{"id":1096284,"date":"2025-01-08T14:59:46","date_gmt":"2025-01-08T06:59:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1096284.html"},"modified":"2025-01-08T14:59:49","modified_gmt":"2025-01-08T06:59:49","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8a%e5%88%97%e5%90%88%e5%b9%b6%e5%8d%95%e5%85%83%e6%a0%bc-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1096284.html","title":{"rendered":"python\u5982\u4f55\u628a\u5217\u5408\u5e76\u5355\u5143\u683c"},"content":{"rendered":"

\"python\u5982\u4f55\u628a\u5217\u5408\u5e76\u5355\u5143\u683c\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:<\/p>\n

Python\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u5c06\u5217\u5408\u5e76\u6210\u4e00\u4e2a\u5355\u5143\u683c\u3001\u53ef\u4ee5\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u6765\u5408\u5e76\u3001\u53ef\u4ee5\u4f7f\u7528apply\u51fd\u6570\u8fdb\u884c\u81ea\u5b9a\u4e49\u5408\u5e76<\/strong>\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u5b9e\u73b0\u5217\u7684\u5408\u5e76\uff0c\u5e76\u4e14\u4f1a\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u7528\u7684\u793a\u4f8b\u4ee3\u7801\u6765\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002\u5c24\u5176\u662f\uff0cpandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u4f7f\u5f97\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u53d8\u5f97\u66f4\u52a0\u65b9\u4fbf\u5feb\u6377\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u7684\u5b89\u88c5\u4e0e\u57fa\u7840\u4ecb\u7ecd<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u3001\u4fbf\u6377\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u5728\u5f00\u59cb\u4f7f\u7528\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u5b89\u88c5pandas\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\u5b83\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165pandas\u5e93\u6765\u5f00\u59cb\u4f7f\u7528\u5b83\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u6f14\u793a\u5217\u5408\u5e76\u64cd\u4f5c\uff0c\u6211\u4eec\u5148\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u548c\u5730\u5740\u4fe1\u606f\u7684\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    'First Name': ['John', 'Jane', 'Tom', 'Lucy'],<\/p>\n
    'Last Name': ['Doe', 'Smith', 'Brown', 'Taylor'],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
  First Name Last Name         City<\/p>\n
0       John       Doe     New York<\/p>\n
1       Jane     Smith  Los Angeles<\/p>\n
2        Tom     Brown      Chicago<\/p>\n
3       Lucy    Taylor      Houston<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528pandas\u5e93\u5408\u5e76\u5217<\/p>\n<\/p>\n
    \n
  1. \u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u6765\u5408\u5e76\u5217<\/strong><\/li>\n<\/ol>\n
    \u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5b57\u7b26\u4e32\u64cd\u4f5c\u5c06\u591a\u4e2a\u5217\u5408\u5e76\u6210\u4e00\u4e2a\u65b0\u7684\u5217\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u60f3\u5c06First Name<\/code>\u548cLast Name<\/code>\u5217\u5408\u5e76\u6210\u4e00\u4e2aFull Name<\/code>\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    df['Full Name'] = df['First Name'] + ' ' + df['Last Name']<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
      First Name Last Name         City     Full Name<\/p>\n
    0       John       Doe     New York     John Doe<\/p>\n
    1       Jane     Smith  Los Angeles   Jane Smith<\/p>\n
    2        Tom     Brown      Chicago     Tom Brown<\/p>\n
    3       Lucy    Taylor      Houston    Lucy Taylor<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528apply\u51fd\u6570\u8fdb\u884c\u81ea\u5b9a\u4e49\u5408\u5e76<\/strong><\/li>\n<\/ol>\n
      apply\u51fd\u6570\u5141\u8bb8\u6211\u4eec\u5bf9\u6bcf\u4e00\u884c\u6216\u6bcf\u4e00\u5217\u5e94\u7528\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u5408\u5e76\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u5982\u679c\u6211\u4eec\u60f3\u5c06City<\/code>\u5217\u7684\u4fe1\u606f\u4e5f\u5305\u542b\u5728Full Name<\/code>\u5217\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
      df['Full Info'] = df.apply(lambda row: f"{row['First Name']} {row['Last Name']} ({row['City']})", axis=1)<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
        First Name Last Name         City       Full Info<\/p>\n
      0       John       Doe     New York  John Doe (New York)<\/p>\n
      1       Jane     Smith  Los Angeles  Jane Smith (Los Angeles)<\/p>\n
      2        Tom     Brown      Chicago    Tom Brown (Chicago)<\/p>\n
      3       Lucy    Taylor      Houston  Lucy Taylor (Houston)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u5408\u5e76\u6307\u5b9a\u7684\u591a\u5217<\/p>\n<\/p>\n
      \u6709\u65f6\u5019\uff0c\u6211\u4eec\u9700\u8981\u5408\u5e76\u591a\u4e2a\u7279\u5b9a\u7684\u5217\u3002\u6bd4\u5982\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u66f4\u591a\u4fe1\u606f\u7684\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
      data = {<\/p>\n
          'First Name': ['John', 'Jane', 'Tom', 'Lucy'],<\/p>\n
          'Last Name': ['Doe', 'Smith', 'Brown', 'Taylor'],<\/p>\n
          'Street': ['123 Elm St', '456 Oak St', '789 Pine St', '101 Maple St'],<\/p>\n
          'City': ['New York', 'Los Angeles', 'Chicago', 'Houston'],<\/p>\n
          'State': ['NY', 'CA', 'IL', 'TX'],<\/p>\n
          'Zip': ['10001', '90001', '60601', '77001']<\/p>\n
      }<\/p>\n
      df = pd.DataFrame(data)<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
        First Name Last Name      Street         City State    Zip<\/p>\n
      0       John       Doe  123 Elm St     New York    NY  10001<\/p>\n
      1       Jane     Smith  456 Oak St  Los Angeles    CA  90001<\/p>\n
      2        Tom     Brown  789 Pine St      Chicago    IL  60601<\/p>\n
      3       Lucy    Taylor 101 Maple St      Houston    TX  77001<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u6211\u4eec\u53ef\u4ee5\u5408\u5e76Street<\/code>\u3001City<\/code>\u3001State<\/code>\u548cZip<\/code>\u5217\u6210\u4e00\u4e2aAddress<\/code>\u5217\uff1a<\/p>\n<\/p>\n
      df['Address'] = df['Street'] + ', ' + df['City'] + ', ' + df['State'] + ' ' + df['Zip']<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
        First Name Last Name      Street         City State    Zip                        Address<\/p>\n
      0       John       Doe  123 Elm St     New York    NY  10001     123 Elm St, New York, NY 10001<\/p>\n
      1       Jane     Smith  456 Oak St  Los Angeles    CA  90001  456 Oak St, Los Angeles, CA 90001<\/p>\n
      2        Tom     Brown  789 Pine St      Chicago    IL  60601       789 Pine St, Chicago, IL 60601<\/p>\n
      3       Lucy    Taylor 101 Maple St      Houston    TX  77001      101 Maple St, Houston, TX 77001<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e94\u3001\u5904\u7406\u7f3a\u5931\u503c<\/p>\n<\/p>\n
      \u5728\u5b9e\u9645\u6570\u636e\u4e2d\uff0c\u7ecf\u5e38\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u7684\u60c5\u51b5\u3002\u5408\u5e76\u5217\u65f6\uff0c\u6211\u4eec\u9700\u8981\u5904\u7406\u8fd9\u4e9b\u7f3a\u5931\u503c\uff0c\u4ee5\u907f\u514d\u5408\u5e76\u540e\u7684\u7ed3\u679c\u51fa\u73b0\u9519\u8bef\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      data = {<\/p>\n
          'First Name': ['John', 'Jane', 'Tom', 'Lucy'],<\/p>\n
          'Last Name': ['Doe', 'Smith', 'Brown', None],<\/p>\n
          'City': ['New York', 'Los Angeles', None, 'Houston']<\/p>\n
      }<\/p>\n
      df = pd.DataFrame(data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e3a\u4e86\u89e3\u51b3\u7f3a\u5931\u503c\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528fillna\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
      df['Last Name'].fillna('', inplace=True)<\/p>\n
      df['City'].fillna('Unknown', inplace=True)<\/p>\n
      df['Full Info'] = df.apply(lambda row: f"{row['First Name']} {row['Last Name']} ({row['City']})", axis=1)<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
        First Name Last Name         City              Full Info<\/p>\n
      0       John       Doe     New York          John Doe (New York)<\/p>\n
      1       Jane     Smith  Los Angeles      Jane Smith (Los Angeles)<\/p>\n
      2        Tom     Brown      Unknown          Tom Brown (Unknown)<\/p>\n
      3       Lucy                Houston            Lucy  (Houston)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u516d\u3001\u5408\u5e76\u5217\u7684\u66f4\u591a\u5b9e\u7528\u6280\u5de7<\/p>\n<\/p>\n
        \n
      1. \u5408\u5e76\u7279\u5b9a\u683c\u5f0f\u7684\u5217<\/strong><\/li>\n<\/ol>\n
        \u6709\u65f6\u5019\u6211\u4eec\u9700\u8981\u5408\u5e76\u5217\u5e76\u4fdd\u6301\u7279\u5b9a\u7684\u683c\u5f0f\uff0c\u6bd4\u5982\u65e5\u671f\u548c\u65f6\u95f4\uff1a<\/p>\n<\/p>\n
        data = {<\/p>\n
            'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'],<\/p>\n
            'Time': ['08:00', '09:00', '10:00', '11:00']<\/p>\n
        }<\/p>\n
        df = pd.DataFrame(data)<\/p>\n
        df['DateTime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])<\/p>\n
        print(df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
                 Date   Time            DateTime<\/p>\n
        0  2023-01-01  08:00 2023-01-01 08:00:00<\/p>\n
        1  2023-01-02  09:00 2023-01-02 09:00:00<\/p>\n
        2  2023-01-03  10:00 2023-01-03 10:00:00<\/p>\n
        3  2023-01-04  11:00 2023-01-04 11:00:00<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5408\u5e76\u5217\u5e76\u4fdd\u7559\u539f\u59cb\u5217<\/strong><\/li>\n<\/ol>\n
          \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u5e0c\u671b\u5408\u5e76\u5217\u4f46\u4fdd\u7559\u539f\u59cb\u5217\u3002\u53ef\u4ee5\u76f4\u63a5\u5728\u6570\u636e\u6846\u4e2d\u65b0\u5efa\u4e00\u5217\u6765\u5b58\u653e\u5408\u5e76\u540e\u7684\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
          df['Full Name'] = df['First Name'] + ' ' + df['Last Name']<\/p>\n
          print(df)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
            First Name Last Name         City     Full Name<\/p>\n
          0       John       Doe     New York     John Doe<\/p>\n
          1       Jane     Smith  Los Angeles   Jane Smith<\/p>\n
          2        Tom     Brown      Chicago     Tom Brown<\/p>\n
          3       Lucy    Taylor      Houston    Lucy Taylor<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u5408\u5e76\u5217\u5e76\u5220\u9664\u539f\u59cb\u5217<\/strong><\/li>\n<\/ol>\n
            \u5982\u679c\u6211\u4eec\u4e0d\u518d\u9700\u8981\u539f\u59cb\u5217\uff0c\u53ef\u4ee5\u5728\u5408\u5e76\u540e\u5220\u9664\u5b83\u4eec\uff1a<\/p>\n<\/p>\n
            df['Full Info'] = df['First Name'] + ' ' + df['Last Name'] + ' (' + df['City'] + ')'<\/p>\n
            df.drop(columns=['First Name', 'Last Name', 'City'], inplace=True)<\/p>\n
            print(df)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
                          Full Info<\/p>\n
            0     John Doe (New York)<\/p>\n
            1  Jane Smith (Los Angeles)<\/p>\n
            2     Tom Brown (Chicago)<\/p>\n
            3    Lucy Taylor (Houston)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e03\u3001\u603b\u7ed3<\/p>\n<\/p>\n
            \u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u8ba8\u8bba\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528pandas\u5e93\u5c06\u5217\u5408\u5e76\u6210\u4e00\u4e2a\u5355\u5143\u683c\u3002\u6211\u4eec\u4ecb\u7ecd\u4e86\u51e0\u79cd\u4e0d\u540c\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u3001apply\u51fd\u6570\u4ee5\u53ca\u5904\u7406\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5c55\u793a\u4e86\u4e00\u4e9b\u5b9e\u9645\u7684\u5e94\u7528\u573a\u666f\u548c\u6280\u5de7\uff0c\u5982\u5408\u5e76\u7279\u5b9a\u683c\u5f0f\u7684\u5217\u3001\u4fdd\u7559\u6216\u5220\u9664\u539f\u59cb\u5217\u7b49\u3002<\/p>\n<\/p>\n
            \u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u6280\u5de7\uff0c\u4f60\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u6570\u636e\uff0c\u4ece\u800c\u4e3a\u4f60\u7684\u6570\u636e\u5206\u6790\u5de5\u4f5c\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u5e76\u80fd\u591f\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u89e3\u51b3\u95ee\u9898\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Pandas\u5408\u5e76DataFrame\u4e2d\u7684\u5217\uff1f<\/strong>
            \u5728Pandas\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528DataFrame<\/code>\u7684assign()<\/code>\u65b9\u6cd5\u6216\u76f4\u63a5\u5bf9\u5217\u8fdb\u884c\u64cd\u4f5c\u6765\u5408\u5e76\u591a\u4e2a\u5217\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u6709\u4e00\u4e2aDataFrame\u5e76\u5e0c\u671b\u5c06column1<\/code>\u548ccolumn2<\/code>\u5408\u5e76\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a  <\/p>\n
            import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8bDataFrame\ndf = pd.DataFrame({'column1': ['A', 'B', 'C'], 'column2': ['D', 'E', 'F']})\n\n# \u5408\u5e76\u5217\ndf['merged'] = df['column1'] + df['column2']\nprint(df)\n<\/code><\/pre>\n
            \u8fd9\u6837\u4f1a\u521b\u5efa\u4e00\u4e2a\u65b0\u5217merged<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u5408\u5e76\u540e\u7684\u5b57\u7b26\u4e32\u3002<\/p>\n
            \u5728Excel\u4e2d\u5982\u4f55\u4f7f\u7528Python\u5408\u5e76\u5355\u5143\u683c\uff1f<\/strong>
            \u5982\u679c\u60a8\u7684\u76ee\u6807\u662f\u5408\u5e76Excel\u6587\u4ef6\u4e2d\u7684\u5355\u5143\u683c\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528openpyxl<\/code>\u5e93\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a  <\/p>\n
            from openpyxl import Workbook\n\n# \u521b\u5efa\u5de5\u4f5c\u7c3f\u548c\u5de5\u4f5c\u8868\nwb = Workbook()\nws = wb.active\n\n# \u6dfb\u52a0\u6570\u636e\nws['A1'] = '\u5408\u5e76\u7684\u5355\u5143\u683c\u5185\u5bb9'\n\n# \u5408\u5e76\u5355\u5143\u683c\nws.merge_cells('A1:B1')\n\n# \u4fdd\u5b58\u6587\u4ef6\nwb.save('merged_cells.xlsx')\n<\/code><\/pre>\n
            \u8fd9\u6bb5\u4ee3\u7801\u4f1a\u5728Excel\u4e2d\u5408\u5e76A1\u548cB1\u5355\u5143\u683c\u3002<\/p>\n
            \u5728\u5408\u5e76\u5217\u65f6\u5982\u4f55\u5904\u7406\u7a7a\u503c\uff1f<\/strong>
            \u5408\u5e76\u5217\u65f6\uff0c\u5904\u7406\u7a7a\u503c\u975e\u5e38\u91cd\u8981\u3002\u4f7f\u7528Pandas\u65f6\uff0c\u53ef\u4ee5\u7528fillna()<\/code>\u65b9\u6cd5\u66ff\u6362\u7a7a\u503c\uff0c\u4ee5\u786e\u4fdd\u5408\u5e76\u540e\u7684\u7ed3\u679c\u4e0d\u4f1a\u51fa\u73b0\u610f\u5916\u7684NaN<\/code>\u3002\u4f8b\u5982\uff1a  <\/p>\n
            df['merged'] = df['column1'].fillna('') + df['column2'].fillna('')\n<\/code><\/pre>\n
            \u8fd9\u6837\u53ef\u4ee5\u6709\u6548\u907f\u514d\u5408\u5e76\u7ed3\u679c\u4e2d\u7684\u7a7a\u503c\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d: Python\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u5c06\u5217\u5408\u5e76\u6210\u4e00\u4e2a\u5355\u5143\u683c\u3001\u53ef\u4ee5\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u6765\u5408\u5e76\u3001\u53ef\u4ee5\u4f7f\u7528appl […]","protected":false},"author":3,"featured_media":1096290,"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\/1096284"}],"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=1096284"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1096284\/revisions"}],"predecessor-version":[{"id":1096291,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1096284\/revisions\/1096291"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1096290"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1096284"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1096284"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1096284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}