u5e38\u89c1\u7684\u505a\u6cd5\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8fd9\u6837\u6dfb\u52a0\u4e00\u4e2a\u65b0\u5217\uff1adf['\u65b0\u5217'] = np.where(df['\u6761\u4ef6\u5217'] > 10, '\u6ee1\u8db3\u6761\u4ef6', '\u4e0d\u6ee1\u8db3\u6761\u4ef6')<\/code>\u3002\u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u5e2e\u52a9\u4f60\u6839\u636e\u73b0\u6709\u6570\u636e\u7684\u7279\u5b9a\u6761\u4ef6\u52a8\u6001\u521b\u5efa\u65b0\u5217\uff0c\u63d0\u9ad8\u6570\u636e\u7684\u53ef\u8bfb\u6027\u548c\u5206\u6790\u80fd\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6dfb\u52a0\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u548c\u539f\u751fPython\u7b49\u65b9\u5f0f\u3002Pandas […]","protected":false},"author":3,"featured_media":933678,"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\/933672"}],"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=933672"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/933672\/revisions"}],"predecessor-version":[{"id":933679,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/933672\/revisions\/933679"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/933678"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=933672"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=933672"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=933672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}