python - 如何扩展数据框中的范围并复制值？

Question

我目前正在解析一个文件，并将其发送到一个数据框以打印到 excel。我想知道如何扩展数据框中的范围以创建更多行并复制原始行中的数据。

数据框看起来像这样

    Hour Value 1 Value 2 Value 3 Value 4 Value 5 
0   1-22   75.43   75.43   75.45   96.54  109.25        
1     23   20.82   20.82   20.84   41.93   54.64        
2     24   81.26   81.26   81.28  102.37  115.08        

理想情况下我希望它看起来像这样

     Hour  Value 1  Value 2  Value 3  Value 4  Value 5 
0     1    75.43    75.43    75.45    96.54    109.25
1     2    75.43    75.43    75.45    96.54    109.25
2     3    75.43    75.43    75.45    96.54    109.25 
...
4     22   75.43    75.43    75.45    96.54    109.25          
5     23   20.82    20.82    20.84    41.93     54.64        
6     24   81.26    81.26    81.28   102.37    115.08        

Answer 1

您可以使用 str.split 和 explode这里^[1]。

m = [
  np.arange(*map(int, el)).astype(str) if len(el) == 2 else el
  for el in df['Hour'].str.split('-')
]

df.assign(Hour=m).explode('Hour')

  Hour  Value 1  Value 2  Value 3  Value 4  Value 5
0    1    75.43    75.43    75.45    96.54   109.25
0    2    75.43    75.43    75.45    96.54   109.25
0    3    75.43    75.43    75.45    96.54   109.25
0    4    75.43    75.43    75.45    96.54   109.25
0    5    75.43    75.43    75.45    96.54   109.25
0    6    75.43    75.43    75.45    96.54   109.25
0    7    75.43    75.43    75.45    96.54   109.25
0    8    75.43    75.43    75.45    96.54   109.25
0    9    75.43    75.43    75.45    96.54   109.25
0   10    75.43    75.43    75.45    96.54   109.25
0   11    75.43    75.43    75.45    96.54   109.25
0   12    75.43    75.43    75.45    96.54   109.25
0   13    75.43    75.43    75.45    96.54   109.25
0   14    75.43    75.43    75.45    96.54   109.25
0   15    75.43    75.43    75.45    96.54   109.25
0   16    75.43    75.43    75.45    96.54   109.25
0   17    75.43    75.43    75.45    96.54   109.25
0   18    75.43    75.43    75.45    96.54   109.25
0   19    75.43    75.43    75.45    96.54   109.25
0   20    75.43    75.43    75.45    96.54   109.25
0   21    75.43    75.43    75.45    96.54   109.25
1   23    20.82    20.82    20.84    41.93    54.64
2   24    81.26    81.26    81.28   102.37   115.08

---

_{[1] 您需要 pandas >= 0.25.0 才能使用 pandas.Series.explode}