Q:

Pandas conditional creation of a dataframe column: based on multiple conditions max

I have a df:
  dog1  dog2  cat1  cat2  ant1  ant2
0    1     2     3     4     5     6
1    1     2     3     4     0     0
2    3     3     3     3     3     3
3    4     3     2     1     1     0

I want to add a new column based on the following conditions:
 if   max(dog1, dog2) > max(cat1, cat2) > max(ant1, ant2) ----->   2
 elif max(dog1, dog2) > max(cat1, cat2)                   ----->   1
 elif max(dog1, dog2) < max(cat1, cat2) < max(ant1, ant2) ----->  -2
 elif max(dog1, dog2) < max(cat1, cat2)                   ----->  -1
 else                                                     ----->   0

So it should become this:
  dog1  dog2  cat1  cat2  ant1  ant2   new
0    1     2     3     4     5     6    -2
1    1     2     3     4     0     0    -1
2    3     3     3     3     3     3     0
3    4     3     2     1     1     0     2     

I know how to do it with straightforward condition, but not this kind with max.  What's the best way to do it? 

A:

You can use .max(axis=1) function in pandas for it:
conditions = [
       (df[['dog1','dog2']].max(axis=1) > df[['cat1','cat2']].max(axis=1)) & (df[['cat1','cat2']].max(axis=1) > df[['ant1','ant2']].max(axis=1)), 
       (df[['dog1','dog2']].max(axis=1) > df[['cat1','cat2']].max(axis=1)),
       (df[['dog1','dog2']].max(axis=1) < df[['cat1','cat2']].max(axis=1)) & (df[['cat1','cat2']].max(axis=1) < df[['ant1','ant2']].max(axis=1)), 
       (df[['dog1','dog2']].max(axis=1) < df[['cat1','cat2']].max(axis=1))]
choices = [2,1,-2,-1]
df['new'] = np.select(conditions, choices, default=0)

output:
   dog1  dog2  cat1  cat2  ant1  ant2  new
0     1     2     3     4     5     6   -2
1     1     2     3     4     0     0   -1
2     3     3     3     3     3     3    0
3     4     3     2     1     1     0    2

