Pandas add a new column based on conditional logic of many other columns

Question

I have a pandas dataframe like this:

aa bb cc dd ee
a  a  b  b  foo
a  b  a  a  foo
b  a  a  a  bar
b  b  b  b  bar

I want to add a new column if value in columns 1 to 4 is a

The results would be like this:

aa bb cc dd ee  ff
a  a  b  b  foo a
a  b  a  a  foo a
b  a  a  a  bar a
b  b  b  b  bar b

The logic is: if value in any of columns 1 to 4 is a then column ff is a else it's b

I can define a function and do each column manually like:

def some_function(row);
   if row['aa']=='a' or row['bb']=='a' or row['cc']=='a' or row[dd]=='a':
       return 'a'
   return 'b'

But I'm looking for a solution that can scale across n number of columns.

Appreciate any help!

Answer 1

Use numpy.where with condition created by eq (==) with any for check at least one True per row:

cols = ['aa','bb','cc', 'dd']
df['ff'] = np.where(df[cols].eq('a').any(1), 'a', 'b')
print (df)
  aa bb cc dd   ee ff
0  a  a  b  b  foo  a
1  a  b  a  a  foo  a
2  b  a  a  a  bar  a
3  b  b  b  b  bar  b

Detail:

print (df[cols].eq('a'))
      aa     bb     cc
0   True   True  False
1   True  False   True
2  False   True   True
3  False  False  False

print (df[cols].eq('a').any(1))
0     True
1     True
2     True
3    False
dtype: bool

If need custom function:

def some_function(row):
   if row[cols].eq('a').any():
       return 'a'
   return 'b'

df['ff'] = df.apply(some_function, 1)
print (df)
  aa bb cc dd   ee ff
0  a  a  b  b  foo  a
1  a  b  a  a  foo  a
2  b  a  a  a  bar  a
3  b  b  b  b  bar  b