使用groupby将功能应用于熊猫数据框（“索引过多”错误）

Question

I am trying to compute mean and var along axis=1 of dataframe using only first k columns (compute as .iloc[:,:-5] ),naively, I would run as:

df.groupby('id').agg([lambda x: x.iloc[:,:-5].mean(axis=1), lambda x: x.iloc[:,:-5].var(axis=1)])

but it throws the 'too many indexers' error.

EDIT

Some data:

       0    1    2    3    4    5    6    7    8    9             Q1      Q2      Q3     Q4              id
0    3.0  3.0  4.0  4.0  3.0  3.0  3.0  3.0  3.0  3.0           12.0    0.83    80.0  1.000            11.0
1    3.0  3.0  4.0  4.0  4.0  3.0  3.0  3.0  3.0  3.0           14.0    1.60    80.0  1.000            11.0
2    3.0  3.0  3.0  3.0  3.0  3.0  3.0  3.0  3.0  5.0           13.0    1.40    75.0  1.000            11.0
3    3.0  3.0  4.0  4.0  4.0  3.0  3.0  3.0  3.0  3.0           12.0    0.50    80.0  0.848            11.0
4    3.0  4.0  4.0  4.0  7.0  7.0  5.0  4.0  4.0  2.0           13.0    1.74    70.0  0.883            11.0
13   3.0  3.0  2.0  2.0  2.0  2.0  3.0  2.0  3.0  3.0           12.0    3.67    45.0  1.000            14.0
14   2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0           13.0    3.67    48.0  0.848            14.0
15   2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0           12.0    1.67    70.0  0.848            14.0
16   2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  NaN  2.0           12.0    3.33    60.0  0.848            14.0
17   2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0           12.0    3.33    60.0  0.848            14.0
25   4.0  4.0  6.0  5.0  NaN  6.0  4.0  3.0  NaN  4.0           11.0    3.36    85.0  0.796            17.0
26   4.0  5.0  4.0  7.0  6.0  5.0  4.0  6.0  7.0  5.0            8.0    4.76    50.0  0.725            17.0
27   4.0  4.0  3.0  4.0  5.0  4.0  5.0  3.0  3.0  5.0            9.0    3.33    50.0  0.725            17.0
28   3.0  4.0  4.0  3.0  4.0  4.0  NaN  3.0  NaN  3.0           10.0    3.12    75.0  0.725            17.0
29   3.0  3.0  2.0  NaN  2.0  1.0  NaN  NaN  1.0  2.0           15.0    3.05    79.0  0.725            17.0
39   3.0  3.0  5.0  4.0  4.0  4.0  4.0  4.0  NaN  5.0           12.0    1.19    80.0  0.883            18.0
40   5.0  4.0  5.0  5.0  5.0  5.0  4.0  5.0  7.0  4.0            9.0    1.83    75.0  0.883            18.0
41   5.0  6.0  4.0  4.0  4.0  4.0  4.0  4.0  7.0  7.0           12.0    1.71    35.0  1.000            18.0
42   5.0  5.0  5.0  5.0  4.0  NaN  4.0  4.0  3.0  2.0           13.0    0.86    85.0  1.000            18.0
43   3.0  3.0  3.0  3.0  3.0  3.0  3.0  5.0  3.0  3.0           11.0    1.36    75.0  1.000            18.0
48   1

Answer 1

It seems you need first:

df['m'] = df.iloc[:,:-5].mean(axis=1)
df['v'] = df.iloc[:,:-5].var(axis=1)

and then aggregate if necesary.