CODEWITHSUNDEEP
pythonpandasnumpydataframemax
saga
saga

Reputation: 755

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?

Upvotes: 2

Views: 227

Answers (3)

Mohnish
Mohnish

Reputation: 1008

You can use apply Documentation 

def newrow(dog1,dog2,cat1,cat2,ant1,ant2):
    if max(dog1, dog2) > max(cat1, cat2) > max(ant1, ant2):
        return 2
    elif max(dog1, dog2) > max(cat1, cat2):
        return 1
    elif max(dog1, dog2) < max(cat1, cat2) < max(ant1, ant2):
        return -2
    elif max(dog1, dog2) < max(cat1, cat2):
        return -1
    return 0

df['new'] = df.apply(lambda x: newrow(*x), axis=1)

The new df will be 

  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

Upvotes: 1

ngo huy
ngo huy

Reputation: 26

It seems you looking for np.maximum(). Try to find it out at numpy maximum Hope it help.

Upvotes: 0

Ehsan
Ehsan

Reputation: 12407

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

Upvotes: 1

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