Python 如何使用数据表中另一列的范围展开数据表

我有下面的数据表

import pandas as pd
  dt = pd.DataFrame({'id_audience': ['Female 13-17', 'Female 18-20'],
                       'gender': ['female', 'female'],
                       'age_min': [13, 18],
                       'age_max': [17, 20]})

我想扩展这个数据框，增加一个列（

age

），并且

age

应该是

age\u min

和

age\u max

之间的范围

最终结果如下所示：

 dt = pd.DataFrame({'id_audience': ['Female 13-17', 'Female 13-17', 'Female 13-17', 'Female 13-17',
                                   'Female 13-17', 'Female 18-20', 'Female 18-20', 'Female 18-20', ],
                   'gender': ['female', 'female', 'female', 'female', 'female', 'female', 'female', 'female'],
                   'age_min': [13, 13, 13, 13, 18, 18, 18, 18],
                   'age_max': [17, 17, 17, 17, 20, 20, 20, 20],
                   'age': [13, 14, 15, 16, 17, 18, 19, 20]})

有什么想法吗？

使用和作为

年龄
栏的计数器：

dt = dt.loc[dt.index.repeat(dt['age_max'] - dt['age_min'] + 1)]
dt['age'] = dt['age_min'] + dt.groupby(level=0).cumcount()
dt = dt.reset_index(drop=True)
print (dt)
    id_audience  gender  age_min  age_max  age
0  Female 13-17  female       13       17   13
1  Female 13-17  female       13       17   14
2  Female 13-17  female       13       17   15
3  Female 13-17  female       13       17   16
4  Female 13-17  female       13       17   17
5  Female 18-20  female       18       20   18
6  Female 18-20  female       18       20   19
7  Female 18-20  female       18       20   20

这里有一种方法可以使用新的pandas 0.25.0
explode

s=dt['id_audience'].str.extractall('(\d+)')

dt['age']= [list(range(y.iloc[0,0],y.iloc[1,0]+1)) for x , y in s.astype(int).groupby(level=0)]
dt=dt.explode('age').reset_index(drop=True)

也可以像@Wen一样使用
explode
，但在最小/最大年龄列上直接访问范围



输出中的第4行不正确，您使用了第二组的范围，但第一组的值您是对的，我更正了它，谢谢您我认为它需要一个
+1
此处：
观众=观众.loc[audients.index.repeat（观众['e']-观众['age']+1）]
，以包括上一组bound@quant-谢谢，对不起，我很想念它。很好的用法：-）伙计
dt.assign(
  age=[np.arange(x, y+1) for x, y in zip(dt['age_min'], dt['age_max'])]
).explode('age').reset_index(drop=True)

    id_audience  gender  age_min  age_max age
0  Female 13-17  female       13       17  13
1  Female 13-17  female       13       17  14
2  Female 13-17  female       13       17  15
3  Female 13-17  female       13       17  16
4  Female 13-17  female       13       17  17
5  Female 18-20  female       18       20  18
6  Female 18-20  female       18       20  19
7  Female 18-20  female       18       20  20