Python 2.7 数据框：向每列添加平均值和标准列
我想在数据帧的每一列中添加一个mean和一个std列。不幸的是，我的代码将原始列替换为mean和std列Python 2.7 数据框：向每列添加平均值和标准列,python-2.7,pandas,Python 2.7,Pandas,我想在数据帧的每一列中添加一个mean和一个std列。不幸的是，我的代码将原始列替换为mean和std列 np.random.seed(50) df = pd.DataFrame(np.random.randint(0,9,size=(30, 3)), columns=list('ABC')) print df df 以下是我的代码： 输出 Agg平均值和sdt df A B C mean
np.random.seed(50)
df = pd.DataFrame(np.random.randint(0,9,size=(30, 3)), columns=list('ABC'))
print 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
在[18]：res=df.rolling（5）.agg（['mean'，'std'））
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
在[19]中：res.columns=res.columns.map（“”.join）
在[54]中：cols=np.concatenate（列表（zip（df.columns，res.columns[0:：2]，res.columns[1:：2]））
在[55]中：cols
出[55]：
数组（['A'，'A_-mean'，'A_-std'，'B'，'B_-mean'，'B_-std'，'C'，'C_-mean'，'C_-std']，
dtype='这太棒了！我不知道你可以列出agg的函数。我将检查映射操作以更好地理解它。非常感谢！！！@Diego，很高兴我能提供帮助：）当我将代码用于更大的数据集（2080 cols）时，出于某种原因，所需的列顺序发生了更改。所有列计算精细，但顺序不同（例如原始数据：A、B.C.A/B、A/C…）输出为A、A/B平均值、A/B标准、A/C平均值、A/C标准、A平均值、A标准）。我花了好几个小时试图弄明白到底发生了什么…有什么线索可以看吗？谢谢A后面的“/”是否会更改顺序？我测试了，它改变了顺序！！！将A/放在A之前！！！
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
A A_mean A_std B B_mean B_std C C_mean C_std
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