So I have age column with different age ranging from 10-70 , I need to create new age groups column like 18-25, 25-40, 40-55 and 55+

Question

I have created a function like this.

def age_group(age):
    if age >= 18 & age <= 25:
        return '18-25'
    elif age > 25 & age <= 40:
        return '25-40'
    elif age > 40 & age <= 55:
        return '40-55 ' 
    else:
        return '55+'
age_group(20)

Then I have used the apply method like this survey['age_group']=survey['age'].apply(age_group)

But the age_group column returns '18-25' age group in all the columns even when the age is greater than 25 or less than 18

`age` is an `int` in this case, so I believe you wanted `and` and not `&`. — Henry Ecker, May 31 '21 at 15:22

score 1 · Answer 1 · answered May 31 '21 at 15:23

1

Use pd.cut:

survey['age_group'] = pd.cut(survey['age'],
                             bins=[18,25,40,55, 70],
                             labels=['18-25', '25-40', '40-55', '55+',
                             include_lowest=True)

answered May 31 '21 at 15:23

Quang Hoang

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score 0 · Accepted Answer · answered May 31 '21 at 15:22

All you need to do is using and instead of &:

def age_group(age):
    if age >= 18 and age <= 25:
        return '18-25'
    elif age > 25 and age <= 40:
        return '25-40'
    elif age > 40 and age <= 55:
        return '40-55' 
    else:
        return '55+'

or you can keep it simpler:

def age_group(age):
    if 18 <= age <= 25:
        return '18-25'
    if 25 < age <= 40:
        return '25-40'
    if 40 < age <= 55:
        return '40-55 ' 
    return '55+'

So I have age column with different age ranging from 10-70 , I need to create new age groups column like 18-25, 25-40, 40-55 and 55+

2 Answers2