{"id":958066,"date":"2024-12-27T03:14:38","date_gmt":"2024-12-26T19:14:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958066.html"},"modified":"2024-12-27T03:14:40","modified_gmt":"2024-12-26T19:14:40","slug":"python%e6%b2%a1%e6%9c%89%e5%88%97%e5%90%8d%e5%a6%82%e4%bd%95%e6%96%b0%e5%a2%9e","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958066.html","title":{"rendered":"python\u6ca1\u6709\u5217\u540d\u5982\u4f55\u65b0\u589e"},"content":{"rendered":"

\"python\u6ca1\u6709\u5217\u540d\u5982\u4f55\u65b0\u589e\"<\/p>\n

\u8981\u5728Python\u4e2d\u4e3a\u6ca1\u6709\u5217\u540d\u7684DataFrame\u65b0\u589e\u5217\u540d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\u7684columns<\/code>\u5c5e\u6027\u6765\u8bbe\u5b9a\u65b0\u5217\u540d\u3001\u5229\u7528rename<\/code>\u65b9\u6cd5\u4e3a\u7279\u5b9a\u5217\u8bbe\u7f6e\u540d\u79f0\u3001\u7ed3\u5408\u6570\u636e\u5206\u6790\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5217\u540d<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528columns<\/code>\u5c5e\u6027\u662f\u6700\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u5c24\u5176\u662f\u5728\u4f7f\u7528Pandas\u5e93\u65f6\uff0c\u7ecf\u5e38\u4f1a\u9047\u5230\u9700\u8981\u4e3aDataFrame\u65b0\u589e\u5217\u540d\u7684\u60c5\u51b5\u3002\u4e00\u4e2aDataFrame\u53ef\u80fd\u5728\u8bfb\u53d6\u6570\u636e\u65f6\u7531\u4e8e\u6e90\u6587\u4ef6\u6ca1\u6709\u5217\u5934\u6216\u8005\u5176\u4ed6\u539f\u56e0\u5bfc\u81f4\u6ca1\u6709\u5217\u540d\u3002\u8fd9\u65f6\u5019\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528COLUMNS\u5c5e\u6027\u76f4\u63a5\u8bbe\u5b9a\u5217\u540d<\/h3>\n<\/p>\n

\u4f7f\u7528Pandas\u7684columns<\/code>\u5c5e\u6027\u76f4\u63a5\u8bbe\u5b9a\u5217\u540d\u662f\u6700\u76f4\u63a5\u7684\u65b9\u5f0f\u3002\u5047\u8bbe\u6211\u4eec\u5df2\u7ecf\u6709\u4e00\u4e2a\u6ca1\u6709\u5217\u540d\u7684DataFrame\uff0c\u901a\u8fc7\u4e3acolumns<\/code>\u5c5e\u6027\u8d4b\u503c\u4e00\u4e2a\u5305\u542b\u65b0\u5217\u540d\u7684\u5217\u8868\uff0c\u5c31\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3aDataFrame\u65b0\u589e\u5217\u540d\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6ca1\u6709\u5217\u540d\u7684DataFrame<\/strong><\/h2>\n
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u8bbe\u7f6e\u65b0\u5217\u540d<\/strong><\/h2>\n
df.columns = ['Column1', 'Column2', 'Column3']<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u8981\u6c42\u6211\u4eec\u77e5\u9053DataFrame\u7684\u5217\u6570\uff0c\u5e76\u4e14\u63d0\u4f9b\u7b49\u91cf\u7684\u5217\u540d\u5217\u8868\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528RENAME\u65b9\u6cd5\u4e3a\u7279\u5b9a\u5217\u8bbe\u7f6e\u540d\u79f0<\/h3>\n<\/p>\n
\u5982\u679c\u6211\u4eec\u53ea\u60f3\u4e3a\u7279\u5b9a\u5217\u8bbe\u7f6e\u540d\u79f0\u6216\u8005\u60f3\u8981\u66f4\u6539\u73b0\u6709\u7684\u5217\u540d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684rename<\/code>\u65b9\u6cd5\u3002rename<\/code>\u65b9\u6cd5\u63d0\u4f9b\u4e86\u66f4\u5927\u7684\u7075\u6d3b\u6027\uff0c\u5141\u8bb8\u6211\u4eec\u53ea\u4fee\u6539\u90e8\u5206\u5217\u7684\u540d\u79f0\u3002<\/p>\n<\/p>\n
# \u5047\u8bbedf\u662f\u4e00\u4e2a\u5df2\u6709\u7684DataFrame\uff0c\u6211\u4eec\u53ef\u4ee5\u8fd9\u6837\u91cd\u547d\u540d\u5217<\/p>\n
df = df.rename(columns={0: 'Column1', 1: 'Column2', 2: 'Column3'})<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u9002\u5408\u5728\u5904\u7406\u5927\u578bDataFrame\u65f6\uff0c\u53ea\u9700\u8981\u4fee\u6539\u67d0\u4e9b\u5217\u540d\uff0c\u800c\u4e0d\u9700\u8981\u91cd\u65b0\u8bbe\u7f6e\u6240\u6709\u7684\u5217\u540d\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408\u6570\u636e\u5206\u6790\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5217\u540d<\/h3>\n<\/p>\n
\u5728\u4e3aDataFrame\u65b0\u589e\u5217\u540d\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u5217\u540d\u5bf9\u4e8e\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u81f3\u5173\u91cd\u8981\u3002\u5217\u540d\u4e0d\u4ec5\u4ec5\u662fDataFrame\u7684\u6807\u8bc6\u7b26\uff0c\u5b83\u4eec\u8fd8\u53ef\u4ee5\u63d0\u4f9b\u5173\u4e8e\u6570\u636e\u5185\u5bb9\u7684\u6709\u4ef7\u503c\u4fe1\u606f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
    \n
  1. \n
    \u7b80\u6d01\u660e\u4e86<\/strong>\uff1a\u5217\u540d\u5e94\u5f53\u7b80\u6d01\u660e\u4e86\uff0c\u80fd\u591f\u53cd\u6620\u6570\u636e\u7684\u5b9e\u9645\u542b\u4e49\u3002\u907f\u514d\u4f7f\u7528\u8fc7\u4e8e\u590d\u6742\u6216\u4e0d\u76f4\u89c2\u7684\u540d\u79f0\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u7edf\u4e00\u683c\u5f0f<\/strong>\uff1a\u786e\u4fdd\u6240\u6709\u5217\u540d\u9075\u5faa\u7edf\u4e00\u7684\u547d\u540d\u7ea6\u5b9a\uff0c\u6bd4\u5982\u5168\u5c0f\u5199\u3001\u4f7f\u7528\u4e0b\u5212\u7ebf\u8fde\u63a5\u7b49\uff0c\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
  3. \n
    \u907f\u514d\u51b2\u7a81<\/strong>\uff1a\u786e\u4fdd\u65b0\u6dfb\u52a0\u7684\u5217\u540d\u4e0d\u4f1a\u4e0e\u73b0\u6709\u7684\u5217\u540d\u51b2\u7a81\uff0c\u5426\u5219\u53ef\u80fd\u5bfc\u81f4\u6570\u636e\u6df7\u6dc6\u3002<\/p>\n<\/p>\n<\/li>\n
  4. \n
    \u9002\u5e94\u6027\u5f3a<\/strong>\uff1a\u9009\u62e9\u7684\u5217\u540d\u5e94\u5f53\u5177\u6709\u9002\u5e94\u6027\uff0c\u80fd\u591f\u9002\u5e94\u6570\u636e\u7684\u6269\u5c55\u548c\u53d8\u5316\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u56db\u3001\u5229\u7528\u5916\u90e8\u6570\u636e\u6e90\u52a8\u6001\u751f\u6210\u5217\u540d<\/h3>\n<\/p>\n
    \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5217\u540d\u53ef\u80fd\u9700\u8981\u6839\u636e\u5916\u90e8\u6570\u636e\u6e90\u52a8\u6001\u751f\u6210\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u8bfb\u53d6\u7684CSV\u6587\u4ef6\u7684\u7b2c\u4e00\u884c\u662f\u5217\u540d\uff0c\u800c\u4e0d\u662f\u6570\u636e\u3002\u8fd9\u65f6\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
    # \u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u5c06\u7b2c\u4e00\u884c\u4f5c\u4e3a\u5217\u540d<\/p>\n
    df = pd.read_csv('data.csv', header=0)<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u6307\u5b9aheader=0<\/code>\u53c2\u6570\uff0cPandas\u4f1a\u81ea\u52a8\u5c06\u7b2c\u4e00\u884c\u6570\u636e\u4f5c\u4e3aDataFrame\u7684\u5217\u540d\u3002\u5982\u679cCSV\u6587\u4ef6\u6ca1\u6709\u5217\u540d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7header=None<\/code>\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u624b\u52a8\u6307\u5b9a\u5217\u540d\u3002<\/p>\n<\/p>\n
    \u4e94\u3001\u5904\u7406\u591a\u7ea7\u7d22\u5f15\u7684\u5217\u540d<\/h3>\n<\/p>\n
    \u5bf9\u4e8e\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\uff0c\u6211\u4eec\u53ef\u80fd\u4f1a\u7528\u5230\u591a\u7ea7\u7d22\u5f15\uff08MultiIndex\uff09\u7684DataFrame\u3002\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5217\u540d\u4f1a\u7531\u591a\u4e2a\u7ea7\u522b\u7ec4\u6210\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5904\u7406\u591a\u7ea7\u7d22\u5f15\u7684\u5217\u540d\uff1a<\/p>\n<\/p>\n
    # \u521b\u5efa\u4e00\u4e2a\u591a\u7ea7\u7d22\u5f15\u7684DataFrame<\/p>\n
    index = pd.MultiIndex.from_tuples([('A', 'Cat'), ('A', 'Dog'), ('B', 'Cat'), ('B', 'Dog')])<\/p>\n
    data = [[1, 2], [3, 4], [5, 6], [7, 8]]<\/p>\n
    df = pd.DataFrame(data, index=index, columns=['Value1', 'Value2'])<\/p>\n
    \u67e5\u770b\u591a\u7ea7\u7d22\u5f15\u7684\u5217\u540d<\/strong><\/h2>\n
    print(df)<\/p>\n
    \u91cd\u547d\u540d\u591a\u7ea7\u7d22\u5f15\u7684\u5217\u540d<\/strong><\/h2>\n
    df.columns = pd.MultiIndex.from_tuples([('Info', 'Value1'), ('Info', 'Value2')])<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5904\u7406\u591a\u7ea7\u7d22\u5f15\u7684\u5217\u540d\u9700\u8981\u4f7f\u7528MultiIndex<\/code>\u5bf9\u8c61\uff0c\u8fd9\u4e3a\u6570\u636e\u7684\u5c42\u6b21\u5316\u7ec4\u7ec7\u63d0\u4f9b\u4e86\u7075\u6d3b\u6027\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7ba1\u7406\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u3002<\/p>\n<\/p>\n
    \u516d\u3001\u603b\u7ed3\u4e0e\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
    \u4e3aDataFrame\u65b0\u589e\u5217\u540d\u662f\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\u7684\u91cd\u8981\u6b65\u9aa4\u4e4b\u4e00\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u6839\u636e\u6570\u636e\u7684\u7279\u70b9\u548c\u5206\u6790\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u65b0\u589e\u5217\u540d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
      \n
    • \u4e86\u89e3\u6570\u636e\u6765\u6e90<\/strong>\uff1a\u5728\u4e3aDataFrame\u65b0\u589e\u5217\u540d\u4e4b\u524d\uff0c\u9996\u5148\u8981\u4e86\u89e3\u6570\u636e\u7684\u6765\u6e90\u548c\u7ed3\u6784\uff0c\u4ee5\u4fbf\u4e3a\u5176\u9009\u62e9\u5408\u9002\u7684\u5217\u540d\u3002<\/li>\n
    • \u4f7f\u7528\u81ea\u52a8\u5316\u5de5\u5177<\/strong>\uff1a\u5229\u7528Pandas\u7b49\u6570\u636e\u5904\u7406\u5de5\u5177\u7684\u81ea\u52a8\u5316\u7279\u6027\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u65b0\u589e\u5217\u540d\u7684\u6548\u7387\u3002<\/li>\n
    • \u8003\u8651\u6570\u636e\u6269\u5c55\u6027<\/strong>\uff1a\u5728\u9009\u62e9\u5217\u540d\u65f6\uff0c\u8981\u8003\u8651\u6570\u636e\u7684\u672a\u6765\u6269\u5c55\u6027\uff0c\u4ee5\u907f\u514d\u9891\u7e41\u4fee\u6539\u5217\u540d\u3002<\/li>\n
    • \u4fdd\u6301\u53ef\u8bfb\u6027\u548c\u4e00\u81f4\u6027<\/strong>\uff1a\u5217\u540d\u5e94\u4fdd\u6301\u7b80\u6d01\u3001\u53ef\u8bfb\uff0c\u5e76\u9075\u5faa\u4e00\u81f4\u7684\u547d\u540d\u89c4\u5219\uff0c\u8fd9\u5bf9\u4e8e\u5927\u578b\u9879\u76ee\u5c24\u4e3a\u91cd\u8981\u3002<\/li>\n<\/ul>\n
      \u901a\u8fc7\u9075\u5faa\u8fd9\u4e9b\u539f\u5219\uff0c\u6211\u4eec\u53ef\u4ee5\u786e\u4fdd\u4e3aDataFrame\u65b0\u589e\u7684\u5217\u540d\u65e2\u7b26\u5408\u6570\u636e\u5206\u6790\u7684\u9700\u6c42\uff0c\u53c8\u5177\u6709\u826f\u597d\u7684\u53ef\u8bfb\u6027\u548c\u6269\u5c55\u6027\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5728\u6ca1\u6709\u5217\u540d\u7684\u60c5\u51b5\u4e0b\u4e3aPython\u6570\u636e\u6846\u6dfb\u52a0\u5217\u540d\uff1f<\/strong>
      \u5728\u4f7f\u7528Pandas\u5e93\u5904\u7406\u6570\u636e\u65f6\uff0c\u5982\u679c\u6570\u636e\u6846\u6ca1\u6709\u5217\u540d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6ecolumns<\/code>\u53c2\u6570\u5728\u521b\u5efa\u6570\u636e\u6846\u65f6\u6307\u5b9a\u5217\u540d\uff0c\u6216\u8005\u4f7f\u7528df.columns<\/code>\u5c5e\u6027\u6765\u4fee\u6539\u73b0\u6709\u6570\u636e\u6846\u7684\u5217\u540d\u3002\u4f8b\u5982\uff1a<\/p>\n
      import pandas as pd\n\n# \u521b\u5efa\u6ca1\u6709\u5217\u540d\u7684\u6570\u636e\u6846\ndata = [[1, 2], [3, 4]]\ndf = pd.DataFrame(data)\n\n# \u6dfb\u52a0\u5217\u540d\ndf.columns = ['Column1', 'Column2']\n<\/code><\/pre>\n
      \u5728\u5904\u7406CSV\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u4e3a\u6ca1\u6709\u5217\u540d\u7684\u6570\u636e\u6dfb\u52a0\u5217\u540d\uff1f<\/strong>
      \u5f53\u8bfb\u53d6\u4e00\u4e2a\u6ca1\u6709\u5217\u540d\u7684CSV\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u5728\u4f7f\u7528pd.read_csv()<\/code>\u65f6\u8bbe\u7f6eheader=None<\/code>\u6765\u6307\u793aPandas\u4e0d\u5c06\u7b2c\u4e00\u884c\u89c6\u4e3a\u5217\u540d\uff0c\u5e76\u5728\u8bfb\u53d6\u540e\u76f4\u63a5\u4e3a\u6570\u636e\u6846\u5206\u914d\u5217\u540d\u3002\u4f8b\u5982\uff1a<\/p>\n
      df = pd.read_csv('file.csv', header=None)\ndf.columns = ['Column1', 'Column2']\n<\/code><\/pre>\n
      \u6709\u6ca1\u6709\u65b9\u6cd5\u53ef\u4ee5\u52a8\u6001\u5730\u4e3a\u6ca1\u6709\u5217\u540d\u7684\u6570\u636e\u6846\u751f\u6210\u5217\u540d\uff1f<\/strong>
      \u53ef\u4ee5\u6839\u636e\u6570\u636e\u6846\u7684\u5f62\u72b6\u52a8\u6001\u751f\u6210\u5217\u540d\uff0c\u4f7f\u7528Python\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u6765\u521b\u5efa\u5217\u540d\u3002\u4f8b\u5982\uff0c\u5982\u679c\u6570\u636e\u6846\u67093\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
      num_columns = df.shape[1]\ndf.columns = [f'Column{i+1}' for i in range(num_columns)]\n<\/code><\/pre>\n
      \u8fd9\u6837\u53ef\u4ee5\u81ea\u52a8\u4e3a\u6bcf\u4e00\u5217\u751f\u6210\u5217\u540d\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u4e3a\u6ca1\u6709\u5217\u540d\u7684DataFrame\u65b0\u589e\u5217\u540d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\u7684columns\u5c5e\u6027\u6765\u8bbe\u5b9a […]","protected":false},"author":3,"featured_media":958069,"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\/958066"}],"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=958066"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/958066\/revisions"}],"predecessor-version":[{"id":958070,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/958066\/revisions\/958070"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/958069"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=958066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=958066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=958066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}