how to compare two columns in pandas to make a third column ?

Question

i have two columns age and sex in a pandas dataframe

sex = [\'m\', \'f\' , \'m\', \'f\', \'f\', \'f\', \'f\']
age = [16 ,  15 , 14 , 9  , 8   , 2   , 56 ]
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Answer 1

You could use pandas.DataFrame.where. For example

child.where(age<=9, sex)

Answer 2

df = pd.DataFrame({'sex':['m', 'f' , 'm', 'f', 'f', 'f', 'f'],
    'age':[16, 15, 14, 9, 8, 2, 56]})
df['yes'] = df.apply(lambda x: 'child' if x['age'] <= 9 else x['sex'], axis=1)

Result:

   age sex    yes
0   16   m      m
1   15   f      f
2   14   m      m
3    9   f  child
4    8   f  child
5    2   f  child
6   56   f      f

Answer 3

Use numpy.where:

df['col3'] = np.where(df['age'] <= 9, 'child', df['sex'])

The resulting output:

   age sex   col3
0   16   m      m
1   15   f      f
2   14   m      m
3    9   f  child
4    8   f  child
5    2   f  child
6   56   f      f

Timings

Using the following setup to get a larger sample DataFrame:

np.random.seed([3,1415])
n = 10**5
df = pd.DataFrame({'sex': np.random.choice(['m', 'f'], size=n), 'age': np.random.randint(0, 100, size=n)})

I get the following timings:

%timeit np.where(df['age'] <= 9, 'child', df['sex'])
1000 loops, best of 3: 1.26 ms per loop

%timeit df['sex'].where(df['age'] > 9, 'child')
100 loops, best of 3: 3.25 ms per loop

%timeit df.apply(lambda x: 'child' if x['age'] <= 9 else x['sex'], axis=1)
100 loops, best of 3: 3.92 ms per loop