Pandas：如何在多个分隔符上拆分？

Question

我有一个数据框架，它在单个列（coordinates）中包含纬度、经度和海拔，我想将coordinates列拆分为三列（纬度、经度和海拔）

df:

ID                                                         Coordinates                                                      Region  
1     latitude_degrees: 52.00755721100514\nlongitude_degrees: 12.565129548994266\naltitude_meters: 185.23616827199143\n     Europe   
2     latitude_degrees: 52.00755721100514\nlongitude_degrees: 12.565129548994266\naltitude_meters: 185.23616827199143\n     Europe   
3     latitude_degrees: 52.00755721100514\nlongitude_degrees: 12.565129548994266\naltitude_meters: 185.23616827199143\n     Europe   
4     latitude_degrees: 52.00755721100514\nlongitude_degrees: 12.565129548994266\naltitude_meters: 185.23616827199143\n     Europe   
5     latitude_degrees: 52.00755721100514\nlongitude_degrees: 12.565129548994266\naltitude_meters: 185.23616827199143\n     Europe  

预期产出：

ID           lat                lon                     alt             Region  
1      52.00755721100514  12.565129548994266     185.23616827199143     Europe   
2      52.00755721100514  12.565129548994266     185.23616827199143     Europe   
3      52.00755721100514  12.565129548994266     185.23616827199143     Europe   
4      52.00755721100514  12.565129548994266     185.23616827199143     Europe   
5      52.00755721100514  12.565129548994266     185.23616827199143     Europe 

我尝试的是：

我试图首先在:的基础上拆分列，但它不起作用：

df.loc[df['Coordinates'].isin(["latitude_degrees", "longitude_degrees"])]= ""

我还试图替换文本，但它不起作用：

df.Coordinates.replace(to_replace=['latitude_degrees','longitude_degrees'],value='')

Answer 1

让我们使用^{}从Coordinates列中提取lat、long和alt，然后unstack对其进行重塑，最后join使用ID和Region列对其进行重构：

c = df['Coordinates'].str.extractall(r'([\d.]+)')[0].unstack()
d = df[['ID', 'Region']].join(c.set_axis(['lat', 'long', 'alt'], 1))

---

   ID  Region                lat                long                 alt
0   1  Europe  52.00755721100514  12.565129548994266  185.23616827199143
1   2  Europe  52.00755721100514  12.565129548994266  185.23616827199143
2   3  Europe  52.00755721100514  12.565129548994266  185.23616827199143
3   4  Europe  52.00755721100514  12.565129548994266  185.23616827199143
4   5  Europe  52.00755721100514  12.565129548994266  185.23616827199143