Pandas Dataframe：すべての列に平均列と標準列を追加する

Question

データフレームのすべての列に平均列と標準列を追加します。残念ながら、私のコードは元の列を平均と標準の列に置き換えます。

np.random.seed(50) 
df = pd.DataFrame(np.random.randint(0,9,size=(30, 3)), columns=list('ABC')) 
print df 

DF

A B C 
0 0 0 1 
1 4 6 5 
2 6 6 5 
3 2 7 4 
4 3 6 4 
5 1 5 0 
6 6 3 2 
7 3 3 3 
8 2 0 3 
9 2 0 3 
10 0 0 7 
11 3 8 7 
12 4 4 0 
13 0 3 3 
14 1 4 5 
15 7 0 3 
16 5 6 1 
17 4 4 4 
18 5 4 6 
19 3 0 5 
20 8 3 6 
21 2 8 8 
22 5 4 7 
23 8 4 4 
24 2 1 8 
25 7 1 5 
26 8 3 3 
27 5 3 6 
28 8 6 0 
29 8 2 1 

ここから私のコードです：

https://pandas.pydata.org/pandas-docs/stable/computation.html

r = df.rolling(window=5) 
print 'Agg mean and sdt df' 
print r['A', 'B', 'C'].agg([np.mean, np.std]) 
print 

出力

は、私はこれらの列「を追加する」の解決策を見つけることができません

A A_mean A_std B B_mean B_std C C_mean C_std 

：

AGGがあることを意味し、SDTのDF

 A    B    C   
    mean  std mean  std mean  std 
0 NaN  NaN NaN  NaN NaN  NaN 
1 NaN  NaN NaN  NaN NaN  NaN 
2 NaN  NaN NaN  NaN NaN  NaN 
3 NaN  NaN NaN  NaN NaN  NaN 
4 3.0 2.236068 5.0 2.828427 3.8 1.643168 
5 3.2 1.923538 6.0 0.707107 3.6 2.073644 
6 3.6 2.302173 5.4 1.516575 3.0 2.000000 
7 3.0 1.870829 4.8 1.788854 2.6 1.673320 
8 3.0 1.870829 3.4 2.302173 2.4 1.516575 
9 2.8 1.923538 2.2 2.167948 2.2 1.303840 
10 2.6 2.190890 1.2 1.643168 3.6 1.949359 
11 2.0 1.224745 2.2 3.492850 4.6 2.190890 
12 2.2 1.483240 2.4 3.577709 4.0 3.000000 
13 1.8 1.788854 3.0 3.316625 4.0 3.000000 
14 1.6 1.816590 3.8 2.863564 4.4 2.966479 
15 3.0 2.738613 3.8 2.863564 3.6 2.607681 
16 3.4 2.880972 3.4 2.190890 2.4 1.949359 
17 3.4 2.880972 3.4 2.190890 3.2 1.483240 
18 4.4 2.190890 3.6 2.190890 3.8 1.923538 
19 4.8 1.483240 2.8 2.683282 3.8 1.923538 
20 5.0 1.870829 3.4 2.190890 4.4 2.073644 
21 4.4 2.302173 3.8 2.863564 5.8 1.483240 
22 4.6 2.302173 3.8 2.863564 6.4 1.140175 
23 5.2 2.774887 3.8 2.863564 6.0 1.581139 
24 5.0 3.000000 4.0 2.549510 6.6 1.673320 
25 4.8 2.774887 3.6 2.880972 6.4 1.816590 
26 6.0 2.549510 2.6 1.516575 5.4 2.073644 
27 6.0 2.549510 2.4 1.341641 5.2 1.923538 
28 6.0 2.549510 2.8 2.049390 4.4 3.049590 
29 7.2 1.303840 3.0 1.870829 3.0 2.549510 

をそして私が探していますと、列（データ）です。

アドバイスをいただきありがとうございます。

Answer 1

In [18]: res = df.rolling(5).agg(['mean','std']) 

In [19]: res.columns = res.columns.map('_'.join) 

In [54]: cols = np.concatenate(list(zip(df.columns, res.columns[0::2], res.columns[1::2]))) 

In [55]: cols 
Out[55]: 
array(['A', 'A_mean', 'A_std', 'B', 'B_mean', 'B_std', 'C', 'C_mean', 'C_std'], 
     dtype='<U6') 

In [56]: res.join(df).loc[:, cols] 
Out[56]: 
    A A_mean  A_std B B_mean  B_std C C_mean  C_std 
0 0  NaN  NaN 0  NaN  NaN 1  NaN  NaN 
1 4  NaN  NaN 6  NaN  NaN 5  NaN  NaN 
2 6  NaN  NaN 6  NaN  NaN 5  NaN  NaN 
3 2  NaN  NaN 7  NaN  NaN 4  NaN  NaN 
4 3  3.0 2.236068 6  5.0 2.828427 4  3.8 1.643168 
5 1  3.2 1.923538 5  6.0 0.707107 0  3.6 2.073644 
6 6  3.6 2.302173 3  5.4 1.516575 2  3.0 2.000000 
7 3  3.0 1.870829 3  4.8 1.788854 3  2.6 1.673320 
8 2  3.0 1.870829 0  3.4 2.302173 3  2.4 1.516575 
9 2  2.8 1.923538 0  2.2 2.167948 3  2.2 1.303840 
10 0  2.6 2.190890 0  1.2 1.643168 7  3.6 1.949359 
11 3  2.0 1.224745 8  2.2 3.492850 7  4.6 2.190890 
12 4  2.2 1.483240 4  2.4 3.577709 0  4.0 3.000000 
13 0  1.8 1.788854 3  3.0 3.316625 3  4.0 3.000000 
14 1  1.6 1.816590 4  3.8 2.863564 5  4.4 2.966479 
15 7  3.0 2.738613 0  3.8 2.863564 3  3.6 2.607681 
16 5  3.4 2.880972 6  3.4 2.190890 1  2.4 1.949359 
17 4  3.4 2.880972 4  3.4 2.190890 4  3.2 1.483240 
18 5  4.4 2.190890 4  3.6 2.190890 6  3.8 1.923538 
19 3  4.8 1.483240 0  2.8 2.683282 5  3.8 1.923538 
20 8  5.0 1.870829 3  3.4 2.190890 6  4.4 2.073644 
21 2  4.4 2.302173 8  3.8 2.863564 8  5.8 1.483240 
22 5  4.6 2.302173 4  3.8 2.863564 7  6.4 1.140175 
23 8  5.2 2.774887 4  3.8 2.863564 4  6.0 1.581139 
24 2  5.0 3.000000 1  4.0 2.549510 8  6.6 1.673320 
25 7  4.8 2.774887 1  3.6 2.880972 5  6.4 1.816590 
26 8  6.0 2.549510 3  2.6 1.516575 3  5.4 2.073644 
27 5  6.0 2.549510 3  2.4 1.341641 6  5.2 1.923538 
28 8  6.0 2.549510 6  2.8 2.049390 0  4.4 3.049590 
29 8  7.2 1.303840 2  3.0 1.870829 1  3.0 2.549510