How to find mean (average) of each 3 values in one column of unknown size using pandas. No Numpy - pandas

Question

I am trying to figure out how to apply such functions (mean. STD etc) on different values of a CSV file. To keep it simple I put here the example of one column.
S08
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
D1 = D.loc[:,'S08']
rang = len(D1)
for i in range(rang):
x = D1.iloc[:,i+2]
m = x.mean()
print(m)
time S08 S09 S15 S37 S38 S39 S41 S45 S49
1 10 5 100 5 145 1500 1 10 99
2 20 15 200 15 135 1400 2 150 99
3 30 25 300 25 125 1300 3 140 99
4 40 35 400 35 115 1200 4 130 99
5 50 45 500 45 105 1100 5 120 99
6 60 55 600 55 95 1000 6 110 99
7 70 65 700 65 85 900 7 100 99
8 80 75 800 75 75 800 8 90 99
9 90 85 900 85 65 700 9 80 99
10 100 95 1000 95 55 600 10 70 99
11 110 105 1100 105 45 500 11 60 99
12 120 115 1200 115 35 400 12 50 99
13 130 125 1300 125 25 300 13 40 99
14 140 135 1400 135 15 200 14 30 99
15 150 145 1500 145 5 100 15 20 99
enter image description here

Answer 1

Use groupby by index floor divided by 3 and then aggregate columns by agg:
#create monotonic unique index (0,1,2...) if necessary
#df = df.reset_index(drop=True)
df = df.groupby(df.index // 3).agg({'col1':'mean', 'col2':'std'})
Sample:
np.random.seed(100)
df = pd.DataFrame(np.random.randint(5, size=(10,3)), columns=list('ABC'))
print (df)
A B C
0 0 0 3
1 0 2 4
2 2 2 2
3 2 1 0
4 0 4 3
5 4 2 0
6 3 1 2
7 3 4 4
8 1 3 4
9 4 3 3
df1 = df.groupby(df.index // 3).agg({'A':'mean', 'B':'std'})
print (df1)
A B
0 0.666667 1.154701
1 2.000000 1.527525
2 2.333333 1.527525
3 4.000000 NaN
#floor dicide index values for create triple groups
print (df.index // 3)
Int64Index([0, 0, 0, 1, 1, 1, 2, 2, 2, 3], dtype='int64')
EDIT:
df1 = df.groupby(df.index // 3).agg(['mean','std'])
df1.columns = df1.columns.map('_'.join)
print (df1)
A_mean A_std B_mean B_std C_mean C_std
0 0.666667 1.154701 1.333333 1.154701 3.000000 1.000000
1 2.000000 2.000000 2.333333 1.527525 1.000000 1.732051
2 2.333333 1.154701 2.666667 1.527525 3.333333 1.154701
3 4.000000 NaN 3.000000 NaN 3.000000 NaN