{"id":1086141,"date":"2025-01-08T13:22:52","date_gmt":"2025-01-08T05:22:52","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1086141.html"},"modified":"2025-01-08T13:22:54","modified_gmt":"2025-01-08T05:22:54","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a9%e8%a1%a8%e4%b8%ad%e6%95%b0%e6%8d%ae%e5%89%8d%e7%a7%bb-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1086141.html","title":{"rendered":"python\u5982\u4f55\u8ba9\u8868\u4e2d\u6570\u636e\u524d\u79fb"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba9\u8868\u4e2d\u6570\u636e\u524d\u79fb\"<\/p>\n

\u4e00\u3001\u4f7f\u7528Python\u8ba9\u8868\u4e2d\u6570\u636e\u524d\u79fb\u7684\u65b9\u6cd5<\/strong>\u6709\u5f88\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528Numpy\u5e93\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\u3001\u4ee5\u53ca\u901a\u8fc7\u5217\u8868\u89e3\u6790\u7b49\u65b9\u5f0f\u8fdb\u884c\u64cd\u4f5c\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\u4e4b\u4e00<\/strong>\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7075\u6d3b\u7684\u64cd\u4f5c\u65b9\u5f0f\u3002<\/p>\n<\/p>\n

Pandas\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53d8\u6362\u3001\u4ee5\u53ca\u5404\u79cd\u6570\u636e\u5206\u6790\u4efb\u52a1<\/strong>\u3002\u5728\u4f7f\u7528Pandas\u5e93\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7DataFrame\u7684\u5404\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u6570\u636e\u524d\u79fb\u7684\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\uff0c\u5e76\u4e3e\u4f8b\u8bf4\u660e\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001PANDAS\u5e93\u7684\u4f7f\u7528<\/h2>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u64cd\u4f5c\u5de5\u5177\u3002\u4f7f\u7528Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6\u3001\u5904\u7406\u548c\u64cd\u4f5c\u6570\u636e\u8868\u3002\u4e0b\u9762\u662f\u4f7f\u7528Pandas\u5e93\u5c06\u8868\u4e2d\u6570\u636e\u524d\u79fb\u7684\u8be6\u7ec6\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

1\u3001\u5bfc\u5165Pandas\u5e93<\/h3>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Pandas\u5e93\u3002\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\uff0c\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efaDataFrame<\/h3>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame\u6765\u6f14\u793a\u6570\u636e\u524d\u79fb\u7684\u64cd\u4f5c\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5217\u7684\u6570\u636e\u8868\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [6, 7, 8, 9, 10],<\/p>\n
    'C': [11, 12, 13, 14, 15]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
   A   B   C<\/p>\n
0  1   6  11<\/p>\n
1  2   7  12<\/p>\n
2  3   8  13<\/p>\n
3  4   9  14<\/p>\n
4  5  10  15<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5b9e\u73b0\u6570\u636e\u524d\u79fb<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5c06\u8868\u4e2d\u7684\u6570\u636e\u524d\u79fb\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
df_shifted = df.shift(-1).fillna(method='ffill')<\/p>\n
print(df_shifted)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86shift<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u5e76\u4f7f\u7528fillna<\/code>\u65b9\u6cd5\u586b\u5145\u7a7a\u7f3a\u503c\u3002\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
     A    B     C<\/p>\n
0  2.0  7.0  12.0<\/p>\n
1  3.0  8.0  13.0<\/p>\n
2  4.0  9.0  14.0<\/p>\n
3  5.0 10.0  15.0<\/p>\n
4  5.0 10.0  15.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u89e3\u91ca\u4ee3\u7801<\/h3>\n<\/p>\n
    \n
  • shift\u65b9\u6cd5<\/strong>\uff1adf.shift(-1)<\/code>\u5c06\u6240\u6709\u884c\u7684\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u6700\u540e\u4e00\u884c\u4f1a\u53d8\u4e3aNaN\u3002<\/li>\n
  • fillna\u65b9\u6cd5<\/strong>\uff1afillna(method='ffill')<\/code>\u7528\u524d\u4e00\u884c\u7684\u6570\u636e\u586b\u5145NaN\u503c\uff0c\u786e\u4fdd\u6570\u636e\u5b8c\u6574\u3002<\/li>\n<\/ul>\n
    \n
    \u4e8c\u3001NUMPY\u5e93\u7684\u4f7f\u7528<\/h2>\n<\/p>\n
    \u9664\u4e86Pandas\u5e93\uff0cNumpy\u5e93\u4e5f\u662f\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u7684\u5f3a\u5927\u5de5\u5177\u3002Numpy\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
    1\u3001\u5bfc\u5165Numpy\u5e93<\/h3>\n<\/p>\n
    \u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Numpy\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
    pip install numpy<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5bfc\u5165Numpy\u5e93\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u521b\u5efaNumpy\u6570\u7ec4<\/h3>\n<\/p>\n
    \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5217\u7684Numpy\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
    data = np.array([<\/p>\n
        [1, 6, 11],<\/p>\n
        [2, 7, 12],<\/p>\n
        [3, 8, 13],<\/p>\n
        [4, 9, 14],<\/p>\n
        [5, 10, 15]<\/p>\n
    ])<\/p>\n
    print(data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    [[ 1  6 11]<\/p>\n
     [ 2  7 12]<\/p>\n
     [ 3  8 13]<\/p>\n
     [ 4  9 14]<\/p>\n
     [ 5 10 15]]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u5b9e\u73b0\u6570\u636e\u524d\u79fb<\/h3>\n<\/p>\n
    \u4e3a\u4e86\u5c06\u6570\u7ec4\u4e2d\u7684\u6570\u636e\u524d\u79fb\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    data_shifted = np.roll(data, -1, axis=0)<\/p>\n
    data_shifted[-1] = data_shifted[-2]<\/p>\n
    print(data_shifted)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86roll<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u5e76\u624b\u52a8\u586b\u5145\u6700\u540e\u4e00\u884c\u7684\u6570\u636e\u3002\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    [[ 2  7 12]<\/p>\n
     [ 3  8 13]<\/p>\n
     [ 4  9 14]<\/p>\n
     [ 5 10 15]<\/p>\n
     [ 5 10 15]]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4\u3001\u89e3\u91ca\u4ee3\u7801<\/h3>\n<\/p>\n
      \n
    • roll\u65b9\u6cd5<\/strong>\uff1anp.roll(data, -1, axis=0)<\/code>\u5c06\u6240\u6709\u884c\u7684\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u6700\u540e\u4e00\u884c\u4f1a\u5faa\u73af\u5230\u7b2c\u4e00\u884c\u3002<\/li>\n
    • \u624b\u52a8\u586b\u5145<\/strong>\uff1adata_shifted[-1] = data_shifted[-2]<\/code>\u7528\u5012\u6570\u7b2c\u4e8c\u884c\u7684\u6570\u636e\u586b\u5145\u6700\u540e\u4e00\u884c\uff0c\u786e\u4fdd\u6570\u636e\u5b8c\u6574\u3002<\/li>\n<\/ul>\n
      \n
      \u4e09\u3001\u5217\u8868\u89e3\u6790\u7684\u4f7f\u7528<\/h2>\n<\/p>\n
      \u9664\u4e86\u4f7f\u7528Pandas\u548cNumpy\u5e93\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u89e3\u6790\u6765\u5b9e\u73b0\u6570\u636e\u524d\u79fb\u3002\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u9ad8\u6548\u7684\u64cd\u4f5c\u65b9\u5f0f\uff0c\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
      1\u3001\u521b\u5efa\u5217\u8868<\/h3>\n<\/p>\n
      \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5b50\u5217\u8868\u7684\u5217\u8868\uff1a<\/p>\n<\/p>\n
      data = [<\/p>\n
          [1, 6, 11],<\/p>\n
          [2, 7, 12],<\/p>\n
          [3, 8, 13],<\/p>\n
          [4, 9, 14],<\/p>\n
          [5, 10, 15]<\/p>\n
      ]<\/p>\n
      print(data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
      [[1, 6, 11],<\/p>\n
       [2, 7, 12],<\/p>\n
       [3, 8, 13],<\/p>\n
       [4, 9, 14],<\/p>\n
       [5, 10, 15]]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2\u3001\u5b9e\u73b0\u6570\u636e\u524d\u79fb<\/h3>\n<\/p>\n
      \u4e3a\u4e86\u5c06\u5217\u8868\u4e2d\u7684\u6570\u636e\u524d\u79fb\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
      data_shifted = [data[i + 1] if i + 1 < len(data) else data[i] for i in range(len(data))]<\/p>\n
      print(data_shifted)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u5217\u8868\u89e3\u6790\u5c06\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u5e76\u5728\u6700\u540e\u4e00\u884c\u4fdd\u7559\u539f\u6570\u636e\u3002\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
      [[2, 7, 12],<\/p>\n
       [3, 8, 13],<\/p>\n
       [4, 9, 14],<\/p>\n
       [5, 10, 15],<\/p>\n
       [5, 10, 15]]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      3\u3001\u89e3\u91ca\u4ee3\u7801<\/h3>\n<\/p>\n
        \n
      • \u5217\u8868\u89e3\u6790<\/strong>\uff1a[data[i + 1] if i + 1 < len(data) else data[i] for i in range(len(data))]<\/code>\u5c06\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u6700\u540e\u4e00\u884c\u4fdd\u7559\u539f\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u5b8c\u6574\u3002<\/li>\n<\/ul>\n
        \n
        \u56db\u3001\u7efc\u5408\u6bd4\u8f83\u4e0e\u9009\u62e9<\/h2>\n<\/p>\n
        \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u60c5\u51b5\u3002\u4ee5\u4e0b\u662f\u4e09\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\u6bd4\u8f83\uff1a<\/p>\n<\/p>\n
        1\u3001Pandas\u5e93<\/h3>\n<\/p>\n
        \u4f18\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
          \n
        • \u529f\u80fd\u5f3a\u5927\uff0c\u652f\u6301\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/li>\n
        • \u4ee3\u7801\u7b80\u6d01\uff0c\u6613\u4e8e\u7ef4\u62a4\u3002<\/li>\n
        • \u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/li>\n<\/ul>\n
          \u7f3a\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
            \n
          • \u9700\u8981\u5b89\u88c5\u989d\u5916\u7684\u5e93\u3002<\/li>\n
          • \u5bf9\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u5904\u7406\u53ef\u80fd\u6709\u4e9b\u8fc7\u4e8e\u590d\u6742\u3002<\/li>\n<\/ul>\n
            2\u3001Numpy\u5e93<\/h3>\n<\/p>\n
            \u4f18\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
              \n
            • \u9ad8\u6548\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/li>\n
            • \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u65b9\u6cd5\u3002<\/li>\n<\/ul>\n
              \u7f3a\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
                \n
              • \u9700\u8981\u5b89\u88c5\u989d\u5916\u7684\u5e93\u3002<\/li>\n
              • \u64cd\u4f5c\u8f83\u4e3a\u5e95\u5c42\uff0c\u4ee3\u7801\u53ef\u8bfb\u6027\u4e0d\u5982Pandas\u3002<\/li>\n<\/ul>\n
                3\u3001\u5217\u8868\u89e3\u6790<\/h3>\n<\/p>\n
                \u4f18\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
                  \n
                • \u4e0d\u9700\u8981\u5b89\u88c5\u989d\u5916\u7684\u5e93\u3002<\/li>\n
                • \u4ee3\u7801\u7b80\u6d01\uff0c\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/li>\n<\/ul>\n
                  \u7f3a\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
                    \n
                  • \u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u6548\u7387\u8f83\u4f4e\u3002<\/li>\n
                  • \u529f\u80fd\u6709\u9650\uff0c\u4e0d\u9002\u7528\u4e8e\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/li>\n<\/ul>\n
                    4\u3001\u9009\u62e9\u5efa\u8bae<\/h3>\n<\/p>\n
                      \n
                    • \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406<\/strong>\uff0c\u63a8\u8350\u4f7f\u7528Pandas\u6216Numpy\u5e93\uff0c\u56e0\u4e3a\u5b83\u4eec\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u64cd\u4f5c\u65b9\u6cd5\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u3002<\/li>\n
                    • \u5bf9\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u5904\u7406<\/strong>\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u5217\u8868\u89e3\u6790\uff0c\u4ee3\u7801\u7b80\u6d01\u4e14\u4e0d\u9700\u8981\u5b89\u88c5\u989d\u5916\u7684\u5e93\u3002<\/li>\n
                    • \u5bf9\u4e8e\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c<\/strong>\uff0c\u63a8\u8350\u4f7f\u7528Pandas\u5e93\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u529f\u80fd\u548c\u66f4\u6613\u4e8e\u7ef4\u62a4\u7684\u4ee3\u7801\u3002<\/li>\n<\/ul>\n
                      \n
                      \u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
                      \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Pandas\u3001Numpy\u5e93\u4ee5\u53ca\u5217\u8868\u89e3\u6790\u6765\u5b9e\u73b0\u8868\u4e2d\u6570\u636e\u524d\u79fb\u7684\u64cd\u4f5c\u3002Pandas\u5e93\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\u4e4b\u4e00<\/strong>\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u548c\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\uff1bNumpy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u65b9\u6cd5<\/strong>\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\uff1b\u5217\u8868\u89e3\u6790\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u5904\u7406<\/strong>\uff0c\u4ee3\u7801\u7b80\u6d01\u6613\u8bfb\u3002<\/p>\n<\/p>\n
                      \u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u79fb\u52a8Pandas DataFrame\u4e2d\u7684\u6570\u636e\uff1f<\/strong>
                      \u5728\u4f7f\u7528Pandas\u5e93\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\u6570\u636e\u524d\u79fb\u3002\u4f8b\u5982\uff0c\u4f7f\u7528shift()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u6570\u636e\u5411\u4e0a\u6216\u5411\u4e0b\u79fb\u52a8\u3002\u5177\u4f53\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n
                      import pandas as pd\n\n# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame\ndata = {'A': [1, 2, 3, 4, 5]}\ndf = pd.DataFrame(data)\n\n# \u5411\u4e0a\u79fb\u52a8\u6570\u636e\ndf['A'] = df['A'].shift(-1)\nprint(df)\n<\/code><\/pre>\n
                      \u6b64\u4ee3\u7801\u4f1a\u5c06\u5217A\u4e2d\u7684\u6240\u6709\u6570\u636e\u5411\u4e0a\u79fb\u52a8\u4e00\u884c\uff0c\u6700\u540e\u4e00\u884c\u5219\u4f1a\u586b\u5145\u4e3aNaN\u3002<\/p>\n
                      \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528NumPy\u5b9e\u73b0\u6570\u7ec4\u6570\u636e\u524d\u79fb\uff1f<\/strong>
                      NumPy\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u7684\u524d\u79fb\u3002\u4f7f\u7528\u5207\u7247\u65b9\u6cd5\uff0c\u53ef\u4ee5\u76f4\u63a5\u5bf9\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\uff1a<\/p>\n
                      import numpy as np\n\n# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bNumPy\u6570\u7ec4\narr = np.array([1, 2, 3, 4, 5])\n\n# \u524d\u79fb\u6570\u636e\narr[:-1] = arr[1:]\narr[-1] = np.nan  # \u6216\u8005\u53ef\u4ee5\u9009\u62e9\u586b\u5145\u5176\u4ed6\u503c\nprint(arr)\n<\/code><\/pre>\n
                      \u8fd9\u79cd\u65b9\u6cd5\u5c06\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u5411\u524d\u79fb\u52a8\u4e00\u4f4d\uff0c\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u88ab\u8bbe\u7f6e\u4e3aNaN\u6216\u5176\u4ed6\u503c\u3002<\/p>\n
                      \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u8868\u683c\u6570\u636e\u7684\u524d\u79fb\u6548\u679c\uff1f<\/strong>
                      \u5982\u679c\u60f3\u8981\u66f4\u590d\u6742\u7684\u6570\u636e\u524d\u79fb\u6548\u679c\uff0c\u53ef\u4ee5\u5229\u7528DataFrame\u7684iloc<\/code>\u548c\u7d22\u5f15\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u7ed3\u5408assign()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u521b\u5efa\u65b0\u7684\u5217\u6765\u5c55\u793a\u524d\u79fb\u540e\u7684\u7ed3\u679c\uff1a<\/p>\n
                      import pandas as pd\n\ndata = {'A': [1, 2, 3, 4, 5]}\ndf = pd.DataFrame(data)\n\n# \u521b\u5efa\u4e00\u4e2a\u65b0\u5217\u663e\u793a\u524d\u79fb\u6570\u636e\ndf['A_shifted'] = df['A'].shift(-1)\nprint(df)\n<\/code><\/pre>\n
                      \u8fd9\u6837\uff0c\u60a8\u4e0d\u4ec5\u53ef\u4ee5\u67e5\u770b\u539f\u59cb\u6570\u636e\uff0c\u8fd8\u53ef\u4ee5\u540c\u65f6\u67e5\u770b\u524d\u79fb\u540e\u7684\u6570\u636e\uff0c\u4fbf\u4e8e\u5206\u6790\u548c\u5bf9\u6bd4\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001\u4f7f\u7528Python\u8ba9\u8868\u4e2d\u6570\u636e\u524d\u79fb\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528Numpy\u5e93\u8fdb\u884c\u6570\u7ec4 […]","protected":false},"author":3,"featured_media":1086148,"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\/1086141"}],"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=1086141"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1086141\/revisions"}],"predecessor-version":[{"id":1086150,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1086141\/revisions\/1086150"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1086148"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1086141"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1086141"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1086141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}