我的数据如下所示:
Gender topic: Big data infrastructure
0 F NaN
1 M -1
2 M -1
3 M -1
4 F 1
5 M NaN
6 M NaN
7 M NaN
8 M -2
9 M 1
10 F 1
11 M NaN
12 M 1
13 M -1
14 M 1
15 M NaN
16 M NaN
17 M NaN
18 M -1
19 M -2
20 F 1
21 M NaN
22 M NaN
23 F 2
24 M -2
25 F 2
26 M NaN
27 M 2
28 M 1
29 M NaN
30 M 2
31 M NaN
32 M NaN
33 F 2
34 M 2
我想以某种方式计算出有多少男性和女性得分为-2,-1,0,1,2或没有回答,但我无法弄明白。我尝试了几种groupby方法,但它们不起作用。有没有人有一些指导或提示?
答案 0 :(得分:2)
执行groupby
并使用value_counts
:
df.groupby('Gender')['topic: Big data infrastructure'].value_counts(dropna=False)
结果输出:
Gender topic: Big data infrastructure
F 1.0 3
2.0 3
NaN 1
M NaN 13
-1.0 5
1.0 4
-2.0 3
2.0 3