Name Sex Age Height Weight
0 Alfred M 14 69.0 112.5
1 Alice F 13 56.5 84.0
2 Barbara F 13 65.3 98.0
3 Carol F 14 62.8 102.5
4 Henry M 14 63.5 102.5
5 James M 12 57.3 83.0
6 Jane F 12 59.8 84.5
7 Janet F 15 62.5 112.5
8 Jeffrey M 13 62.5 84.0
9 John M 12 59.0 99.5
10 Joyce F 11 51.3 50.5
11 Judy F 14 64.3 90.0
12 Louise F 12 56.3 77.0
13 Mary F 15 66.5 112.0
14 Philip M 16 72.0 150.0
15 Robert M 12 64.8 128.0
16 Ronald M 15 67.0 133.0
17 Thomas M 11 57.5 85.0
18 William M 15 66.5 112.0
我想交替输出性别列行
Name Sex Age Height Weight
Alice F 13 56.5 84.0
Alfred M 14 69.0 112.5
Barbara F 13 65.3 98.0
Henry M 14 63.5 102.5
Carol F 14 62.8 102.5
James M 12 57.3 83.0
Jane F 12 59.8 84.5
Jeffrey M 13 62.5 84.0
Janet F 15 62.5 112.5
John M 12 59.0 99.5
Joyce F 11 51.3 50.5
Philip M 16 72.0 150.0
Judy F 14 64.3 90.0
Robert M 12 64.8 128.0
Louise F 12 56.3 77.0
Ronald M 15 67.0 133.0
Mary F 15 66.5 112.0
Thomas M 11 57.5 85.0
答案 0 :(得分：0)
您可以使用 groupby().cumcount() 枚举组内的行，然后使用 sort_values：
(df.assign(order=df.groupby(['Sex']).cumcount())
.sort_values(['order','Sex'])
.drop('order',axis=1)
)
输出：
Name Sex Age Height Weight
1 Alice F 13 56.5 84.0
0 Alfred M 14 69.0 112.5
2 Barbara F 13 65.3 98.0
4 Henry M 14 63.5 102.5
3 Carol F 14 62.8 102.5
5 James M 12 57.3 83.0
6 Jane F 12 59.8 84.5
8 Jeffrey M 13 62.5 84.0
7 Janet F 15 62.5 112.5
9 John M 12 59.0 99.5
10 Joyce F 11 51.3 50.5
14 Philip M 16 72.0 150.0
11 Judy F 14 64.3 90.0
15 Robert M 12 64.8 128.0
12 Louise F 12 56.3 77.0
16 Ronald M 15 67.0 133.0
13 Mary F 15 66.5 112.0
17 Thomas M 11 57.5 85.0
18 William M 15 66.5 112.0