I have the following data

```
attr1_A    attr1_B    attr1_C    attr1_D    attr2_A    attr2_B   attr2_C
      1          0          0          1          1          0         0
      0          1          1          0          0          0         1
      0          0          0          0          0          1         0
      1          1          1          0          1          1         0

```

I want to retain `attr1_A`, `attr1_B` and combine `attr1_C` and `attr1_D` into `attr1_others`. As long as `attr1_C` and/or `attr1_D` is 1, then `attr1_others` will be 1. Similarly, I want to keep `attr2_A` but combine the remaining `attr2_*` into `attr2_others`. Like this:

```
attr1_A    attr1_B    attr1_others    attr2_A    attr2_others
      1          0          1               1               0     
      0          1          1               0               1  
      0          0          0               0               1 
      1          1          1               1               1 

```

In other words, for any group of `attr`, I want to retain a few known columns but combine the remaining (which I don't know how many remaining `attr` of the same group.

I am thinking of doing each group separately: processing all `attr1_*`, and then `attr2_*` because there are a limited number of groups in my dataset, but many attr under each group.

What I can think right now is to retrieve the `others` columns like:

```
# for group 1
df[x for x in df.columns if "A" not in x and "B" not in x and "attr1_" in x]

# for group 2
df[x for x in df.columns if "A" not in x and "attr2_" in x]

```

And to combine, I am thinking of using `any` function, but I can't come up with the syntax. Could you help?

**Updated attempt**:

I tried this

```
# for group 1
df['attr1_others'] = df[df[[x for x in list(df.columns) 
                            if "attr1_" in x
                            and "A" not in x 
                            and "B" not in x]].any(axis = 'column')]

```

but got the below error:

> 
> ValueError: No axis named column for object type `<`class 'pandas.core.frame.DataFrame'>
> 
> 
>