How to calculate mean and std in Python Pandas?-CodePudding

I have CSV dataset like this:

"len","supp","dose"
4.2,"VC",0.5
11.5,"VC",0.5
7.3,"VC",0.5
5.8,"VC",0.5
6.4,"VC",0.5
10,"VC",0.5
11.2,"VC",0.5
11.2,"VC",0.5
5.2,"VC",0.5
7,"VC",0.5
16.5,"VC",1
16.5,"VC",1
15.2,"VC",1
17.3,"VC",1
22.5,"VC",1
17.3,"VC",1
13.6,"VC",1
14.5,"VC",1
18.8,"VC",1
15.5,"VC",1
23.6,"VC",2
18.5,"VC",2
33.9,"VC",2
25.5,"VC",2
26.4,"VC",2
32.5,"VC",2
26.7,"VC",2
21.5,"VC",2
23.3,"VC",2
29.5,"VC",2
15.2,"OJ",0.5
21.5,"OJ",0.5
17.6,"OJ",0.5
9.7,"OJ",0.5
14.5,"OJ",0.5
10,"OJ",0.5
8.2,"OJ",0.5
9.4,"OJ",0.5
16.5,"OJ",0.5
9.7,"OJ",0.5
19.7,"OJ",1
23.3,"OJ",1
23.6,"OJ",1
26.4,"OJ",1
20,"OJ",1
25.2,"OJ",1
25.8,"OJ",1
21.2,"OJ",1
14.5,"OJ",1
27.3,"OJ",1
25.5,"OJ",2
26.4,"OJ",2
22.4,"OJ",2
24.5,"OJ",2
24.8,"OJ",2
30.9,"OJ",2
26.4,"OJ",2
27.3,"OJ",2
29.4,"OJ",2
23,"OJ",2

I need to calculate mean and standart deviation by grouping "supp" and "dose" using Python Pandas modules. Result should look like this:

I tried using agg function but it seems that isn't really working

data = data.groupby(['supp', 'dose']).agg({['mean', 'std']})

Is it possible to calculate mean and std at once in Python Pandas modules?

CodePudding user response：

You can also rename the new column name in agg similar to in R:

df.groupby(['supp', 'dose'], as_index=False).agg(length_mean=('len', 'mean'),\
                                            length_std=('len', 'std'),\
                                            count=('len', 'count'))

Output:

  supp dose length_mean length_std  count
0   OJ  0.5 13.23       4.459709    10
1   OJ  1.0 22.70       3.910953    10
2   OJ  2.0 26.06       2.655058    10
3   VC  0.5 7.98        2.746634    10
4   VC  1.0 16.77       2.515309    10
5   VC  2.0 26.14       4.797731    10